From eeec85ae5bb7b57646cb878f4c4e59ac5fb66ccf Mon Sep 17 00:00:00 2001 From: Fatigue24 Date: Sun, 3 Nov 2024 02:29:14 +0200 Subject: [PATCH 01/49] Improving the baseline algorithm for QKP --- tig-challenges/src/knapsack.rs | 126 +++++++++++++++++++++++++++++---- 1 file changed, 111 insertions(+), 15 deletions(-) diff --git a/tig-challenges/src/knapsack.rs b/tig-challenges/src/knapsack.rs index e65e238..a354c7e 100644 --- a/tig-challenges/src/knapsack.rs +++ b/tig-challenges/src/knapsack.rs @@ -102,31 +102,127 @@ impl crate::ChallengeTrait for Challenge { // Precompute the ratio between the total value (value + sum of interactive values) and // weight for each item. Pair the ratio with the item's weight and index - let mut value_weight_ratios: Vec<(usize, f32, u32)> = (0..difficulty.num_items) + let mut item_values: Vec<(usize, f32)> = (0..difficulty.num_items) .map(|i| { let total_value = values[i] as i32 + interaction_values[i].iter().sum::(); - let weight = weights[i]; - let ratio = total_value as f32 / weight as f32; - (i, ratio, weight) + let ratio = total_value as f32 / weights[i] as f32; + (i, ratio) }) .collect(); - // Sort the list of tuples by value-to-weight ratio in descending order - value_weight_ratios.sort_unstable_by(|&(_, ratio_a, _), &(_, ratio_b, _)| { - ratio_b.partial_cmp(&ratio_a).unwrap() - }); + // Sort the list of ratios in descending order + item_values.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + // Step 1: Initial solution obtained by greedily selecting items based on value-weight ratio + let mut selected_items = Vec::with_capacity(difficulty.num_items); + let mut unselected_items = Vec::with_capacity(difficulty.num_items); let mut total_weight = 0; - let mut selected_indices = Vec::new(); - for &(i, _, weight) in &value_weight_ratios { - if total_weight + weight <= max_weight { - selected_indices.push(i); - total_weight += weight; + let mut total_value = 0; + + for &(item, _) in &item_values { + if total_weight + weights[item] <= max_weight { + total_weight += weights[item]; + total_value += values[item] as i32; + + for &prev_item in &selected_items { + total_value += interaction_values[item][prev_item]; + } + selected_items.push(item); + } else { + unselected_items.push(item); } } - selected_indices.sort_unstable(); + + // Step 2: Improvement of solution with Local Search and Tabu-List + // Precompute sum of interaction values with each selected item for all items(if i==j, then interaction_values[i][j] == 0) + let mut interaction_sum_list = vec![0; difficulty.num_items]; + for x in 0..difficulty.num_items { + interaction_sum_list[x] = values[x] as i32; + for &item in &selected_items { + interaction_sum_list[x] += interaction_values[x][item]; + } + } + + // Optimized local search with tabu list + let max_iterations = 100; + let mut tabu_list = vec![0; difficulty.num_items]; - let mut min_value = calculate_total_value(&selected_indices, &values, &interaction_values); + for _ in 0..max_iterations { + let mut best_improvement = 0; + let mut best_swap = None; + + for i in 0..unselected_items.len() { + let new_item = unselected_items[i]; + if tabu_list[new_item] > 0 { + continue; + } + + let new_item_values_sum = interaction_sum_list[new_item]; + // Greedy assumption that remove_item_values_sum + interaction_values[new_item][remove_item] is higher or equal to zero + // It's not only a huge optimization, but it also produces better solutions most of the time + if new_item_values_sum < best_improvement { + continue; + } + + // Compute minimal weight of remove_item required to put new_item + let min_weight = weights[new_item] as i32 - (max_weight as i32 - total_weight as i32); + for j in 0..selected_items.len() { + let remove_item = selected_items[j]; + if tabu_list[remove_item] > 0 { + continue; + } + + // Don't check the weight if there is enough remaining capacity + if min_weight < 0 + { + // Skip a remove_item if the remaining capacity after removal is insufficient to push a new_item + let removed_item_weight = weights[remove_item] as i32; + if removed_item_weight < min_weight { + continue; + } + } + + let remove_item_values_sum = interaction_sum_list[remove_item]; + let value_diff = new_item_values_sum - remove_item_values_sum - interaction_values[new_item][remove_item]; + + if value_diff > best_improvement { + best_improvement = value_diff; + best_swap = Some((i, j)); + } + } + } + + if let Some((unselected_index, selected_index)) = best_swap { + let new_item = unselected_items[unselected_index]; + let remove_item = selected_items[selected_index]; + + selected_items.swap_remove(selected_index); + unselected_items.swap_remove(unselected_index); + selected_items.push(new_item); + unselected_items.push(remove_item); + + total_value += best_improvement; + total_weight = total_weight + weights[new_item] - weights[remove_item]; + + // Update sum of interaction values after swapping items + for x in 0..difficulty.num_items { + interaction_sum_list[x] += interaction_values[x][new_item] - interaction_values[x][remove_item]; + } + + // Update tabu list + tabu_list[new_item] = 3; + tabu_list[remove_item] = 3; + } else { + break; // No improvement found, terminate local search + } + + // Decrease tabu counters + for t in tabu_list.iter_mut() { + *t = if *t > 0 { *t - 1 } else { 0 }; + } + } + + let mut min_value = calculate_total_value(&selected_items, &values, &interaction_values); min_value = (min_value as f32 * (1.0 + difficulty.better_than_baseline as f32 / 1000.0)) .round() as u32; From cd12b6f72d3c01af67cf01be666393fc6c764f82 Mon Sep 17 00:00:00 2001 From: Fatigue24 Date: Sun, 3 Nov 2024 02:58:48 +0200 Subject: [PATCH 02/49] fixed condition for skipping weight check --- tig-challenges/src/knapsack.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tig-challenges/src/knapsack.rs b/tig-challenges/src/knapsack.rs index a354c7e..d4e833a 100644 --- a/tig-challenges/src/knapsack.rs +++ b/tig-challenges/src/knapsack.rs @@ -173,7 +173,7 @@ impl crate::ChallengeTrait for Challenge { } // Don't check the weight if there is enough remaining capacity - if min_weight < 0 + if min_weight >= 0 { // Skip a remove_item if the remaining capacity after removal is insufficient to push a new_item let removed_item_weight = weights[remove_item] as i32; From fea23bf2219c50d6e1f705f028809e46e38b0599 Mon Sep 17 00:00:00 2001 From: Fatigue24 Date: Wed, 6 Nov 2024 18:23:50 +0200 Subject: [PATCH 03/49] Align knapsack instance generation with academic benchmarks --- tig-challenges/src/knapsack.rs | 84 +++++++++++++++++++++++----------- 1 file changed, 58 insertions(+), 26 deletions(-) diff --git a/tig-challenges/src/knapsack.rs b/tig-challenges/src/knapsack.rs index d4e833a..06a5af3 100644 --- a/tig-challenges/src/knapsack.rs +++ b/tig-challenges/src/knapsack.rs @@ -77,29 +77,48 @@ impl crate::ChallengeTrait for Challenge { fn generate_instance(seed: [u8; 32], difficulty: &Difficulty) -> Result { let mut rng = SmallRng::from_seed(StdRng::from_seed(seed).gen()); - + + // Set constant density for value generation + let density = 0.25; + // Generate weights w_i in the range [1, 50] let weights: Vec = (0..difficulty.num_items) .map(|_| rng.gen_range(1..=50)) .collect(); - // Generate values v_i in the range [50, 100] + + // Generate values v_i in the range [1, 100] with density probability, 0 otherwise let values: Vec = (0..difficulty.num_items) - .map(|_| rng.gen_range(50..=100)) + .map(|_| { + if rng.gen_bool(density) { + rng.gen_range(1..=100) + } else { + 0 + } + }) .collect(); - - // Generate interactive values V_ij in the range [1, 50], with V_ij == V_ji and V_ij where i==j is 0. - let mut interaction_values: Vec> = - vec![vec![0; difficulty.num_items]; difficulty.num_items]; + + // Generate interaction values V_ij with the following properties: + // - V_ij == V_ji (symmetric matrix) + // - V_ii == 0 (diagonal is zero) + // - Values are in range [1, 100] with density probability, 0 otherwise + let mut interaction_values: Vec> = vec![vec![0; difficulty.num_items]; difficulty.num_items]; + for i in 0..difficulty.num_items { for j in (i + 1)..difficulty.num_items { - let value = rng.gen_range(-50..=50); + let value = if rng.gen_bool(density) { + rng.gen_range(1..=100) + } else { + 0 + }; + + // Set both V_ij and V_ji due to symmetry interaction_values[i][j] = value; interaction_values[j][i] = value; } } - + let max_weight: u32 = weights.iter().sum::() / 2; - + // Precompute the ratio between the total value (value + sum of interactive values) and // weight for each item. Pair the ratio with the item's weight and index let mut item_values: Vec<(usize, f32)> = (0..difficulty.num_items) @@ -109,15 +128,16 @@ impl crate::ChallengeTrait for Challenge { (i, ratio) }) .collect(); - + // Sort the list of ratios in descending order item_values.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); - + // Step 1: Initial solution obtained by greedily selecting items based on value-weight ratio let mut selected_items = Vec::with_capacity(difficulty.num_items); let mut unselected_items = Vec::with_capacity(difficulty.num_items); let mut total_weight = 0; let mut total_value = 0; + let mut is_selected = vec![false; difficulty.num_items]; for &(item, _) in &item_values { if total_weight + weights[item] <= max_weight { @@ -128,13 +148,14 @@ impl crate::ChallengeTrait for Challenge { total_value += interaction_values[item][prev_item]; } selected_items.push(item); + is_selected[item] = true; } else { unselected_items.push(item); } } // Step 2: Improvement of solution with Local Search and Tabu-List - // Precompute sum of interaction values with each selected item for all items(if i==j, then interaction_values[i][j] == 0) + // Precompute sum of interaction values with each selected item for all items let mut interaction_sum_list = vec![0; difficulty.num_items]; for x in 0..difficulty.num_items { interaction_sum_list[x] = values[x] as i32; @@ -143,10 +164,17 @@ impl crate::ChallengeTrait for Challenge { } } + let mut min_selected_item_values = i32::MAX; + for x in 0..difficulty.num_items { + if is_selected[x] { + min_selected_item_values = min_selected_item_values.min(interaction_sum_list[x]); + } + } + // Optimized local search with tabu list let max_iterations = 100; let mut tabu_list = vec![0; difficulty.num_items]; - + for _ in 0..max_iterations { let mut best_improvement = 0; let mut best_swap = None; @@ -158,30 +186,27 @@ impl crate::ChallengeTrait for Challenge { } let new_item_values_sum = interaction_sum_list[new_item]; - // Greedy assumption that remove_item_values_sum + interaction_values[new_item][remove_item] is higher or equal to zero - // It's not only a huge optimization, but it also produces better solutions most of the time - if new_item_values_sum < best_improvement { + if new_item_values_sum < best_improvement + min_selected_item_values { continue; } - + // Compute minimal weight of remove_item required to put new_item - let min_weight = weights[new_item] as i32 - (max_weight as i32 - total_weight as i32); + let min_weight = weights[new_item] as i32 - (max_weight as i32 - total_weight as i32); for j in 0..selected_items.len() { let remove_item = selected_items[j]; if tabu_list[remove_item] > 0 { continue; } - + // Don't check the weight if there is enough remaining capacity - if min_weight >= 0 - { + if min_weight > 0 { // Skip a remove_item if the remaining capacity after removal is insufficient to push a new_item let removed_item_weight = weights[remove_item] as i32; if removed_item_weight < min_weight { continue; } } - + let remove_item_values_sum = interaction_sum_list[remove_item]; let value_diff = new_item_values_sum - remove_item_values_sum - interaction_values[new_item][remove_item]; @@ -201,12 +226,19 @@ impl crate::ChallengeTrait for Challenge { selected_items.push(new_item); unselected_items.push(remove_item); + is_selected[new_item] = true; + is_selected[remove_item] = false; + total_value += best_improvement; total_weight = total_weight + weights[new_item] - weights[remove_item]; // Update sum of interaction values after swapping items + min_selected_item_values = i32::MAX; for x in 0..difficulty.num_items { interaction_sum_list[x] += interaction_values[x][new_item] - interaction_values[x][remove_item]; + if is_selected[x] { + min_selected_item_values = min_selected_item_values.min(interaction_sum_list[x]); + } } // Update tabu list @@ -215,17 +247,17 @@ impl crate::ChallengeTrait for Challenge { } else { break; // No improvement found, terminate local search } - + // Decrease tabu counters for t in tabu_list.iter_mut() { *t = if *t > 0 { *t - 1 } else { 0 }; } } - + let mut min_value = calculate_total_value(&selected_items, &values, &interaction_values); min_value = (min_value as f32 * (1.0 + difficulty.better_than_baseline as f32 / 1000.0)) .round() as u32; - + Ok(Challenge { seed, difficulty: difficulty.clone(), From 74c31623b90850dc1352d6644eb1ce6c3e4a43cf Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Sat, 28 Dec 2024 22:43:30 +0800 Subject: [PATCH 04/49] Add jekyll config to ignore symbolic links. --- _config.yml | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 _config.yml diff --git a/_config.yml b/_config.yml new file mode 100644 index 0000000..48336a4 --- /dev/null +++ b/_config.yml @@ -0,0 +1,2 @@ +exclude: + - tig-benchmarker/ \ No newline at end of file From 236437717404edbf4b307a6767c7eac3383b40a0 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Sun, 29 Dec 2024 03:09:49 +0800 Subject: [PATCH 05/49] Add script to calc apy. --- tig-benchmarker/calc_apy.py | 112 ++++++++++++++++++ .../{calc_rewards.py => calc_reward_share.py} | 66 +---------- tig-benchmarker/common/calcs.py | 102 ++++++++++++++++ tig-benchmarker/common/structs.py | 5 +- 4 files changed, 221 insertions(+), 64 deletions(-) create mode 100644 tig-benchmarker/calc_apy.py rename tig-benchmarker/{calc_rewards.py => calc_reward_share.py} (58%) create mode 100644 tig-benchmarker/common/calcs.py diff --git a/tig-benchmarker/calc_apy.py b/tig-benchmarker/calc_apy.py new file mode 100644 index 0000000..a1cf1a8 --- /dev/null +++ b/tig-benchmarker/calc_apy.py @@ -0,0 +1,112 @@ +import numpy as np +import json +import requests +from common.structs import * +from common.calcs import * + +API_URL = "https://mainnet-api.tig.foundation" + +deposit = input("Enter deposit in TIG (leave blank to fetch deposit from your player_id): ") +if deposit != "": + lock_period = input("Enter number of weeks to lock (longer lock will have higher APY): ") + deposit = float(deposit) + lock_period = float(lock_period) + player_id = None +else: + player_id = input("Enter player_id: ").lower() + +print("Fetching data...") +block = Block.from_dict(requests.get(f"{API_URL}/get-block?include_data").json()["block"]) +opow_data = { + x["player_id"]: OPoW.from_dict(x) + for x in requests.get(f"{API_URL}/get-opow?block_id={block.id}").json()["opow"] +} + +factors = { + benchmarker: { + **{ + f: opow_data[benchmarker].block_data.num_qualifiers_by_challenge.get(f, 0) + for f in block.data.active_ids["challenge"] + }, + "weighted_deposit": opow_data[benchmarker].block_data.delegated_weighted_deposit.to_float() + } + for benchmarker in opow_data +} + +if player_id is None: + blocks_till_round_ends = block.config["rounds"]["blocks_per_round"] - (block.details.height % block.config["rounds"]["blocks_per_round"]) + seconds_till_round_ends = blocks_till_round_ends * block.config["rounds"]["seconds_between_blocks"] + weighted_deposit = calc_weighted_deposit(deposit, seconds_till_round_ends, lock_period * 604800) +else: + player_data = Player.from_dict(requests.get(f"{API_URL}/get-player-data?player_id={player_id}&block_id={block.id}").json()["player"]) + deposit = sum(x.to_float() for x in player_data.block_data.deposit_by_locked_period) + weighted_deposit = player_data.block_data.weighted_deposit.to_float() + for delegatee, fraction in player_data.block_data.delegatees.items(): + factors[delegatee]["weighted_deposit"] -= fraction * weighted_deposit + +total_factors = { + f: sum(factors[benchmarker][f] for benchmarker in opow_data) + for f in list(block.data.active_ids["challenge"]) + ["weighted_deposit"] +} +reward_shares = { + benchmarker: opow_data[benchmarker].block_data.reward_share + for benchmarker in opow_data +} + +print("Optimising delegation by splitting into 100 chunks...") +chunk = weighted_deposit / 100 +delegate = {} +for i in range(100): + print(f"Chunk {i + 1}: simulating delegation...") + total_factors["weighted_deposit"] += chunk + if len(delegate) == 10: + potential_delegatees = list(delegate) + else: + potential_delegatees = [benchmarker for benchmarker in opow_data if opow_data[benchmarker].block_data.self_deposit.to_float() >= 10000] + highest_apy_benchmarker = max( + potential_delegatees, + key=lambda delegatee: ( + calc_influence({ + benchmarker: { + f: (factors[benchmarker][f] + chunk * (benchmarker == delegatee and f == "weighted_deposit")) / total_factors[f] + for f in total_factors + } + for benchmarker in opow_data + }, block.config["opow"])[delegatee] * + reward_shares[delegatee] * chunk / (factors[delegatee]["weighted_deposit"] + chunk) + ) + ) + print(f"Chunk {i + 1}: best delegatee is {highest_apy_benchmarker}") + if highest_apy_benchmarker not in delegate: + delegate[highest_apy_benchmarker] = 0 + delegate[highest_apy_benchmarker] += 1 + factors[highest_apy_benchmarker]["weighted_deposit"] += chunk + +influences = calc_influence({ + benchmarker: { + f: factors[benchmarker][f] / total_factors[f] + for f in total_factors + } + for benchmarker in opow_data +}, block.config["opow"]) +print("") + + +print("Optimised delegation split:") +reward_pool = block.config["rewards"]["distribution"]["opow"] * next(x["block_reward"] for x in reversed(block.config["rewards"]["schedule"]) if x["round_start"] <= block.details.round) +deposit_chunk = deposit / 100 +total_reward = 0 +for delegatee, num_chunks in delegate.items(): + share_fraction = influences[delegatee] * reward_shares[delegatee] * (num_chunks * chunk) / factors[delegatee]["weighted_deposit"] + reward = share_fraction * reward_pool + total_reward += reward + apy = reward * block.config["rounds"]["blocks_per_round"] * 52 / (num_chunks * deposit_chunk) + print(f"{delegatee}: %delegated = {num_chunks}%, apy = {apy * 100:.2f}%") + +print(f"average_apy = {total_reward * 10080 * 52 / deposit * 100:.2f}% on your deposit of {deposit} TIG") + +print("") +print("To set this delegation split, run the following command:") +req = {"delegatees": {k: v / 100 for k, v in delegate.items()}} +print("API_KEY=") +print(f"curl -H \"X-Api-Key: $API_KEY\" -X POST -d '{json.dumps(req)}' {API_URL}/set-delegatees") \ No newline at end of file diff --git a/tig-benchmarker/calc_rewards.py b/tig-benchmarker/calc_reward_share.py similarity index 58% rename from tig-benchmarker/calc_rewards.py rename to tig-benchmarker/calc_reward_share.py index 1a28989..853053a 100644 --- a/tig-benchmarker/calc_rewards.py +++ b/tig-benchmarker/calc_reward_share.py @@ -1,68 +1,10 @@ import numpy as np -import json import requests -from typing import List, Dict from common.structs import * +from common.calcs import * from copy import deepcopy -def calc_influence(fractions, opow_config) -> Dict[str, float]: - benchmarkers = list(fractions) - factors = list(next(iter(fractions.values()))) - num_challenges = len(factors) - 1 - avg_qualifier_fractions = { - benchmarker: sum( - fractions[benchmarker][f] - for f in factors - if f != "weighted_deposit" - ) / num_challenges - for benchmarker in benchmarkers - } - deposit_fraction_cap = { - benchmarker: avg_qualifier_fractions[benchmarker] * opow_config["max_deposit_to_qualifier_ratio"] - for benchmarker in benchmarkers - } - capped_fractions = { - benchmarker: { - **fractions[benchmarker], - "weighted_deposit": min( - fractions[benchmarker]["weighted_deposit"], - deposit_fraction_cap[benchmarker] - ) - } - for benchmarker in benchmarkers - } - avg_fraction = { - benchmarker: np.mean(list(capped_fractions[benchmarker].values())) - for benchmarker in benchmarkers - } - var_fraction = { - benchmarker: np.var(list(capped_fractions[benchmarker].values())) - for benchmarker in benchmarkers - } - imbalance = { - benchmarker: (var_fraction[benchmarker] / np.square(avg_fraction[benchmarker]) / num_challenges) if avg_fraction[benchmarker] > 0 else 0 - for benchmarker in benchmarkers - } - imbalance_penalty = { - benchmarker: 1.0 - np.exp(-opow_config["imbalance_multiplier"] * imbalance[benchmarker]) - for benchmarker in benchmarkers - } - weighted_avg_fraction = { - benchmarker: ((avg_qualifier_fractions[benchmarker] * num_challenges) + capped_fractions[benchmarker]["weighted_deposit"] * opow_config["deposit_multiplier"]) / (num_challenges + opow_config["deposit_multiplier"]) - for benchmarker in benchmarkers - } - unormalised_influence = { - benchmarker: weighted_avg_fraction[benchmarker] * (1.0 - imbalance_penalty[benchmarker]) - for benchmarker in benchmarkers - } - total = sum(unormalised_influence.values()) - influence = { - benchmarker: unormalised_influence[benchmarker] / total - for benchmarker in benchmarkers - } - return influence - API_URL = "https://mainnet-api.tig.foundation" player_id = input("Enter player_id: ").lower() @@ -140,12 +82,12 @@ print(f"reward = {reward['only_self_deposit']:.4f} TIG per block") print("") print("Scenario 2 (current self + delegated deposit)") print(f"%weighted_deposit = {fractions[player_id]['weighted_deposit'] * 100:.2f}%") -print(f"reward = {reward['current']:.4f} TIG per block ({reward['current'] / reward['only_self_deposit'] * 100 - 100:.2f}% difference*)") +print(f"reward = {reward['current']:.4f} TIG per block (recommended max reward_share = {100 - reward['only_self_deposit'] / reward['current'] * 100:.2f}%*)") print("") print(f"Scenario 3 (self + delegated deposit at parity)") print(f"%weighted_deposit = average %qualifiers = {average_fraction_of_qualifiers * 100:.2f}%") -print(f"reward = {reward['at_parity']:.4f} TIG per block ({reward['at_parity'] / reward['only_self_deposit'] * 100 - 100:.2f}% difference*)") +print(f"reward = {reward['at_parity']:.4f} TIG per block (recommended max reward_share = {100 - reward['only_self_deposit'] / reward['at_parity'] * 100:.2f}%*)") print("") -print("*These are percentage differences in reward compared with relying only on self-deposit (Scenario 1).") +print("*Recommend not setting reward_share above the max. You will not benefit from delegation (earn the same as Scenario 1 with zero delegation).") print("") print("Note: the imbalance penalty is such that your reward increases at a high rate when moving up to parity") \ No newline at end of file diff --git a/tig-benchmarker/common/calcs.py b/tig-benchmarker/common/calcs.py new file mode 100644 index 0000000..3f47230 --- /dev/null +++ b/tig-benchmarker/common/calcs.py @@ -0,0 +1,102 @@ +import numpy as np +from typing import List, Dict + + +def calc_influence(fractions: Dict[str, Dict[str, float]], opow_config: dict) -> Dict[str, float]: + """ + Calculate the influence of each benchmarker based on their fractions and the OPoW configuration. + + Args: + fractions: A dictionary of dictionaries, mapping benchmarker_ids to their fraction of each factor (challenges & weighted_deposit). + opow_config: A dictionary containing configuration parameters for the calculation. + + Returns: + Dict[str, float]: A dictionary mapping each benchmarker_id to their calculated influence. + """ + benchmarkers = list(fractions) + factors = list(next(iter(fractions.values()))) + num_challenges = len(factors) - 1 + avg_qualifier_fractions = { + benchmarker: sum( + fractions[benchmarker][f] + for f in factors + if f != "weighted_deposit" + ) / num_challenges + for benchmarker in benchmarkers + } + deposit_fraction_cap = { + benchmarker: avg_qualifier_fractions[benchmarker] * opow_config["max_deposit_to_qualifier_ratio"] + for benchmarker in benchmarkers + } + capped_fractions = { + benchmarker: { + **fractions[benchmarker], + "weighted_deposit": min( + fractions[benchmarker]["weighted_deposit"], + deposit_fraction_cap[benchmarker] + ) + } + for benchmarker in benchmarkers + } + avg_fraction = { + benchmarker: np.mean(list(capped_fractions[benchmarker].values())) + for benchmarker in benchmarkers + } + var_fraction = { + benchmarker: np.var(list(capped_fractions[benchmarker].values())) + for benchmarker in benchmarkers + } + imbalance = { + benchmarker: (var_fraction[benchmarker] / np.square(avg_fraction[benchmarker]) / num_challenges) if avg_fraction[benchmarker] > 0 else 0 + for benchmarker in benchmarkers + } + imbalance_penalty = { + benchmarker: 1.0 - np.exp(-opow_config["imbalance_multiplier"] * imbalance[benchmarker]) + for benchmarker in benchmarkers + } + weighted_avg_fraction = { + benchmarker: ((avg_qualifier_fractions[benchmarker] * num_challenges) + capped_fractions[benchmarker]["weighted_deposit"] * opow_config["deposit_multiplier"]) / (num_challenges + opow_config["deposit_multiplier"]) + for benchmarker in benchmarkers + } + unormalised_influence = { + benchmarker: weighted_avg_fraction[benchmarker] * (1.0 - imbalance_penalty[benchmarker]) + for benchmarker in benchmarkers + } + total = sum(unormalised_influence.values()) + influence = { + benchmarker: unormalised_influence[benchmarker] / total + for benchmarker in benchmarkers + } + return influence + + +def calc_weighted_deposit(deposit: float, seconds_till_round_end: int, lock_seconds: int) -> float: + """ + Calculate weighted deposit + + Args: + deposit: Amount to deposit + seconds_till_round_end: Seconds remaining in current round + lock_seconds: Total lock duration in seconds + + Returns: + Weighted deposit + """ + weighted_deposit = 0 + + if lock_seconds <= 0: + return weighted_deposit + + # Calculate first chunk (partial week) + weighted_deposit += deposit * min(seconds_till_round_end, lock_seconds) // lock_seconds + + remaining_seconds = lock_seconds - min(seconds_till_round_end, lock_seconds) + weight = 2 + while remaining_seconds > 0: + chunk_seconds = min(remaining_seconds, 604800) + chunk = deposit * chunk_seconds // lock_seconds + weighted_deposit += chunk * weight + remaining_seconds -= chunk_seconds + weight = min(weight + 1, 26) + + return weighted_deposit \ No newline at end of file diff --git a/tig-benchmarker/common/structs.py b/tig-benchmarker/common/structs.py index c2d29f1..4a69547 100644 --- a/tig-benchmarker/common/structs.py +++ b/tig-benchmarker/common/structs.py @@ -201,6 +201,7 @@ class OPoWBlockData(FromDict): num_qualifiers_by_challenge: Dict[str, int] cutoff: int delegated_weighted_deposit: PreciseNumber + self_deposit: PreciseNumber delegators: Set[str] reward_share: float imbalance: PreciseNumber @@ -221,13 +222,13 @@ class PlayerDetails(FromDict): class PlayerState(FromDict): total_fees_paid: PreciseNumber available_fee_balance: PreciseNumber - delegatee: Optional[dict] + delegatees: Optional[dict] votes: dict reward_share: Optional[dict] @dataclass class PlayerBlockData(FromDict): - delegatee: Optional[str] + delegatees: Dict[str, float] reward_by_type: Dict[str, PreciseNumber] deposit_by_locked_period: List[PreciseNumber] weighted_deposit: PreciseNumber From 48d8dcd88889739d2c847a98197d6a6f1d18ffba Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Tue, 31 Dec 2024 22:20:04 +0800 Subject: [PATCH 06/49] Move envvars to .env file. --- .gitignore | 1 - tig-benchmarker/.env | 7 +++++++ tig-benchmarker/README.md | 20 ++++++++++++-------- 3 files changed, 19 insertions(+), 9 deletions(-) create mode 100644 tig-benchmarker/.env diff --git a/.gitignore b/.gitignore index 0e380f9..ce4f5b6 100644 --- a/.gitignore +++ b/.gitignore @@ -18,5 +18,4 @@ Cargo.lock __pycache__/ # Ignore these. -.env db_data/ \ No newline at end of file diff --git a/tig-benchmarker/.env b/tig-benchmarker/.env new file mode 100644 index 0000000..31520d1 --- /dev/null +++ b/tig-benchmarker/.env @@ -0,0 +1,7 @@ +POSTGRES_USER=postgres +POSTGRES_PASSWORD=mysecretpassword +POSTGRES_DB=postgres +UI_PORT=80 +DB_PORT=5432 +MASTER_PORT=5115 +VERBOSE= \ No newline at end of file diff --git a/tig-benchmarker/README.md b/tig-benchmarker/README.md index 4285f64..b5be57e 100644 --- a/tig-benchmarker/README.md +++ b/tig-benchmarker/README.md @@ -7,17 +7,21 @@ Benchmarker for TIG. Expected setup is a single master and multiple slaves on di Simply run: ``` -POSTGRES_USER=postgres \ -POSTGRES_PASSWORD=mysecretpassword \ -POSTGRES_DB=postgres \ -UI_PORT=80 \ -DB_PORT=5432 \ -MASTER_PORT=5115 \ -# set VERBOSE=1 for debug master logs -VERBOSE= \ docker-compose up --build ``` +This uses the `.env` file: + +``` +POSTGRES_USER=postgres +POSTGRES_PASSWORD=mysecretpassword +POSTGRES_DB=postgres +UI_PORT=80 +DB_PORT=5432 +MASTER_PORT=5115 +VERBOSE= +``` + See last section on how to find your player_id & api_key. **Notes:** From fb4eba57569f00d9aeca817d7f98982da1a05245 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Tue, 31 Dec 2024 22:20:57 +0800 Subject: [PATCH 07/49] Hardcode master port to 5115. --- tig-benchmarker/master/master/slave_manager.py | 4 ++-- tig-benchmarker/postgres/init.sql | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/tig-benchmarker/master/master/slave_manager.py b/tig-benchmarker/master/master/slave_manager.py index 100f2ff..aede5d1 100644 --- a/tig-benchmarker/master/master/slave_manager.py +++ b/tig-benchmarker/master/master/slave_manager.py @@ -276,8 +276,8 @@ class SlaveManager: return {"status": "OK"} config = CONFIG["slave_manager_config"] - thread = Thread(target=lambda: uvicorn.run(app, host="0.0.0.0", port=config["port"])) + thread = Thread(target=lambda: uvicorn.run(app, host="0.0.0.0", port=5115)) thread.daemon = True thread.start() - logger.info(f"webserver started on 0.0.0.0:{config['port']}") + logger.info(f"webserver started on 0.0.0.0:5115") diff --git a/tig-benchmarker/postgres/init.sql b/tig-benchmarker/postgres/init.sql index a822447..fa413be 100644 --- a/tig-benchmarker/postgres/init.sql +++ b/tig-benchmarker/postgres/init.sql @@ -160,7 +160,6 @@ SELECT ' } }, "slave_manager_config": { - "port": 5115, "time_before_batch_retry": 60000, "slaves": [ { From 587ea86692d4392ae1517eff1792ca5e36614480 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Tue, 31 Dec 2024 22:21:48 +0800 Subject: [PATCH 08/49] Refresh db connection. --- .../master/master/client_manager.py | 10 ++++---- tig-benchmarker/master/master/job_manager.py | 24 +++++++++---------- .../master/master/precommit_manager.py | 4 ++-- .../master/master/slave_manager.py | 10 ++++---- tig-benchmarker/master/master/sql.py | 20 +++++++++------- .../master/master/submissions_manager.py | 10 ++++---- 6 files changed, 41 insertions(+), 37 deletions(-) diff --git a/tig-benchmarker/master/master/client_manager.py b/tig-benchmarker/master/master/client_manager.py index f46667e..d6faf07 100644 --- a/tig-benchmarker/master/master/client_manager.py +++ b/tig-benchmarker/master/master/client_manager.py @@ -5,7 +5,7 @@ import threading import uvicorn import json from datetime import datetime -from master.sql import db_conn +from master.sql import get_db_conn from fastapi import FastAPI, Query, Request, HTTPException, Depends, Header from fastapi.responses import JSONResponse from fastapi.middleware.cors import CORSMiddleware @@ -21,7 +21,7 @@ class ClientManager: logger.info("ClientManager initialized and connected to the database.") # Fetch initial config from database - result = db_conn.fetch_one( + result = get_db_conn().fetch_one( """ SELECT config FROM config LIMIT 1 @@ -54,7 +54,7 @@ class ClientManager: @self.app.get('/stop/{benchmark_id}') async def stop_benchmark(benchmark_id: str): try: - db_conn.execute( + get_db_conn().execute( """ UPDATE job SET stopped = true @@ -76,7 +76,7 @@ class ClientManager: new_config["api_url"] = new_config["api_url"].rstrip('/') # Update config in database - db_conn.execute( + get_db_conn().execute( """ DELETE FROM config; INSERT INTO config (config) @@ -95,7 +95,7 @@ class ClientManager: @self.app.get("/get-jobs") async def get_jobs(): - result = db_conn.fetch_all( + result = get_db_conn().fetch_all( """ WITH recent_jobs AS ( SELECT benchmark_id diff --git a/tig-benchmarker/master/master/job_manager.py b/tig-benchmarker/master/master/job_manager.py index 82bbc0a..c956235 100644 --- a/tig-benchmarker/master/master/job_manager.py +++ b/tig-benchmarker/master/master/job_manager.py @@ -5,7 +5,7 @@ from common.merkle_tree import MerkleHash, MerkleBranch, MerkleTree from common.structs import * from common.utils import * from typing import Dict, List, Optional, Set -from master.sql import db_conn +from master.sql import get_db_conn from master.client_manager import CONFIG import math @@ -39,7 +39,7 @@ class JobManager: for benchmark_id, x in precommits.items(): if ( benchmark_id in proofs or - db_conn.fetch_one( # check if job is already created + get_db_conn().fetch_one( # check if job is already created """ SELECT 1 FROM job @@ -122,14 +122,14 @@ class JobManager: ) ] - db_conn.execute_many(*atomic_inserts) + get_db_conn().execute_many(*atomic_inserts) # update jobs from confirmed benchmarks for benchmark_id, x in benchmarks.items(): if ( benchmark_id in proofs or - (result := db_conn.fetch_one( + (result := get_db_conn().fetch_one( """ SELECT num_batches, batch_size FROM job @@ -171,11 +171,11 @@ class JobManager: ) ] - db_conn.execute_many(*atomic_update) + get_db_conn().execute_many(*atomic_update) # update jobs from confirmed proofs if len(proofs) > 0: - db_conn.execute( + get_db_conn().execute( """ UPDATE job SET end_time = (EXTRACT(EPOCH FROM NOW()) * 1000)::BIGINT @@ -186,7 +186,7 @@ class JobManager: ) # stop any expired jobs - db_conn.execute( + get_db_conn().execute( """ UPDATE job SET stopped = true @@ -202,7 +202,7 @@ class JobManager: now = int(time.time() * 1000) # Find jobs where all root_batchs are ready - rows = db_conn.fetch_all( + rows = get_db_conn().fetch_all( """ WITH ready AS ( SELECT A.benchmark_id @@ -241,7 +241,7 @@ class JobManager: merkle_root = tree.calc_merkle_root() # Update the database with calculated merkle root - db_conn.execute_many(*[ + get_db_conn().execute_many(*[ ( """ UPDATE job_data @@ -266,7 +266,7 @@ class JobManager: ]) # Find jobs where all proofs_batchs are ready - rows = db_conn.fetch_all( + rows = get_db_conn().fetch_all( """ WITH ready AS ( SELECT A.benchmark_id @@ -306,7 +306,7 @@ class JobManager: for x in y ] - batch_merkle_roots = db_conn.fetch_one( + batch_merkle_roots = get_db_conn().fetch_one( """ SELECT JSONB_AGG(merkle_root ORDER BY batch_idx) as batch_merkle_roots FROM batch_data @@ -341,7 +341,7 @@ class JobManager: ) # Update database with calculated merkle proofs - db_conn.execute_many(*[ + get_db_conn().execute_many(*[ ( """ UPDATE job_data diff --git a/tig-benchmarker/master/master/precommit_manager.py b/tig-benchmarker/master/master/precommit_manager.py index 1e3a58e..bbba4f5 100644 --- a/tig-benchmarker/master/master/precommit_manager.py +++ b/tig-benchmarker/master/master/precommit_manager.py @@ -6,7 +6,7 @@ from master.submissions_manager import SubmitPrecommitRequest from common.structs import * from common.utils import FromDict from typing import Dict, List, Optional, Set -from master.sql import db_conn +from master.sql import get_db_conn from master.client_manager import CONFIG logger = logging.getLogger(os.path.splitext(os.path.basename(__file__))[0]) @@ -93,7 +93,7 @@ class PrecommitManager: logger.info(f"global qualifier difficulty stats for {challenges[c_id].details.name}: (#nonces: {x['nonces']}, #solutions: {x['solutions']}, avg_nonces_per_solution: {avg_nonces_per_solution})") def run(self, difficulty_samples: Dict[str, List[int]]) -> SubmitPrecommitRequest: - num_pending_jobs = db_conn.fetch_one( + num_pending_jobs = get_db_conn().fetch_one( """ SELECT COUNT(*) FROM job diff --git a/tig-benchmarker/master/master/slave_manager.py b/tig-benchmarker/master/master/slave_manager.py index aede5d1..94df085 100644 --- a/tig-benchmarker/master/master/slave_manager.py +++ b/tig-benchmarker/master/master/slave_manager.py @@ -13,7 +13,7 @@ import uvicorn from common.structs import * from common.utils import * from typing import Dict, List, Optional, Set -from master.sql import db_conn +from master.sql import get_db_conn from master.client_manager import CONFIG @@ -27,7 +27,7 @@ class SlaveManager: def run(self): with self.lock: - self.batches = db_conn.fetch_all( + self.batches = get_db_conn().fetch_all( """ SELECT * FROM ( SELECT @@ -145,7 +145,7 @@ class SlaveManager: if len(concurrent) == 0: logger.debug(f"no batches available for {slave_name}") if len(updates) > 0: - db_conn.execute_many(*updates) + get_db_conn().execute_many(*updates) return JSONResponse(content=jsonable_encoder(concurrent)) @app.post('/submit-batch-root/{batch_id}') @@ -182,7 +182,7 @@ class SlaveManager: # Update roots table with merkle root and solution nonces benchmark_id, batch_idx = batch_id.split("_") batch_idx = int(batch_idx) - db_conn.execute_many(*[ + get_db_conn().execute_many(*[ ( """ UPDATE root_batch @@ -247,7 +247,7 @@ class SlaveManager: # Update proofs table with merkle proofs benchmark_id, batch_idx = batch_id.split("_") batch_idx = int(batch_idx) - db_conn.execute_many(*[ + get_db_conn().execute_many(*[ ( """ UPDATE proofs_batch diff --git a/tig-benchmarker/master/master/sql.py b/tig-benchmarker/master/master/sql.py index 0c4d46f..288e444 100644 --- a/tig-benchmarker/master/master/sql.py +++ b/tig-benchmarker/master/master/sql.py @@ -100,11 +100,15 @@ class PostgresDB: db_conn = None -if db_conn is None: - db_conn = PostgresDB( - host=os.environ["POSTGRES_HOST"], - port=5432, - dbname=os.environ["POSTGRES_DB"], - user=os.environ["POSTGRES_USER"], - password=os.environ["POSTGRES_PASSWORD"] - ) \ No newline at end of file + +def get_db_conn(): + global db_conn + if db_conn is None or db_conn.closed: + db_conn = PostgresDB( + host=os.environ["POSTGRES_HOST"], + port=5432, + dbname=os.environ["POSTGRES_DB"], + user=os.environ["POSTGRES_USER"], + password=os.environ["POSTGRES_PASSWORD"] + ) + return db_conn \ No newline at end of file diff --git a/tig-benchmarker/master/master/submissions_manager.py b/tig-benchmarker/master/master/submissions_manager.py index e941e56..16cf85f 100644 --- a/tig-benchmarker/master/master/submissions_manager.py +++ b/tig-benchmarker/master/master/submissions_manager.py @@ -7,7 +7,7 @@ import os from common.structs import * from common.utils import * from typing import Union, Set, List, Dict -from master.sql import db_conn +from master.sql import get_db_conn from master.client_manager import CONFIG logger = logging.getLogger(os.path.splitext(os.path.basename(__file__))[0]) @@ -72,7 +72,7 @@ class SubmissionsManager: **kwargs ): if len(benchmarks) > 0: - db_conn.execute( + get_db_conn().execute( """ UPDATE job SET benchmark_submitted = true @@ -82,7 +82,7 @@ class SubmissionsManager: ) if len(proofs) > 0: - db_conn.execute( + get_db_conn().execute( """ UPDATE job SET proof_submitted = true @@ -100,7 +100,7 @@ class SubmissionsManager: else: self._post_thread("precommit", submit_precommit_req) - benchmark_to_submit = db_conn.fetch_one( + benchmark_to_submit = get_db_conn().fetch_one( """ WITH updated AS ( UPDATE job @@ -144,7 +144,7 @@ class SubmissionsManager: else: logger.debug("no benchmark to submit") - proof_to_submit = db_conn.fetch_one( + proof_to_submit = get_db_conn().fetch_one( """ WITH updated AS ( UPDATE job From 614de4fe302f9a280f7f3bcd4631963cb664b59c Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Tue, 31 Dec 2024 22:31:20 +0800 Subject: [PATCH 09/49] Address warning there is already a transaction in progress. --- tig-benchmarker/master/master/sql.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tig-benchmarker/master/master/sql.py b/tig-benchmarker/master/master/sql.py index 288e444..311a622 100644 --- a/tig-benchmarker/master/master/sql.py +++ b/tig-benchmarker/master/master/sql.py @@ -17,10 +17,15 @@ class PostgresDB: } self._conn = None + @property + def closed(self) -> bool: + return self._conn is None or self._conn.closed + def connect(self) -> None: """Establish connection to PostgreSQL database""" try: self._conn = psycopg2.connect(**self.conn_params) + self._conn.autocommit = False logger.info(f"Connected to PostgreSQL database at {self.conn_params['host']}:{self.conn_params['port']}") except Exception as e: logger.error(f"Error connecting to PostgreSQL: {str(e)}") @@ -40,7 +45,6 @@ class PostgresDB: try: with self._conn.cursor() as cur: - cur.execute("BEGIN") for query in args: cur.execute(*query) self._conn.commit() From 3138f31c0007b5506f42c1fdb9a9860fb5957007 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Tue, 31 Dec 2024 22:54:05 +0800 Subject: [PATCH 10/49] Slave kills tig-worker if master stops batch. --- tig-benchmarker/slave.py | 39 +++++++++++++++++++++++++++------------ 1 file changed, 27 insertions(+), 12 deletions(-) diff --git a/tig-benchmarker/slave.py b/tig-benchmarker/slave.py index 13762c8..cfcaeab 100644 --- a/tig-benchmarker/slave.py +++ b/tig-benchmarker/slave.py @@ -53,18 +53,30 @@ def run_tig_worker(tig_worker_path, batch, wasm_path, num_workers, output_path): process = subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - stdout, stderr = process.communicate() - if process.returncode != 0: - PROCESSING_BATCH_IDS.remove(batch["id"]) - raise Exception(f"tig-worker failed: {stderr.decode()}") - result = json.loads(stdout.decode()) - logger.info(f"computing batch took {now() - start}ms") - logger.debug(f"batch result: {result}") - with open(f"{output_path}/{batch['id']}/result.json", "w") as f: - json.dump(result, f) - - PROCESSING_BATCH_IDS.remove(batch["id"]) - READY_BATCH_IDS.add(batch["id"]) + while True: + ret = process.poll() + if ret is not None: + if ret != 0: + PROCESSING_BATCH_IDS.remove(batch["id"]) + raise Exception(f"tig-worker failed with return code {ret}") + + stdout, stderr = process.communicate() + result = json.loads(stdout.decode()) + logger.info(f"computing batch {batch['id']} took {now() - start}ms") + logger.debug(f"batch {batch['id']} result: {result}") + with open(f"{output_path}/{batch['id']}/result.json", "w") as f: + json.dump(result, f) + + PROCESSING_BATCH_IDS.remove(batch["id"]) + READY_BATCH_IDS.add(batch["id"]) + break + + elif batch["id"] not in PROCESSING_BATCH_IDS: + process.kill() + logger.info(f"batch {batch['id']} stopped") + break + + time.sleep(0.1) def purge_folders(output_path, ttl): @@ -214,6 +226,9 @@ def poll_batches(session, master_ip, master_port, output_path): json.dump(batch, f) PENDING_BATCH_IDS.clear() PENDING_BATCH_IDS.update(root_batch_ids + proofs_batch_ids) + for batch_id in PROCESSING_BATCH_IDS - set(root_batch_ids + proofs_batch_ids): + logger.info(f"stopping batch {batch_id}") + PROCESSING_BATCH_IDS.remove(batch_id) time.sleep(5) else: From 715da6cd6904d4fb357b04304cb338e136ceaed2 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Sun, 5 Jan 2025 11:13:24 +0800 Subject: [PATCH 11/49] Only process votes for players who have active deposits. --- tig-protocol/src/contracts/algorithms.rs | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tig-protocol/src/contracts/algorithms.rs b/tig-protocol/src/contracts/algorithms.rs index 1cd7787..3cb824f 100644 --- a/tig-protocol/src/contracts/algorithms.rs +++ b/tig-protocol/src/contracts/algorithms.rs @@ -148,6 +148,7 @@ pub(crate) async fn update(cache: &mut AddBlockCache) { let active_algorithm_ids = &block_data.active_ids[&ActiveType::Algorithm]; let active_breakthrough_ids = &block_data.active_ids[&ActiveType::Breakthrough]; let active_challenge_ids = &block_data.active_ids[&ActiveType::Challenge]; + let active_player_ids = &block_data.active_ids[&ActiveType::Player]; // update votes for breakthrough_state in voting_breakthroughs_state.values_mut() { @@ -156,7 +157,8 @@ pub(crate) async fn update(cache: &mut AddBlockCache) { (false, PreciseNumber::from(0)), ]); } - for (player_id, player_state) in active_players_state.iter() { + for player_id in active_player_ids.iter() { + let player_state = &active_players_state[player_id]; let player_data = &active_players_block_data[player_id]; for (breakthrough_id, vote) in player_state.votes.iter() { let yes = vote.value; From 14373ff80c4d2df5ffb4d313a7ee48c446fb30da Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 9 Jan 2025 11:25:43 +0800 Subject: [PATCH 12/49] Add delay before voting begins. --- tig-protocol/src/contracts/players.rs | 4 ++-- tig-structs/src/config.rs | 1 + tig-structs/src/core.rs | 1 + 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/tig-protocol/src/contracts/players.rs b/tig-protocol/src/contracts/players.rs index 652d1e3..7991a8c 100644 --- a/tig-protocol/src/contracts/players.rs +++ b/tig-protocol/src/contracts/players.rs @@ -212,8 +212,8 @@ pub async fn set_vote( .get_breakthrough_state(&breakthrough_id) .await .ok_or_else(|| anyhow!("Invalid breakthrough '{}'", breakthrough_id))?; - if breakthrough_state.round_pushed > latest_block_details.round - && latest_block_details.round >= breakthrough_state.round_votes_tallied + if latest_block_details.round < breakthrough_state.round_voting_starts + || latest_block_details.round >= breakthrough_state.round_votes_tallied { return Err(anyhow!("Cannot vote on breakthrough '{}'", breakthrough_id)); } diff --git a/tig-structs/src/config.rs b/tig-structs/src/config.rs index 4da83da..2c82563 100644 --- a/tig-structs/src/config.rs +++ b/tig-structs/src/config.rs @@ -23,6 +23,7 @@ serializable_struct_with_getters! { BreakthroughsConfig { bootstrap_address: String, min_percent_yes_votes: f64, + vote_start_delay: u32, vote_period: u32, min_lock_period_to_vote: u32, submission_fee: PreciseNumber, diff --git a/tig-structs/src/core.rs b/tig-structs/src/core.rs index 6b88564..553972c 100644 --- a/tig-structs/src/core.rs +++ b/tig-structs/src/core.rs @@ -272,6 +272,7 @@ serializable_struct_with_getters! { block_confirmed: u32, round_submitted: u32, round_pushed: u32, + round_voting_starts: u32, round_votes_tallied: u32, votes_tally: HashMap, round_active: Option, From 30a0f59f30f0637dd8b40e1c29c5e03cf83db0eb Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 9 Jan 2025 15:46:37 +0800 Subject: [PATCH 13/49] Update description for knapsack. --- docs/challenges/knapsack.md | 48 ++++++++++++++++++++----------------- 1 file changed, 26 insertions(+), 22 deletions(-) diff --git a/docs/challenges/knapsack.md b/docs/challenges/knapsack.md index cecb097..be7e71d 100644 --- a/docs/challenges/knapsack.md +++ b/docs/challenges/knapsack.md @@ -1,50 +1,54 @@ # Knapsack Problem -The quadratic knapsack problem is one of the most popular variants of the single knapsack problem with applications in many optimization problems. The aim is to maximise the value of individual items placed in the knapsack while satisfying a weight constraint. However, pairs of items also have interaction values which may be negative or positive that are added to the total value within the knapsack. +The quadratic knapsack problem is one of the most popular variants of the single knapsack problem, with applications in many optimization contexts. The aim is to maximize the value of individual items placed in the knapsack while satisfying a weight constraint. However, pairs of items also have positive interaction values, contributing to the total value within the knapsack. -# Example + +## Challenge Overview For our challenge, we use a version of the quadratic knapsack problem with configurable difficulty, where the following two parameters can be adjusted in order to vary the difficulty of the challenge: - Parameter 1: $num\textunderscore{ }items$ is the number of items from which you need to select a subset to put in the knapsack. -- Parameter 2: $better\textunderscore{ }than\textunderscore{ }baseline \geq 1$ is the factor by which a solution must be better than the baseline value [link TIG challenges for explanation of baseline value]. - +- Parameter 2: $better\textunderscore{ }than\textunderscore{ }baseline \geq 1$ is the factor by which a solution must be better than the baseline value. The larger the $num\textunderscore{ }items$, the more number of possible $S_{knapsack}$, making the challenge more difficult. Also, the higher $better\textunderscore{ }than\textunderscore{ }baseline$, the less likely a given $S_{knapsack}$ will be a solution, making the challenge more difficult. -The weight $w_i$ of each of the $num\textunderscore{ }items$ is an integer, chosen independently, uniformly at random, and such that each of the item weights $1 <= w_i <= 50$, for $i=1,2,...,num\textunderscore{ }items$. The individual values of the items $v_i$ are selected by random from the range $50 <= v_i <= 100$, and the interaction values of pairs of items $V_{ij}$ are selected by random from the range $-50 <= V_{ij} <= 50$. +The weight $w_i$ of each of the $num\textunderscore{ }items$ is an integer, chosen independently, uniformly at random, and such that each of the item weights $1 <= w_i <= 50$, for $i=1,2,...,num\textunderscore{ }items$. The values of the items are nonzero with a density of 25%, meaning they have a 25% probability of being nonzero. The nonzero individual values of the item, $v_i$, and the nonzero interaction values of pairs of items, $V_{ij}$, are selected at random from the range $[1,100]$. -The total value of a knapsack is determined by summing up the individual values of items in the knapsack, as well as the interaction values of every pair of items $(i,j)$ where $i > j$ in the knapsack: +The total value of a knapsack is determined by summing up the individual values of items in the knapsack, as well as the interaction values of every pair of items \((i,j)\), where \( i > j \), in the knapsack: -$$V_{knapsack} = \sum_{i \in knapsack}{v_i} + \sum_{(i,j)\in knapsack}{V_{ij}}$$ +$$ +V_{knapsack} = \sum_{i \in knapsack}{v_i} + \sum_{(i,j)\in knapsack}{V_{ij}} +$$ We impose a weight constraint $W(S_{knapsack}) <= 0.5 \cdot W(S_{all})$, where the knapsack can hold at most half the total weight of all items. -Consider an example of a challenge instance with `num_items=4` and `better_than_baseline = 1.10`. Let the baseline value be 150: +# Example + +Consider an example of a challenge instance with `num_items=4` and `better_than_baseline = 1.50`. Let the baseline value be 46: ``` -weights = [26, 20, 39, 13] -individual_values = [63, 87, 52, 97] -interaction_values = [ 0, 23, -18, -37 - 23, 0, 42, -28 - -18, 42, 0, 32 - -37, -28, 32, 0] -max_weight = 60 -min_value = baseline*better_than_baseline = 165 +weights = [39, 29, 15, 43] +individual_values = [0, 14, 0, 75] +interaction_values = [ 0, 0, 0, 0 + 0, 0, 32, 0 + 0, 32, 0, 0 + 0, 0, 0, 0] +max_weight = 63 +min_value = baseline*better_than_baseline = 69 ``` -The objective is to find a set of items where the total weight is at most 60 but has a total value of at least 165. +The objective is to find a set of items where the total weight is at most 63 but has a total value of at least 69. Now consider the following selection: ``` -selected_items = [0, 1, 3] +selected_items = [2, 3] ``` -When evaluating this selection, we can confirm that the total weight is less than 60, and the total value is more than 165, thereby this selection of items is a solution: +When evaluating this selection, we can confirm that the total weight is less than 63, and the total value is more than 69, thereby this selection of items is a solution: -* Total weight = 26 + 20 + 13 = 59 -* Total value = 63 + 52 + 97 + 23 - 37 - 28 = 170 +* Total weight = 15 + 43 = 58 +* Total value = 0 + 75 + 0 = 75 # Our Challenge -In TIG, the baseline value is determined by a greedy algorithm that simply iterates through items sorted by potential value to weight ratio, adding them if knapsack is still below the weight constraint. +In TIG, the baseline value is determined by a two-stage approach. First, items are selected based on their value-to-weight ratio, including interaction values, until the capacity is reached. Then, a tabu-based local search refines the solution by swapping items to improve value while avoiding reversals, with early termination for unpromising swaps. From 6e445c35c75d53ce315257644448eea721ba1743 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 9 Jan 2025 19:25:29 +0800 Subject: [PATCH 14/49] Lock rust-toolchain to v1.82.0 --- .github/workflows/build_algorithm.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_algorithm.yml b/.github/workflows/build_algorithm.yml index 7c3ce36..487126f 100644 --- a/.github/workflows/build_algorithm.yml +++ b/.github/workflows/build_algorithm.yml @@ -38,7 +38,7 @@ jobs: echo "CHALLENGE=$CHALLENGE" >> $GITHUB_ENV echo "ALGORITHM=$ALGORITHM" >> $GITHUB_ENV echo "WASM_PATH=$ALGORITHM" >> $GITHUB_ENV - - uses: dtolnay/rust-toolchain@stable + - uses: dtolnay/rust-toolchain@1.82.0 with: targets: wasm32-wasi # - name: Install CUDA From f7910611429b5e9f250838ad39ae354642ed99bc Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Sat, 11 Jan 2025 01:44:27 +0800 Subject: [PATCH 15/49] Increase default max fuel to 10 billion. --- scripts/test_algorithm.sh | 8 +++++++- tig-worker/src/main.rs | 2 +- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/scripts/test_algorithm.sh b/scripts/test_algorithm.sh index 14b04b2..fb6ebf2 100644 --- a/scripts/test_algorithm.sh +++ b/scripts/test_algorithm.sh @@ -76,6 +76,12 @@ if ! is_positive_integer "$num_workers"; then echo "Error: Number of workers must be a positive integer." exit 1 fi +read -p "Enter max fuel (default is 10000000000): " max_fuel +max_fuel=${max_fuel:-1} +if ! is_positive_integer "$max_fuel"; then + echo "Error: Max fuel must be a positive integer." + exit 1 +fi read -p "Enable debug mode? (leave blank to disable) " enable_debug if [[ -n $enable_debug ]]; then debug_mode=true @@ -117,7 +123,7 @@ while [ $remaining_nonces -gt 0 ]; do start_time=$(date +%s%3N) stdout=$(mktemp) stderr=$(mktemp) - ./target/release/tig-worker compute_batch "$SETTINGS" "random_string" $current_nonce $nonces_to_compute $power_of_2_nonces $REPO_DIR/tig-algorithms/wasm/$CHALLENGE/$ALGORITHM.wasm --workers $nonces_to_compute >"$stdout" 2>"$stderr" + ./target/release/tig-worker compute_batch "$SETTINGS" "random_string" $current_nonce $nonces_to_compute $power_of_2_nonces $REPO_DIR/tig-algorithms/wasm/$CHALLENGE/$ALGORITHM.wasm --workers $nonces_to_compute --fuel $max_fuel >"$stdout" 2>"$stderr" exit_code=$? output_stdout=$(cat "$stdout") output_stderr=$(cat "$stderr") diff --git a/tig-worker/src/main.rs b/tig-worker/src/main.rs index 260ad47..e2bea8b 100644 --- a/tig-worker/src/main.rs +++ b/tig-worker/src/main.rs @@ -28,7 +28,7 @@ fn cli() -> Command { .arg(arg!( "Path to a wasm file").value_parser(clap::value_parser!(PathBuf))) .arg( arg!(--fuel [FUEL] "Optional maximum fuel parameter for WASM VM") - .default_value("2000000000") + .default_value("10000000000") .value_parser(clap::value_parser!(u64)), ) .arg( From 07737efe621eb277e4b38f7b8b4ac930eba25eb8 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Sun, 12 Jan 2025 15:45:31 +0000 Subject: [PATCH 16/49] Change breakthrough submission flow. --- docs/agreements/invention_assignment.doc | Bin 73728 -> 74240 bytes tig-breakthroughs/README.md | 24 +++++++++---- .../knapsack/{template.md => evidence.md} | 34 +++++++++++++++--- .../evidence.md} | 34 +++++++++++++++--- .../template.md => vector_search/evidence.md} | 34 +++++++++++++++--- .../{template.md => evidence.md} | 34 +++++++++++++++--- 6 files changed, 133 insertions(+), 27 deletions(-) rename tig-breakthroughs/knapsack/{template.md => evidence.md} (87%) rename tig-breakthroughs/{vector_search/template.md => satisfiability/evidence.md} (87%) rename tig-breakthroughs/{satisfiability/template.md => vector_search/evidence.md} (87%) rename tig-breakthroughs/vehicle_routing/{template.md => evidence.md} (87%) diff --git a/docs/agreements/invention_assignment.doc b/docs/agreements/invention_assignment.doc index b74d27ff61119add9753235e39a34ff64ba00b25..deef5578bf755cd0def450bd24fff77ee8037fb1 100644 GIT binary patch delta 8810 zcmcJV30%}imdC4YE|mu5Xl(@ib4bgjMm*yMXuK6oG;wC4YXdFa(sYxiL5as-5~E8* z5zCDu5w9dt zZS!L9seMWY^M-Dm9Yb8d3hRRe{nPn=4Bp%okATZ4hV;+ zjBp>(U2frjllK&z^RNwq6Xr7jUVQ+&ln|E-_6qludV331(x6g5?((7ujLoH0avV}W z4w0Yrj7^j*BcHe)k}jo{=;Ki;7MMr86UcLYhKMO+9lDMcKor=*V9(#!h;K5lJI9pD z55VF|G|9mNN4pG@1q*{9`(S1JA*B_jqYQd`(94nOcKP+5_8F5ro-sK~8Ot?3V<(aw zWVobf*KYI7D%9V=f=RQKQfYRQY+JUN$Trd5ktDYT2v#@7^9xv0iColBNx?mp=HXuM zlz9*DE&U~{ou$u+*S328ndiu^ZKA_U>oA{FGHbU^3uSywXRS=ZA9g+-l&P_31=Bo1 zlb5H@GUjNFme~qxt?xCNn4vXWG0rd1HW1gTlW*G7_^~Nkg zV=NT(Ik^Tc^`WuXT69{o7Mza zwRst4eL6Hn4E>5WgE|skK<}AOslQ;>=H*gVffh~BKgJPK&00;iMQ1kUXX%8O3iO#; 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Of particular importance is Section 1 which describes your breakthrough -3. Copy [invention_assignment.doc](../docs/agreements/invention_assignment.doc), fill in the details, and sign + * **Invention assignment**: copy & replace [invention_assignment.doc](../docs/agreements/invention_assignment.doc) the highlighted parts. Inventor and witness must sign. -4. Email your invention assignment to contact@tig.foundation with subject "Invention Assignment" + * **Address Signature**: use [etherscan](https://etherscan.io/verifiedSignatures#) to sign a message `I am signing this message to confirm my submission of breakthrough `. Use your player_id that is making the submission. Send the verified etherscan link with message and signature. -5. Submit your evidence via https://play.tig.foundation/innovator - * 250 TIG will be deducted from your Available Fee Balance to make a submission - * You can topup via the [Benchmarker page](https://play.tig.foundation/benchmarker) + * (Optional) **Code implementation**: attach code implementing your breakthrough. Do not submit this code to TIG separately. This will be done for you + +**Notes**: +* The time of submission will be taken as the timestamp of the auto-reply to your email attaching the required documents. + +* Iterations are permitted for errors highlighted by the Foundation. This will not change the timestamp of your submission + +* 250 TIG will be deducted from your Available Fee Balance to make a breakthrough submission + +* An additional 10 TIG will be deducted from your Available Fee Balance to make an algorithm submission (if one is attached) + +* You can topup via the [Benchmarker page](https://play.tig.foundation/benchmarker) ## Method Submission Flow 1. New submissions get their branch pushed to a private version of this repository 2. A new submission made during round `X` will have its branch pushed to the public version of this repository at the start of round `X + 2` -3. From the start of round `X + 2` till the start of round `X + 4`, token holders can vote on whether they consider the method to be a breakthrough based off the submitted evidence +3. From the start of round `X + 3` till the start of round `X + 4`, token holders can vote on whether they consider the method to be a breakthrough based off the submitted evidence 4. At the start of round `X + 4`, if the submission has at least 50% yes votes, it becomes active 5. Every block, a method's adoption is the sum of all algorithm adoption, where the algorithm is attributed to that method. Methods with at least 50% adoption earn rewards and a merge point 6. At the end of a round, a method from each challenge with the most merge points, meeting the minimum threshold of 5040, gets merged to the `main` branch diff --git a/tig-breakthroughs/knapsack/template.md b/tig-breakthroughs/knapsack/evidence.md similarity index 87% rename from tig-breakthroughs/knapsack/template.md rename to tig-breakthroughs/knapsack/evidence.md index 43c8fa0..033a260 100644 --- a/tig-breakthroughs/knapsack/template.md +++ b/tig-breakthroughs/knapsack/evidence.md @@ -23,19 +23,35 @@ WHEN PROVIDING EVIDENCE, YOU MAY CITE LINKS TO EXTERNAL DATA SOURCES. ## UNIQUE ALGORITHM IDENTIFIER (UAI) -> UAI PLACEHOLDER - DO NOT EDIT +> UAI PLACEHOLDER - ASSIGNED BY TIG PROTOCOL -## DESCRIPTION OF ALGORITHMIC METHOD +## SECTION 1 + +IT IS IMPORTANT THAT THIS SECTION IS COMPLETED IN SUFFICIENT DETAIL TO FULLY DESCRIBE THE METHOD BECAUSE THIS WILL DEFINE THE METHOD THAT IS THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. + +### DESCRIPTION OF ALGORITHMIC METHOD PLEASE IDENTIFY WHICH TIG CHALLENGE THE ALGORITHMIC METHOD ADDRESSES. -> knapsack - DO NOT EDIT +> knapsack PLEASE DESCRIBE THE ALGORITHMIC METHOD AND THE PROBLEM THAT IT SOLVES. > YOUR RESPONSE HERE -## ATTRIBUTION +## SECTION 2 + +THE COPYRIGHT IN THE IMPLEMENTATION WILL BE THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. + +### IMPLEMENTATION OF ALGORITHMIC METHOD + +TO THE EXTENT THAT YOU HAVE IMPLEMENTED THE ALGORITHMIC METHOD IN CODE YOU SHOULD IDENTIFY THE CODE AND SUBMIT IT WITH THIS + +> YOUR RESPONSE HERE + +## SECTION 3 + +### ATTRIBUTION PLEASE PROVIDE THE IDENTITY OF THE CREATOR OF THE ALGORITHMIC METHOD (this should be a natural person or legal entity. If an artificial intelligence has been used to assist in the creation of the algorithmic method then the creator is the operator of the artificial intelligence) @@ -45,7 +61,9 @@ PLEASE PROVIDE EVIDENCE OF AUTHORSHIP WHERE IT IS AVAILABLE. > YOUR RESPONSE HERE -## NOVELTY AND INVENTIVENESS +## SECTION 4 + +### NOVELTY AND INVENTIVENESS To support your claim that an algorithmic method is novel and inventive, you should provide evidence that demonstrates both its uniqueness (novelty) and its non-obviousness (inventiveness) in relation to the existing state of the art. @@ -114,18 +132,24 @@ To support your claim that an algorithmic method is novel and inventive, you sho > YOUR RESPONSE HERE +## SECTION 5 + ### EVIDENCE TO SUPPORT PATENTABILITY - **Development Records:** Please provide documentation of the invention process, including notes, sketches, and software versions, to establish a timeline of your innovation. > YOUR RESPONSE HERE +## SECTION 6 + ### SUGGESTED APPLICATIONS - Please provide suggestions for any real world applications of your abstract algorithmic method that occur to you. > YOUR RESPONSE HERE +## SECTION 7 + ### ANY OTHER INFORMATION - Please provide any other evidence or argument that you think might help support you request for eligibility for Breakthrough Rewards. diff --git a/tig-breakthroughs/vector_search/template.md b/tig-breakthroughs/satisfiability/evidence.md similarity index 87% rename from tig-breakthroughs/vector_search/template.md rename to tig-breakthroughs/satisfiability/evidence.md index 846f3b8..adc25ce 100644 --- a/tig-breakthroughs/vector_search/template.md +++ b/tig-breakthroughs/satisfiability/evidence.md @@ -23,19 +23,35 @@ WHEN PROVIDING EVIDENCE, YOU MAY CITE LINKS TO EXTERNAL DATA SOURCES. ## UNIQUE ALGORITHM IDENTIFIER (UAI) -> UAI PLACEHOLDER - DO NOT EDIT +> UAI PLACEHOLDER - ASSIGNED BY TIG PROTOCOL -## DESCRIPTION OF ALGORITHMIC METHOD +## SECTION 1 + +IT IS IMPORTANT THAT THIS SECTION IS COMPLETED IN SUFFICIENT DETAIL TO FULLY DESCRIBE THE METHOD BECAUSE THIS WILL DEFINE THE METHOD THAT IS THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. + +### DESCRIPTION OF ALGORITHMIC METHOD PLEASE IDENTIFY WHICH TIG CHALLENGE THE ALGORITHMIC METHOD ADDRESSES. -> vector_search - DO NOT EDIT +> satisfiability PLEASE DESCRIBE THE ALGORITHMIC METHOD AND THE PROBLEM THAT IT SOLVES. > YOUR RESPONSE HERE -## ATTRIBUTION +## SECTION 2 + +THE COPYRIGHT IN THE IMPLEMENTATION WILL BE THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. + +### IMPLEMENTATION OF ALGORITHMIC METHOD + +TO THE EXTENT THAT YOU HAVE IMPLEMENTED THE ALGORITHMIC METHOD IN CODE YOU SHOULD IDENTIFY THE CODE AND SUBMIT IT WITH THIS + +> YOUR RESPONSE HERE + +## SECTION 3 + +### ATTRIBUTION PLEASE PROVIDE THE IDENTITY OF THE CREATOR OF THE ALGORITHMIC METHOD (this should be a natural person or legal entity. If an artificial intelligence has been used to assist in the creation of the algorithmic method then the creator is the operator of the artificial intelligence) @@ -45,7 +61,9 @@ PLEASE PROVIDE EVIDENCE OF AUTHORSHIP WHERE IT IS AVAILABLE. > YOUR RESPONSE HERE -## NOVELTY AND INVENTIVENESS +## SECTION 4 + +### NOVELTY AND INVENTIVENESS To support your claim that an algorithmic method is novel and inventive, you should provide evidence that demonstrates both its uniqueness (novelty) and its non-obviousness (inventiveness) in relation to the existing state of the art. @@ -114,18 +132,24 @@ To support your claim that an algorithmic method is novel and inventive, you sho > YOUR RESPONSE HERE +## SECTION 5 + ### EVIDENCE TO SUPPORT PATENTABILITY - **Development Records:** Please provide documentation of the invention process, including notes, sketches, and software versions, to establish a timeline of your innovation. > YOUR RESPONSE HERE +## SECTION 6 + ### SUGGESTED APPLICATIONS - Please provide suggestions for any real world applications of your abstract algorithmic method that occur to you. > YOUR RESPONSE HERE +## SECTION 7 + ### ANY OTHER INFORMATION - Please provide any other evidence or argument that you think might help support you request for eligibility for Breakthrough Rewards. diff --git a/tig-breakthroughs/satisfiability/template.md b/tig-breakthroughs/vector_search/evidence.md similarity index 87% rename from tig-breakthroughs/satisfiability/template.md rename to tig-breakthroughs/vector_search/evidence.md index a6c86a0..16965b1 100644 --- a/tig-breakthroughs/satisfiability/template.md +++ b/tig-breakthroughs/vector_search/evidence.md @@ -23,19 +23,35 @@ WHEN PROVIDING EVIDENCE, YOU MAY CITE LINKS TO EXTERNAL DATA SOURCES. ## UNIQUE ALGORITHM IDENTIFIER (UAI) -> UAI PLACEHOLDER - DO NOT EDIT +> UAI PLACEHOLDER - ASSIGNED BY TIG PROTOCOL -## DESCRIPTION OF ALGORITHMIC METHOD +## SECTION 1 + +IT IS IMPORTANT THAT THIS SECTION IS COMPLETED IN SUFFICIENT DETAIL TO FULLY DESCRIBE THE METHOD BECAUSE THIS WILL DEFINE THE METHOD THAT IS THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. + +### DESCRIPTION OF ALGORITHMIC METHOD PLEASE IDENTIFY WHICH TIG CHALLENGE THE ALGORITHMIC METHOD ADDRESSES. -> satisfiability - DO NOT EDIT +> vector_search PLEASE DESCRIBE THE ALGORITHMIC METHOD AND THE PROBLEM THAT IT SOLVES. > YOUR RESPONSE HERE -## ATTRIBUTION +## SECTION 2 + +THE COPYRIGHT IN THE IMPLEMENTATION WILL BE THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. + +### IMPLEMENTATION OF ALGORITHMIC METHOD + +TO THE EXTENT THAT YOU HAVE IMPLEMENTED THE ALGORITHMIC METHOD IN CODE YOU SHOULD IDENTIFY THE CODE AND SUBMIT IT WITH THIS + +> YOUR RESPONSE HERE + +## SECTION 3 + +### ATTRIBUTION PLEASE PROVIDE THE IDENTITY OF THE CREATOR OF THE ALGORITHMIC METHOD (this should be a natural person or legal entity. If an artificial intelligence has been used to assist in the creation of the algorithmic method then the creator is the operator of the artificial intelligence) @@ -45,7 +61,9 @@ PLEASE PROVIDE EVIDENCE OF AUTHORSHIP WHERE IT IS AVAILABLE. > YOUR RESPONSE HERE -## NOVELTY AND INVENTIVENESS +## SECTION 4 + +### NOVELTY AND INVENTIVENESS To support your claim that an algorithmic method is novel and inventive, you should provide evidence that demonstrates both its uniqueness (novelty) and its non-obviousness (inventiveness) in relation to the existing state of the art. @@ -114,18 +132,24 @@ To support your claim that an algorithmic method is novel and inventive, you sho > YOUR RESPONSE HERE +## SECTION 5 + ### EVIDENCE TO SUPPORT PATENTABILITY - **Development Records:** Please provide documentation of the invention process, including notes, sketches, and software versions, to establish a timeline of your innovation. > YOUR RESPONSE HERE +## SECTION 6 + ### SUGGESTED APPLICATIONS - Please provide suggestions for any real world applications of your abstract algorithmic method that occur to you. > YOUR RESPONSE HERE +## SECTION 7 + ### ANY OTHER INFORMATION - Please provide any other evidence or argument that you think might help support you request for eligibility for Breakthrough Rewards. diff --git a/tig-breakthroughs/vehicle_routing/template.md b/tig-breakthroughs/vehicle_routing/evidence.md similarity index 87% rename from tig-breakthroughs/vehicle_routing/template.md rename to tig-breakthroughs/vehicle_routing/evidence.md index 8837425..6577f8a 100644 --- a/tig-breakthroughs/vehicle_routing/template.md +++ b/tig-breakthroughs/vehicle_routing/evidence.md @@ -23,19 +23,35 @@ WHEN PROVIDING EVIDENCE, YOU MAY CITE LINKS TO EXTERNAL DATA SOURCES. ## UNIQUE ALGORITHM IDENTIFIER (UAI) -> UAI PLACEHOLDER - DO NOT EDIT +> UAI PLACEHOLDER - ASSIGNED BY TIG PROTOCOL -## DESCRIPTION OF ALGORITHMIC METHOD +## SECTION 1 + +IT IS IMPORTANT THAT THIS SECTION IS COMPLETED IN SUFFICIENT DETAIL TO FULLY DESCRIBE THE METHOD BECAUSE THIS WILL DEFINE THE METHOD THAT IS THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. + +### DESCRIPTION OF ALGORITHMIC METHOD PLEASE IDENTIFY WHICH TIG CHALLENGE THE ALGORITHMIC METHOD ADDRESSES. -> vehicle_routing - DO NOT EDIT +> vehicle_routing PLEASE DESCRIBE THE ALGORITHMIC METHOD AND THE PROBLEM THAT IT SOLVES. > YOUR RESPONSE HERE -## ATTRIBUTION +## SECTION 2 + +THE COPYRIGHT IN THE IMPLEMENTATION WILL BE THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. + +### IMPLEMENTATION OF ALGORITHMIC METHOD + +TO THE EXTENT THAT YOU HAVE IMPLEMENTED THE ALGORITHMIC METHOD IN CODE YOU SHOULD IDENTIFY THE CODE AND SUBMIT IT WITH THIS + +> YOUR RESPONSE HERE + +## SECTION 3 + +### ATTRIBUTION PLEASE PROVIDE THE IDENTITY OF THE CREATOR OF THE ALGORITHMIC METHOD (this should be a natural person or legal entity. If an artificial intelligence has been used to assist in the creation of the algorithmic method then the creator is the operator of the artificial intelligence) @@ -45,7 +61,9 @@ PLEASE PROVIDE EVIDENCE OF AUTHORSHIP WHERE IT IS AVAILABLE. > YOUR RESPONSE HERE -## NOVELTY AND INVENTIVENESS +## SECTION 4 + +### NOVELTY AND INVENTIVENESS To support your claim that an algorithmic method is novel and inventive, you should provide evidence that demonstrates both its uniqueness (novelty) and its non-obviousness (inventiveness) in relation to the existing state of the art. @@ -114,18 +132,24 @@ To support your claim that an algorithmic method is novel and inventive, you sho > YOUR RESPONSE HERE +## SECTION 5 + ### EVIDENCE TO SUPPORT PATENTABILITY - **Development Records:** Please provide documentation of the invention process, including notes, sketches, and software versions, to establish a timeline of your innovation. > YOUR RESPONSE HERE +## SECTION 6 + ### SUGGESTED APPLICATIONS - Please provide suggestions for any real world applications of your abstract algorithmic method that occur to you. > YOUR RESPONSE HERE +## SECTION 7 + ### ANY OTHER INFORMATION - Please provide any other evidence or argument that you think might help support you request for eligibility for Breakthrough Rewards. From 70907ed1ddb5f3f0b576b5f5ef306a86f967b864 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Wed, 15 Jan 2025 00:32:47 +0000 Subject: [PATCH 17/49] Remove session from slave to fix connection issue. --- tig-benchmarker/slave.py | 31 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/tig-benchmarker/slave.py b/tig-benchmarker/slave.py index cfcaeab..4f1594b 100644 --- a/tig-benchmarker/slave.py +++ b/tig-benchmarker/slave.py @@ -21,11 +21,11 @@ FINISHED_BATCH_IDS = {} def now(): return int(time.time() * 1000) -def download_wasm(session, download_url, wasm_path): +def download_wasm(download_url, wasm_path): if not os.path.exists(wasm_path): start = now() logger.info(f"downloading WASM from {download_url}") - resp = session.get(download_url) + resp = requests.get(download_url) if resp.status_code != 200: raise Exception(f"status {resp.status_code} when downloading WASM: {resp.text}") with open(wasm_path, 'wb') as f: @@ -93,11 +93,11 @@ def purge_folders(output_path, ttl): for batch_id in purge_batch_ids: if os.path.exists(f"{output_path}/{batch_id}"): logger.info(f"purging batch {batch_id}") - shutil.rmtree(f"{output_path}/{batch_id}") + shutil.rmtree(f"{output_path}/{batch_id}", ignore_errors=True) FINISHED_BATCH_IDS.pop(batch_id) -def send_results(session, master_ip, master_port, tig_worker_path, download_wasms_folder, num_workers, output_path): +def send_results(headers, master_ip, master_port, tig_worker_path, download_wasms_folder, num_workers, output_path): try: batch_id = READY_BATCH_IDS.pop() except KeyError: @@ -130,7 +130,7 @@ def send_results(session, master_ip, master_port, tig_worker_path, download_wasm submit_url = f"http://{master_ip}:{master_port}/submit-batch-root/{batch_id}" logger.info(f"posting root to {submit_url}") - resp = session.post(submit_url, json=result) + resp = requests.post(submit_url, headers=headers, json=result) if resp.status_code == 200: FINISHED_BATCH_IDS[batch_id] = now() logger.info(f"successfully posted root for batch {batch_id}") @@ -163,7 +163,7 @@ def send_results(session, master_ip, master_port, tig_worker_path, download_wasm submit_url = f"http://{master_ip}:{master_port}/submit-batch-proofs/{batch_id}" logger.info(f"posting proofs to {submit_url}") - resp = session.post(submit_url, json={"merkle_proofs": proofs_to_submit}) + resp = requests.post(submit_url, headers=headers, json={"merkle_proofs": proofs_to_submit}) if resp.status_code == 200: FINISHED_BATCH_IDS[batch_id] = now() logger.info(f"successfully posted proofs for batch {batch_id}") @@ -176,7 +176,7 @@ def send_results(session, master_ip, master_port, tig_worker_path, download_wasm time.sleep(2) -def process_batch(session, tig_worker_path, download_wasms_folder, num_workers, output_path): +def process_batch(tig_worker_path, download_wasms_folder, num_workers, output_path): try: batch_id = PENDING_BATCH_IDS.pop() except KeyError: @@ -200,7 +200,7 @@ def process_batch(session, tig_worker_path, download_wasms_folder, num_workers, batch = json.load(f) wasm_path = os.path.join(download_wasms_folder, f"{batch['settings']['algorithm_id']}.wasm") - download_wasm(session, batch['download_url'], wasm_path) + download_wasm(batch['download_url'], wasm_path) Thread( target=run_tig_worker, @@ -208,10 +208,10 @@ def process_batch(session, tig_worker_path, download_wasms_folder, num_workers, ).start() -def poll_batches(session, master_ip, master_port, output_path): +def poll_batches(headers, master_ip, master_port, output_path): get_batches_url = f"http://{master_ip}:{master_port}/get-batches" logger.info(f"fetching batches from {get_batches_url}") - resp = session.get(get_batches_url) + resp = requests.get(get_batches_url, headers=headers) if resp.status_code == 200: batches = resp.json() @@ -262,19 +262,18 @@ def main( raise FileNotFoundError(f"tig-worker not found at path: {tig_worker_path}") os.makedirs(download_wasms_folder, exist_ok=True) - session = requests.Session() - session.headers.update({ + headers = { "User-Agent": slave_name - }) + } Thread( target=wrap_thread, - args=(process_batch, session, tig_worker_path, download_wasms_folder, num_workers, output_path) + args=(process_batch, tig_worker_path, download_wasms_folder, num_workers, output_path) ).start() Thread( target=wrap_thread, - args=(send_results, session, master_ip, master_port, tig_worker_path, download_wasms_folder, num_workers, output_path) + args=(send_results, headers, master_ip, master_port, tig_worker_path, download_wasms_folder, num_workers, output_path) ).start() Thread( @@ -282,7 +281,7 @@ def main( args=(purge_folders, output_path, ttl) ).start() - wrap_thread(poll_batches, session, master_ip, master_port, output_path) + wrap_thread(poll_batches, headers, master_ip, master_port, output_path) if __name__ == "__main__": From 81c01e5bcfe3d16bdc56d2a843604d220e7104a5 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 17:45:54 +0000 Subject: [PATCH 18/49] Allow difficulty_ranges to be disabled. --- .../master/master/difficulty_sampler.py | 22 +++++++++++++------ 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/tig-benchmarker/master/master/difficulty_sampler.py b/tig-benchmarker/master/master/difficulty_sampler.py index 784c4bd..a79d789 100644 --- a/tig-benchmarker/master/master/difficulty_sampler.py +++ b/tig-benchmarker/master/master/difficulty_sampler.py @@ -117,6 +117,7 @@ class DifficultySampler: self.challenges = {} def on_new_block(self, challenges: Dict[str, Challenge], **kwargs): + config = CONFIG["difficulty_sampler_config"] for c in challenges.values(): if c.block_data is None: continue @@ -126,7 +127,10 @@ class DifficultySampler: else: upper_frontier, lower_frontier = c.block_data.base_frontier, c.block_data.scaled_frontier self.valid_difficulties[c.details.name] = calc_valid_difficulties(list(upper_frontier), list(lower_frontier)) - self.frontiers[c.details.name] = calc_all_frontiers(self.valid_difficulties[c.details.name]) + if config["difficulty_ranges"] is None: + self.frontiers[c.details.name] = [] + else: + self.frontiers[c.details.name] = calc_all_frontiers(self.valid_difficulties[c.details.name]) self.challenges = [c.details.name for c in challenges.values()] @@ -153,12 +157,16 @@ class DifficultySampler: logger.debug(f"No valid difficulties found for {c_name} - skipping selected difficulties") if not found_valid: - frontiers = self.frontiers[c_name] - difficulty_range = config["difficulty_ranges"][c_name] - idx1 = math.floor(difficulty_range[0] * (len(frontiers) - 1)) - idx2 = math.ceil(difficulty_range[1] * (len(frontiers) - 1)) - difficulties = [p for frontier in frontiers[idx1:idx2 + 1] for p in frontier] - difficulty = random.choice(difficulties) + if len(self.frontiers[c_name]) == 0 or config["difficulty_ranges"] is None: + valid_difficulties = self.valid_difficulties[c_name] + difficulty = random.choice(valid_difficulties) + else: + frontiers = self.frontiers[c_name] + difficulty_range = config["difficulty_ranges"][c_name] + idx1 = math.floor(difficulty_range[0] * (len(frontiers) - 1)) + idx2 = math.ceil(difficulty_range[1] * (len(frontiers) - 1)) + difficulties = [p for frontier in frontiers[idx1:idx2 + 1] for p in frontier] + difficulty = random.choice(difficulties) samples[c_name] = difficulty logger.debug(f"Sampled difficulty {difficulty} for challenge {c_name}") From 387d1d9f0df3f9ddb437f4231ccffe97868e2097 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:03:29 +0000 Subject: [PATCH 19/49] Compiled knapsack/classic_quadkp --- .../classic_quadkp/benchmarker_outbound.rs | 167 ++++++++++++++++++ .../src/knapsack/classic_quadkp/commercial.rs | 167 ++++++++++++++++++ .../src/knapsack/classic_quadkp/inbound.rs | 167 ++++++++++++++++++ .../classic_quadkp/innovator_outbound.rs | 167 ++++++++++++++++++ .../src/knapsack/classic_quadkp/mod.rs | 4 + .../src/knapsack/classic_quadkp/open_data.rs | 167 ++++++++++++++++++ tig-algorithms/src/knapsack/mod.rs | 3 +- tig-algorithms/src/knapsack/template.rs | 26 +-- .../wasm/knapsack/classic_quadkp.wasm | Bin 0 -> 162976 bytes 9 files changed, 843 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/knapsack/classic_quadkp/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/knapsack/classic_quadkp/commercial.rs create mode 100644 tig-algorithms/src/knapsack/classic_quadkp/inbound.rs create mode 100644 tig-algorithms/src/knapsack/classic_quadkp/innovator_outbound.rs create mode 100644 tig-algorithms/src/knapsack/classic_quadkp/mod.rs create mode 100644 tig-algorithms/src/knapsack/classic_quadkp/open_data.rs create mode 100644 tig-algorithms/wasm/knapsack/classic_quadkp.wasm diff --git a/tig-algorithms/src/knapsack/classic_quadkp/benchmarker_outbound.rs b/tig-algorithms/src/knapsack/classic_quadkp/benchmarker_outbound.rs new file mode 100644 index 0000000..4f65bef --- /dev/null +++ b/tig-algorithms/src/knapsack/classic_quadkp/benchmarker_outbound.rs @@ -0,0 +1,167 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Result; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut edge_costs: Vec<(usize, f32)> = (0..vertex_count) + .map(|flow_index| { + let total_flow = challenge.values[flow_index] as i32 + + challenge.interaction_values[flow_index].iter().sum::(); + let cost = total_flow as f32 / challenge.weights[flow_index] as f32; + (flow_index, cost) + }) + .collect(); + + edge_costs.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut coloring = Vec::with_capacity(vertex_count); + let mut uncolored = Vec::with_capacity(vertex_count); + let mut current_entropy = 0; + let mut current_temperature = 0; + + for &(flow_index, _) in &edge_costs { + if current_entropy + challenge.weights[flow_index] <= challenge.max_weight { + current_entropy += challenge.weights[flow_index]; + current_temperature += challenge.values[flow_index] as i32; + + for &colored in &coloring { + current_temperature += challenge.interaction_values[flow_index][colored]; + } + coloring.push(flow_index); + } else { + uncolored.push(flow_index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for flow_index in 0..vertex_count { + mutation_rates[flow_index] = challenge.values[flow_index] as i32; + for &colored in &coloring { + mutation_rates[flow_index] += challenge.interaction_values[flow_index][colored]; + } + } + + let max_generations = 100; + let mut cooling_schedule = vec![0; vertex_count]; + + for _ in 0..max_generations { + let mut best_mutation = 0; + let mut best_crossover = None; + + for uncolored_index in 0..uncolored.len() { + let mutant = uncolored[uncolored_index]; + if cooling_schedule[mutant] > 0 { + continue; + } + + unsafe { + let mutant_fitness = *mutation_rates.get_unchecked(mutant); + let min_entropy_reduction = *challenge.weights.get_unchecked(mutant) as i32 - (challenge.max_weight as i32 - current_entropy as i32); + + if mutant_fitness < 0 { + continue; + } + + for colored_index in 0..coloring.len() { + let gene_to_remove = *coloring.get_unchecked(colored_index); + if *cooling_schedule.get_unchecked(gene_to_remove) > 0 { + continue; + } + + if min_entropy_reduction > 0 { + let removed_entropy = *challenge.weights.get_unchecked(gene_to_remove) as i32; + if removed_entropy < min_entropy_reduction { + continue; + } + } + + let fitness_change = mutant_fitness - *mutation_rates.get_unchecked(gene_to_remove) + - *challenge.interaction_values.get_unchecked(mutant).get_unchecked(gene_to_remove); + + if fitness_change > best_mutation { + best_mutation = fitness_change; + best_crossover = Some((uncolored_index, colored_index)); + } + } + } + } + + if let Some((uncolored_index, colored_index)) = best_crossover { + let gene_to_add = uncolored[uncolored_index]; + let gene_to_remove = coloring[colored_index]; + + coloring.swap_remove(colored_index); + uncolored.swap_remove(uncolored_index); + coloring.push(gene_to_add); + uncolored.push(gene_to_remove); + + current_temperature += best_mutation; + current_entropy = current_entropy + challenge.weights[gene_to_add] - challenge.weights[gene_to_remove]; + + unsafe { + for flow_index in 0..vertex_count { + *mutation_rates.get_unchecked_mut(flow_index) += + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_add) - + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_remove); + } + } + + cooling_schedule[gene_to_add] = 3; + cooling_schedule[gene_to_remove] = 3; + } else { + break; + } + + if current_temperature as u32 >= challenge.min_value { + return Ok(Some(Solution { items: coloring })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + } + + if current_temperature as u32 >= challenge.min_value { + Ok(Some(Solution { items: coloring })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/classic_quadkp/commercial.rs b/tig-algorithms/src/knapsack/classic_quadkp/commercial.rs new file mode 100644 index 0000000..c8ff1e3 --- /dev/null +++ b/tig-algorithms/src/knapsack/classic_quadkp/commercial.rs @@ -0,0 +1,167 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Result; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut edge_costs: Vec<(usize, f32)> = (0..vertex_count) + .map(|flow_index| { + let total_flow = challenge.values[flow_index] as i32 + + challenge.interaction_values[flow_index].iter().sum::(); + let cost = total_flow as f32 / challenge.weights[flow_index] as f32; + (flow_index, cost) + }) + .collect(); + + edge_costs.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut coloring = Vec::with_capacity(vertex_count); + let mut uncolored = Vec::with_capacity(vertex_count); + let mut current_entropy = 0; + let mut current_temperature = 0; + + for &(flow_index, _) in &edge_costs { + if current_entropy + challenge.weights[flow_index] <= challenge.max_weight { + current_entropy += challenge.weights[flow_index]; + current_temperature += challenge.values[flow_index] as i32; + + for &colored in &coloring { + current_temperature += challenge.interaction_values[flow_index][colored]; + } + coloring.push(flow_index); + } else { + uncolored.push(flow_index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for flow_index in 0..vertex_count { + mutation_rates[flow_index] = challenge.values[flow_index] as i32; + for &colored in &coloring { + mutation_rates[flow_index] += challenge.interaction_values[flow_index][colored]; + } + } + + let max_generations = 100; + let mut cooling_schedule = vec![0; vertex_count]; + + for _ in 0..max_generations { + let mut best_mutation = 0; + let mut best_crossover = None; + + for uncolored_index in 0..uncolored.len() { + let mutant = uncolored[uncolored_index]; + if cooling_schedule[mutant] > 0 { + continue; + } + + unsafe { + let mutant_fitness = *mutation_rates.get_unchecked(mutant); + let min_entropy_reduction = *challenge.weights.get_unchecked(mutant) as i32 - (challenge.max_weight as i32 - current_entropy as i32); + + if mutant_fitness < 0 { + continue; + } + + for colored_index in 0..coloring.len() { + let gene_to_remove = *coloring.get_unchecked(colored_index); + if *cooling_schedule.get_unchecked(gene_to_remove) > 0 { + continue; + } + + if min_entropy_reduction > 0 { + let removed_entropy = *challenge.weights.get_unchecked(gene_to_remove) as i32; + if removed_entropy < min_entropy_reduction { + continue; + } + } + + let fitness_change = mutant_fitness - *mutation_rates.get_unchecked(gene_to_remove) + - *challenge.interaction_values.get_unchecked(mutant).get_unchecked(gene_to_remove); + + if fitness_change > best_mutation { + best_mutation = fitness_change; + best_crossover = Some((uncolored_index, colored_index)); + } + } + } + } + + if let Some((uncolored_index, colored_index)) = best_crossover { + let gene_to_add = uncolored[uncolored_index]; + let gene_to_remove = coloring[colored_index]; + + coloring.swap_remove(colored_index); + uncolored.swap_remove(uncolored_index); + coloring.push(gene_to_add); + uncolored.push(gene_to_remove); + + current_temperature += best_mutation; + current_entropy = current_entropy + challenge.weights[gene_to_add] - challenge.weights[gene_to_remove]; + + unsafe { + for flow_index in 0..vertex_count { + *mutation_rates.get_unchecked_mut(flow_index) += + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_add) - + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_remove); + } + } + + cooling_schedule[gene_to_add] = 3; + cooling_schedule[gene_to_remove] = 3; + } else { + break; + } + + if current_temperature as u32 >= challenge.min_value { + return Ok(Some(Solution { items: coloring })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + } + + if current_temperature as u32 >= challenge.min_value { + Ok(Some(Solution { items: coloring })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/classic_quadkp/inbound.rs b/tig-algorithms/src/knapsack/classic_quadkp/inbound.rs new file mode 100644 index 0000000..314acfb --- /dev/null +++ b/tig-algorithms/src/knapsack/classic_quadkp/inbound.rs @@ -0,0 +1,167 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Result; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut edge_costs: Vec<(usize, f32)> = (0..vertex_count) + .map(|flow_index| { + let total_flow = challenge.values[flow_index] as i32 + + challenge.interaction_values[flow_index].iter().sum::(); + let cost = total_flow as f32 / challenge.weights[flow_index] as f32; + (flow_index, cost) + }) + .collect(); + + edge_costs.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut coloring = Vec::with_capacity(vertex_count); + let mut uncolored = Vec::with_capacity(vertex_count); + let mut current_entropy = 0; + let mut current_temperature = 0; + + for &(flow_index, _) in &edge_costs { + if current_entropy + challenge.weights[flow_index] <= challenge.max_weight { + current_entropy += challenge.weights[flow_index]; + current_temperature += challenge.values[flow_index] as i32; + + for &colored in &coloring { + current_temperature += challenge.interaction_values[flow_index][colored]; + } + coloring.push(flow_index); + } else { + uncolored.push(flow_index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for flow_index in 0..vertex_count { + mutation_rates[flow_index] = challenge.values[flow_index] as i32; + for &colored in &coloring { + mutation_rates[flow_index] += challenge.interaction_values[flow_index][colored]; + } + } + + let max_generations = 100; + let mut cooling_schedule = vec![0; vertex_count]; + + for _ in 0..max_generations { + let mut best_mutation = 0; + let mut best_crossover = None; + + for uncolored_index in 0..uncolored.len() { + let mutant = uncolored[uncolored_index]; + if cooling_schedule[mutant] > 0 { + continue; + } + + unsafe { + let mutant_fitness = *mutation_rates.get_unchecked(mutant); + let min_entropy_reduction = *challenge.weights.get_unchecked(mutant) as i32 - (challenge.max_weight as i32 - current_entropy as i32); + + if mutant_fitness < 0 { + continue; + } + + for colored_index in 0..coloring.len() { + let gene_to_remove = *coloring.get_unchecked(colored_index); + if *cooling_schedule.get_unchecked(gene_to_remove) > 0 { + continue; + } + + if min_entropy_reduction > 0 { + let removed_entropy = *challenge.weights.get_unchecked(gene_to_remove) as i32; + if removed_entropy < min_entropy_reduction { + continue; + } + } + + let fitness_change = mutant_fitness - *mutation_rates.get_unchecked(gene_to_remove) + - *challenge.interaction_values.get_unchecked(mutant).get_unchecked(gene_to_remove); + + if fitness_change > best_mutation { + best_mutation = fitness_change; + best_crossover = Some((uncolored_index, colored_index)); + } + } + } + } + + if let Some((uncolored_index, colored_index)) = best_crossover { + let gene_to_add = uncolored[uncolored_index]; + let gene_to_remove = coloring[colored_index]; + + coloring.swap_remove(colored_index); + uncolored.swap_remove(uncolored_index); + coloring.push(gene_to_add); + uncolored.push(gene_to_remove); + + current_temperature += best_mutation; + current_entropy = current_entropy + challenge.weights[gene_to_add] - challenge.weights[gene_to_remove]; + + unsafe { + for flow_index in 0..vertex_count { + *mutation_rates.get_unchecked_mut(flow_index) += + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_add) - + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_remove); + } + } + + cooling_schedule[gene_to_add] = 3; + cooling_schedule[gene_to_remove] = 3; + } else { + break; + } + + if current_temperature as u32 >= challenge.min_value { + return Ok(Some(Solution { items: coloring })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + } + + if current_temperature as u32 >= challenge.min_value { + Ok(Some(Solution { items: coloring })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/classic_quadkp/innovator_outbound.rs b/tig-algorithms/src/knapsack/classic_quadkp/innovator_outbound.rs new file mode 100644 index 0000000..3c9de50 --- /dev/null +++ b/tig-algorithms/src/knapsack/classic_quadkp/innovator_outbound.rs @@ -0,0 +1,167 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Result; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut edge_costs: Vec<(usize, f32)> = (0..vertex_count) + .map(|flow_index| { + let total_flow = challenge.values[flow_index] as i32 + + challenge.interaction_values[flow_index].iter().sum::(); + let cost = total_flow as f32 / challenge.weights[flow_index] as f32; + (flow_index, cost) + }) + .collect(); + + edge_costs.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut coloring = Vec::with_capacity(vertex_count); + let mut uncolored = Vec::with_capacity(vertex_count); + let mut current_entropy = 0; + let mut current_temperature = 0; + + for &(flow_index, _) in &edge_costs { + if current_entropy + challenge.weights[flow_index] <= challenge.max_weight { + current_entropy += challenge.weights[flow_index]; + current_temperature += challenge.values[flow_index] as i32; + + for &colored in &coloring { + current_temperature += challenge.interaction_values[flow_index][colored]; + } + coloring.push(flow_index); + } else { + uncolored.push(flow_index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for flow_index in 0..vertex_count { + mutation_rates[flow_index] = challenge.values[flow_index] as i32; + for &colored in &coloring { + mutation_rates[flow_index] += challenge.interaction_values[flow_index][colored]; + } + } + + let max_generations = 100; + let mut cooling_schedule = vec![0; vertex_count]; + + for _ in 0..max_generations { + let mut best_mutation = 0; + let mut best_crossover = None; + + for uncolored_index in 0..uncolored.len() { + let mutant = uncolored[uncolored_index]; + if cooling_schedule[mutant] > 0 { + continue; + } + + unsafe { + let mutant_fitness = *mutation_rates.get_unchecked(mutant); + let min_entropy_reduction = *challenge.weights.get_unchecked(mutant) as i32 - (challenge.max_weight as i32 - current_entropy as i32); + + if mutant_fitness < 0 { + continue; + } + + for colored_index in 0..coloring.len() { + let gene_to_remove = *coloring.get_unchecked(colored_index); + if *cooling_schedule.get_unchecked(gene_to_remove) > 0 { + continue; + } + + if min_entropy_reduction > 0 { + let removed_entropy = *challenge.weights.get_unchecked(gene_to_remove) as i32; + if removed_entropy < min_entropy_reduction { + continue; + } + } + + let fitness_change = mutant_fitness - *mutation_rates.get_unchecked(gene_to_remove) + - *challenge.interaction_values.get_unchecked(mutant).get_unchecked(gene_to_remove); + + if fitness_change > best_mutation { + best_mutation = fitness_change; + best_crossover = Some((uncolored_index, colored_index)); + } + } + } + } + + if let Some((uncolored_index, colored_index)) = best_crossover { + let gene_to_add = uncolored[uncolored_index]; + let gene_to_remove = coloring[colored_index]; + + coloring.swap_remove(colored_index); + uncolored.swap_remove(uncolored_index); + coloring.push(gene_to_add); + uncolored.push(gene_to_remove); + + current_temperature += best_mutation; + current_entropy = current_entropy + challenge.weights[gene_to_add] - challenge.weights[gene_to_remove]; + + unsafe { + for flow_index in 0..vertex_count { + *mutation_rates.get_unchecked_mut(flow_index) += + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_add) - + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_remove); + } + } + + cooling_schedule[gene_to_add] = 3; + cooling_schedule[gene_to_remove] = 3; + } else { + break; + } + + if current_temperature as u32 >= challenge.min_value { + return Ok(Some(Solution { items: coloring })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + } + + if current_temperature as u32 >= challenge.min_value { + Ok(Some(Solution { items: coloring })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/classic_quadkp/mod.rs b/tig-algorithms/src/knapsack/classic_quadkp/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/knapsack/classic_quadkp/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/classic_quadkp/open_data.rs b/tig-algorithms/src/knapsack/classic_quadkp/open_data.rs new file mode 100644 index 0000000..5c7bc34 --- /dev/null +++ b/tig-algorithms/src/knapsack/classic_quadkp/open_data.rs @@ -0,0 +1,167 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Result; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut edge_costs: Vec<(usize, f32)> = (0..vertex_count) + .map(|flow_index| { + let total_flow = challenge.values[flow_index] as i32 + + challenge.interaction_values[flow_index].iter().sum::(); + let cost = total_flow as f32 / challenge.weights[flow_index] as f32; + (flow_index, cost) + }) + .collect(); + + edge_costs.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut coloring = Vec::with_capacity(vertex_count); + let mut uncolored = Vec::with_capacity(vertex_count); + let mut current_entropy = 0; + let mut current_temperature = 0; + + for &(flow_index, _) in &edge_costs { + if current_entropy + challenge.weights[flow_index] <= challenge.max_weight { + current_entropy += challenge.weights[flow_index]; + current_temperature += challenge.values[flow_index] as i32; + + for &colored in &coloring { + current_temperature += challenge.interaction_values[flow_index][colored]; + } + coloring.push(flow_index); + } else { + uncolored.push(flow_index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for flow_index in 0..vertex_count { + mutation_rates[flow_index] = challenge.values[flow_index] as i32; + for &colored in &coloring { + mutation_rates[flow_index] += challenge.interaction_values[flow_index][colored]; + } + } + + let max_generations = 100; + let mut cooling_schedule = vec![0; vertex_count]; + + for _ in 0..max_generations { + let mut best_mutation = 0; + let mut best_crossover = None; + + for uncolored_index in 0..uncolored.len() { + let mutant = uncolored[uncolored_index]; + if cooling_schedule[mutant] > 0 { + continue; + } + + unsafe { + let mutant_fitness = *mutation_rates.get_unchecked(mutant); + let min_entropy_reduction = *challenge.weights.get_unchecked(mutant) as i32 - (challenge.max_weight as i32 - current_entropy as i32); + + if mutant_fitness < 0 { + continue; + } + + for colored_index in 0..coloring.len() { + let gene_to_remove = *coloring.get_unchecked(colored_index); + if *cooling_schedule.get_unchecked(gene_to_remove) > 0 { + continue; + } + + if min_entropy_reduction > 0 { + let removed_entropy = *challenge.weights.get_unchecked(gene_to_remove) as i32; + if removed_entropy < min_entropy_reduction { + continue; + } + } + + let fitness_change = mutant_fitness - *mutation_rates.get_unchecked(gene_to_remove) + - *challenge.interaction_values.get_unchecked(mutant).get_unchecked(gene_to_remove); + + if fitness_change > best_mutation { + best_mutation = fitness_change; + best_crossover = Some((uncolored_index, colored_index)); + } + } + } + } + + if let Some((uncolored_index, colored_index)) = best_crossover { + let gene_to_add = uncolored[uncolored_index]; + let gene_to_remove = coloring[colored_index]; + + coloring.swap_remove(colored_index); + uncolored.swap_remove(uncolored_index); + coloring.push(gene_to_add); + uncolored.push(gene_to_remove); + + current_temperature += best_mutation; + current_entropy = current_entropy + challenge.weights[gene_to_add] - challenge.weights[gene_to_remove]; + + unsafe { + for flow_index in 0..vertex_count { + *mutation_rates.get_unchecked_mut(flow_index) += + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_add) - + challenge.interaction_values.get_unchecked(flow_index).get_unchecked(gene_to_remove); + } + } + + cooling_schedule[gene_to_add] = 3; + cooling_schedule[gene_to_remove] = 3; + } else { + break; + } + + if current_temperature as u32 >= challenge.min_value { + return Ok(Some(Solution { items: coloring })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + } + + if current_temperature as u32 >= challenge.min_value { + Ok(Some(Solution { items: coloring })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/mod.rs b/tig-algorithms/src/knapsack/mod.rs index 33ddf59..1f8cd19 100644 --- a/tig-algorithms/src/knapsack/mod.rs +++ b/tig-algorithms/src/knapsack/mod.rs @@ -98,7 +98,8 @@ // c003_a050 -// c003_a051 +pub mod classic_quadkp; +pub use classic_quadkp as c003_a051; // c003_a052 diff --git a/tig-algorithms/src/knapsack/template.rs b/tig-algorithms/src/knapsack/template.rs index 6386cbf..dab7384 100644 --- a/tig-algorithms/src/knapsack/template.rs +++ b/tig-algorithms/src/knapsack/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::knapsack::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/knapsack/classic_quadkp.wasm b/tig-algorithms/wasm/knapsack/classic_quadkp.wasm new file mode 100644 index 0000000000000000000000000000000000000000..8fcd958c63ed49e2f81fc3a6f808707b670438a6 GIT binary patch literal 162976 zcmeFa3z%NVS?75!-~H14wbZs{wQSWnYR9ceiWP7q$2N(oXM9m)W0QFOgzU^>SXe^0 z329j?!lWg&E!il+3?A^z1TaKNOypT2aKK@*j6+(%3;|5=>@u6oV2DFJ;3ov|f_KI& zA==;nt#iIhUo1NVWcJxrKl;u&Rdwp}*89Ho-l|UY?RR}|97R$5Ks>4-9?rf-BBTrhgb!wy63^tc&^zq2~kw!sg|R9_o&rX6d<)3iXJ_BG`gc6?p0&u zXed}Y#*LaJKmFJ7$Kte7>7+>;KgqH4=%ugD})oQiUs9WVEL?l-$X`&*o^ow!(yWVl=;17J;JHPLH-f<{;EN=djIP!7(+q0$k?)a)Z?}@t`dly{# zE!+Ol_uhWz*oJ!hf`9N2Z~O1=_|AXuw*P+5RU1#l-TUHu;}6EQ3-?_0kEdSuKP+4{ zzUkoR%P#z;H@xxM>u&z$TfY6^v>0Ni_iA#L-mMI_4k5%KOnpUE=i)WH3*N^TRc{K0Sn=AWV>>|&U 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znnYUd_11f`Q&X`%QnjyK23J+8!_mP1(W;d9;XNZsn#!iIt5kA@9q;YhvQ(AIbnMIR zYP|Vdo?%m_Wvb*hR$h_BWZj81Bk`25w34;q)jZY{Huz4wPC=41rOM$d$Fi7%JC^$# SwJE&78r Date: Thu, 16 Jan 2025 21:03:33 +0000 Subject: [PATCH 20/49] Compiled knapsack/dynamic --- .../knapsack/dynamic/benchmarker_outbound.rs | 95 ++++++++++++++++++ .../src/knapsack/dynamic/commercial.rs | 95 ++++++++++++++++++ .../src/knapsack/dynamic/inbound.rs | 95 ++++++++++++++++++ .../knapsack/dynamic/innovator_outbound.rs | 95 ++++++++++++++++++ tig-algorithms/src/knapsack/dynamic/mod.rs | 4 + .../src/knapsack/dynamic/open_data.rs | 95 ++++++++++++++++++ tig-algorithms/src/knapsack/mod.rs | 3 +- tig-algorithms/src/knapsack/template.rs | 26 +---- tig-algorithms/wasm/knapsack/dynamic.wasm | Bin 0 -> 155901 bytes 9 files changed, 483 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/knapsack/dynamic/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/knapsack/dynamic/commercial.rs create mode 100644 tig-algorithms/src/knapsack/dynamic/inbound.rs create mode 100644 tig-algorithms/src/knapsack/dynamic/innovator_outbound.rs create mode 100644 tig-algorithms/src/knapsack/dynamic/mod.rs create mode 100644 tig-algorithms/src/knapsack/dynamic/open_data.rs create mode 100644 tig-algorithms/wasm/knapsack/dynamic.wasm diff --git a/tig-algorithms/src/knapsack/dynamic/benchmarker_outbound.rs b/tig-algorithms/src/knapsack/dynamic/benchmarker_outbound.rs new file mode 100644 index 0000000..f6b1570 --- /dev/null +++ b/tig-algorithms/src/knapsack/dynamic/benchmarker_outbound.rs @@ -0,0 +1,95 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight; + let min_value = challenge.min_value; + let num_items = challenge.difficulty.num_items; + + // Sort items by value-to-weight ratio in descending order + let mut sorted_items: Vec = (0..num_items).collect(); + sorted_items.sort_by(|&a, &b| { + let ratio_a = challenge.values[a] as f64 / challenge.weights[a] as f64; + let ratio_b = challenge.values[b] as f64 / challenge.weights[b] as f64; + ratio_b.partial_cmp(&ratio_a).unwrap() + }); + + // Initialize combinations with a single empty combo + let mut combinations: Vec<(Vec, u32, u32)> = vec![(vec![false; num_items], 0, 0)]; + + let mut items = Vec::new(); + for &item in &sorted_items { + // Create new combos with the current item + let mut new_combinations: Vec<(Vec, u32, u32)> = combinations + .iter() + .map(|(combo, value, weight)| { + let mut new_combo = combo.clone(); + new_combo[item] = true; + ( + new_combo, + value + challenge.values[item], + weight + challenge.weights[item], + ) + }) + .filter(|&(_, _, weight)| weight <= max_weight) // Keep only combos within weight limit + .collect(); + + // Check if any new combination meets the minimum value requirement + if let Some((combo, _, _)) = new_combinations + .iter() + .find(|&&(_, value, _)| value >= min_value) + { + items = combo + .iter() + .enumerate() + .filter_map(|(i, &included)| if included { Some(i) } else { None }) + .collect(); + break; + } + + // Merge new_combinations with existing combinations + combinations.append(&mut new_combinations); + + // Deduplicate combinations by keeping the highest value for each weight + combinations.sort_by(|a, b| a.2.cmp(&b.2).then_with(|| b.1.cmp(&a.1))); // Sort by weight, then by value + combinations.dedup_by(|a, b| a.2 == b.2 && a.1 <= b.1); // Deduplicate by weight, keeping highest value + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/dynamic/commercial.rs b/tig-algorithms/src/knapsack/dynamic/commercial.rs new file mode 100644 index 0000000..5814371 --- /dev/null +++ b/tig-algorithms/src/knapsack/dynamic/commercial.rs @@ -0,0 +1,95 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight; + let min_value = challenge.min_value; + let num_items = challenge.difficulty.num_items; + + // Sort items by value-to-weight ratio in descending order + let mut sorted_items: Vec = (0..num_items).collect(); + sorted_items.sort_by(|&a, &b| { + let ratio_a = challenge.values[a] as f64 / challenge.weights[a] as f64; + let ratio_b = challenge.values[b] as f64 / challenge.weights[b] as f64; + ratio_b.partial_cmp(&ratio_a).unwrap() + }); + + // Initialize combinations with a single empty combo + let mut combinations: Vec<(Vec, u32, u32)> = vec![(vec![false; num_items], 0, 0)]; + + let mut items = Vec::new(); + for &item in &sorted_items { + // Create new combos with the current item + let mut new_combinations: Vec<(Vec, u32, u32)> = combinations + .iter() + .map(|(combo, value, weight)| { + let mut new_combo = combo.clone(); + new_combo[item] = true; + ( + new_combo, + value + challenge.values[item], + weight + challenge.weights[item], + ) + }) + .filter(|&(_, _, weight)| weight <= max_weight) // Keep only combos within weight limit + .collect(); + + // Check if any new combination meets the minimum value requirement + if let Some((combo, _, _)) = new_combinations + .iter() + .find(|&&(_, value, _)| value >= min_value) + { + items = combo + .iter() + .enumerate() + .filter_map(|(i, &included)| if included { Some(i) } else { None }) + .collect(); + break; + } + + // Merge new_combinations with existing combinations + combinations.append(&mut new_combinations); + + // Deduplicate combinations by keeping the highest value for each weight + combinations.sort_by(|a, b| a.2.cmp(&b.2).then_with(|| b.1.cmp(&a.1))); // Sort by weight, then by value + combinations.dedup_by(|a, b| a.2 == b.2 && a.1 <= b.1); // Deduplicate by weight, keeping highest value + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/dynamic/inbound.rs b/tig-algorithms/src/knapsack/dynamic/inbound.rs new file mode 100644 index 0000000..216e1c9 --- /dev/null +++ b/tig-algorithms/src/knapsack/dynamic/inbound.rs @@ -0,0 +1,95 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight; + let min_value = challenge.min_value; + let num_items = challenge.difficulty.num_items; + + // Sort items by value-to-weight ratio in descending order + let mut sorted_items: Vec = (0..num_items).collect(); + sorted_items.sort_by(|&a, &b| { + let ratio_a = challenge.values[a] as f64 / challenge.weights[a] as f64; + let ratio_b = challenge.values[b] as f64 / challenge.weights[b] as f64; + ratio_b.partial_cmp(&ratio_a).unwrap() + }); + + // Initialize combinations with a single empty combo + let mut combinations: Vec<(Vec, u32, u32)> = vec![(vec![false; num_items], 0, 0)]; + + let mut items = Vec::new(); + for &item in &sorted_items { + // Create new combos with the current item + let mut new_combinations: Vec<(Vec, u32, u32)> = combinations + .iter() + .map(|(combo, value, weight)| { + let mut new_combo = combo.clone(); + new_combo[item] = true; + ( + new_combo, + value + challenge.values[item], + weight + challenge.weights[item], + ) + }) + .filter(|&(_, _, weight)| weight <= max_weight) // Keep only combos within weight limit + .collect(); + + // Check if any new combination meets the minimum value requirement + if let Some((combo, _, _)) = new_combinations + .iter() + .find(|&&(_, value, _)| value >= min_value) + { + items = combo + .iter() + .enumerate() + .filter_map(|(i, &included)| if included { Some(i) } else { None }) + .collect(); + break; + } + + // Merge new_combinations with existing combinations + combinations.append(&mut new_combinations); + + // Deduplicate combinations by keeping the highest value for each weight + combinations.sort_by(|a, b| a.2.cmp(&b.2).then_with(|| b.1.cmp(&a.1))); // Sort by weight, then by value + combinations.dedup_by(|a, b| a.2 == b.2 && a.1 <= b.1); // Deduplicate by weight, keeping highest value + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/dynamic/innovator_outbound.rs b/tig-algorithms/src/knapsack/dynamic/innovator_outbound.rs new file mode 100644 index 0000000..9889e0a --- /dev/null +++ b/tig-algorithms/src/knapsack/dynamic/innovator_outbound.rs @@ -0,0 +1,95 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight; + let min_value = challenge.min_value; + let num_items = challenge.difficulty.num_items; + + // Sort items by value-to-weight ratio in descending order + let mut sorted_items: Vec = (0..num_items).collect(); + sorted_items.sort_by(|&a, &b| { + let ratio_a = challenge.values[a] as f64 / challenge.weights[a] as f64; + let ratio_b = challenge.values[b] as f64 / challenge.weights[b] as f64; + ratio_b.partial_cmp(&ratio_a).unwrap() + }); + + // Initialize combinations with a single empty combo + let mut combinations: Vec<(Vec, u32, u32)> = vec![(vec![false; num_items], 0, 0)]; + + let mut items = Vec::new(); + for &item in &sorted_items { + // Create new combos with the current item + let mut new_combinations: Vec<(Vec, u32, u32)> = combinations + .iter() + .map(|(combo, value, weight)| { + let mut new_combo = combo.clone(); + new_combo[item] = true; + ( + new_combo, + value + challenge.values[item], + weight + challenge.weights[item], + ) + }) + .filter(|&(_, _, weight)| weight <= max_weight) // Keep only combos within weight limit + .collect(); + + // Check if any new combination meets the minimum value requirement + if let Some((combo, _, _)) = new_combinations + .iter() + .find(|&&(_, value, _)| value >= min_value) + { + items = combo + .iter() + .enumerate() + .filter_map(|(i, &included)| if included { Some(i) } else { None }) + .collect(); + break; + } + + // Merge new_combinations with existing combinations + combinations.append(&mut new_combinations); + + // Deduplicate combinations by keeping the highest value for each weight + combinations.sort_by(|a, b| a.2.cmp(&b.2).then_with(|| b.1.cmp(&a.1))); // Sort by weight, then by value + combinations.dedup_by(|a, b| a.2 == b.2 && a.1 <= b.1); // Deduplicate by weight, keeping highest value + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/dynamic/mod.rs b/tig-algorithms/src/knapsack/dynamic/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/knapsack/dynamic/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/dynamic/open_data.rs b/tig-algorithms/src/knapsack/dynamic/open_data.rs new file mode 100644 index 0000000..9098047 --- /dev/null +++ b/tig-algorithms/src/knapsack/dynamic/open_data.rs @@ -0,0 +1,95 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight; + let min_value = challenge.min_value; + let num_items = challenge.difficulty.num_items; + + // Sort items by value-to-weight ratio in descending order + let mut sorted_items: Vec = (0..num_items).collect(); + sorted_items.sort_by(|&a, &b| { + let ratio_a = challenge.values[a] as f64 / challenge.weights[a] as f64; + let ratio_b = challenge.values[b] as f64 / challenge.weights[b] as f64; + ratio_b.partial_cmp(&ratio_a).unwrap() + }); + + // Initialize combinations with a single empty combo + let mut combinations: Vec<(Vec, u32, u32)> = vec![(vec![false; num_items], 0, 0)]; + + let mut items = Vec::new(); + for &item in &sorted_items { + // Create new combos with the current item + let mut new_combinations: Vec<(Vec, u32, u32)> = combinations + .iter() + .map(|(combo, value, weight)| { + let mut new_combo = combo.clone(); + new_combo[item] = true; + ( + new_combo, + value + challenge.values[item], + weight + challenge.weights[item], + ) + }) + .filter(|&(_, _, weight)| weight <= max_weight) // Keep only combos within weight limit + .collect(); + + // Check if any new combination meets the minimum value requirement + if let Some((combo, _, _)) = new_combinations + .iter() + .find(|&&(_, value, _)| value >= min_value) + { + items = combo + .iter() + .enumerate() + .filter_map(|(i, &included)| if included { Some(i) } else { None }) + .collect(); + break; + } + + // Merge new_combinations with existing combinations + combinations.append(&mut new_combinations); + + // Deduplicate combinations by keeping the highest value for each weight + combinations.sort_by(|a, b| a.2.cmp(&b.2).then_with(|| b.1.cmp(&a.1))); // Sort by weight, then by value + combinations.dedup_by(|a, b| a.2 == b.2 && a.1 <= b.1); // Deduplicate by weight, keeping highest value + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/mod.rs b/tig-algorithms/src/knapsack/mod.rs index 33ddf59..384121e 100644 --- a/tig-algorithms/src/knapsack/mod.rs +++ b/tig-algorithms/src/knapsack/mod.rs @@ -1,4 +1,5 @@ -// c003_a001 +pub mod dynamic; +pub use dynamic as c003_a001; // c003_a002 diff --git a/tig-algorithms/src/knapsack/template.rs b/tig-algorithms/src/knapsack/template.rs index 6386cbf..dab7384 100644 --- a/tig-algorithms/src/knapsack/template.rs +++ b/tig-algorithms/src/knapsack/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::knapsack::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/knapsack/dynamic.wasm b/tig-algorithms/wasm/knapsack/dynamic.wasm new file mode 100644 index 0000000000000000000000000000000000000000..f40eead5177b5c142e9939efc4b6a7e317fde8bd GIT binary patch literal 155901 zcmeFa3!I+UUElfK-uwH`yd#Yr*&}Dp^GLQwmK`gQ$c_^douR%cjuR7HZ%Dg~X<-R7 zCXHlOgk~g-BiSg40T)c`fFVjSk!hmEAr6}bhm2Cw1~9?935@|44DnJIXx2bzy^BHp z{{H8A-pgDpJAr0DyVZR3KDYCn%m4iE|Nl8p^c}Z-ZyZHYe0My3OM2wUk@(0hiT%b$ zBArEcH@YR`85gk%)OD__+SrjNR*GZ9yNC7eVXLcrfd?&n;aoLlheN@X zCu)n!DE&nI@i@)0Zkoh#9L2qfERI{8w&TS9rAb;#;(9$!_18$UtkdLwQqO8>lNZyJ zx3XHqZ`{fnyqRWkGTG2|9A|y~>Xf_uq;XuU)v|Uo+>FzR+qEoBD8jSwuaWXE;eVW^ zQyk;CPUq5y13--Gn>N>JF@8sU%PloppQ^V2m|tFAi8_rut$*)#eeXZn|NTjneb3v! z=Rowaxbt0aKd}G%zx^HmA_?tFW5`()$2@%!REx7`(=lYiSq|M`!czVw^E>z(Isn)&_< 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zqqWK9Y^+Z+iuF#P-D!7OZ3E@u_f9J#DhhoAtPH?#TG?MQ*Ak>2bAs9ZA`dZj{>2f*l%f<+fzyBtHC2|G?TVH)#4oBq|m literal 0 HcmV?d00001 From e7f98cd403399d8d07ec8a35cdd3a0a777af57d6 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:04:03 +0000 Subject: [PATCH 21/49] Compiled knapsack/knapheudp --- .../knapheudp/benchmarker_outbound.rs | 110 ++++++++++++++++++ .../src/knapsack/knapheudp/commercial.rs | 110 ++++++++++++++++++ .../src/knapsack/knapheudp/inbound.rs | 110 ++++++++++++++++++ .../knapsack/knapheudp/innovator_outbound.rs | 110 ++++++++++++++++++ tig-algorithms/src/knapsack/knapheudp/mod.rs | 4 + .../src/knapsack/knapheudp/open_data.rs | 110 ++++++++++++++++++ tig-algorithms/src/knapsack/mod.rs | 3 +- tig-algorithms/src/knapsack/template.rs | 26 +---- tig-algorithms/wasm/knapsack/knapheudp.wasm | Bin 0 -> 156587 bytes 9 files changed, 558 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/knapsack/knapheudp/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/knapsack/knapheudp/commercial.rs create mode 100644 tig-algorithms/src/knapsack/knapheudp/inbound.rs create mode 100644 tig-algorithms/src/knapsack/knapheudp/innovator_outbound.rs create mode 100644 tig-algorithms/src/knapsack/knapheudp/mod.rs create mode 100644 tig-algorithms/src/knapsack/knapheudp/open_data.rs create mode 100644 tig-algorithms/wasm/knapsack/knapheudp.wasm diff --git a/tig-algorithms/src/knapsack/knapheudp/benchmarker_outbound.rs b/tig-algorithms/src/knapsack/knapheudp/benchmarker_outbound.rs new file mode 100644 index 0000000..863a240 --- /dev/null +++ b/tig-algorithms/src/knapsack/knapheudp/benchmarker_outbound.rs @@ -0,0 +1,110 @@ +/*! +Copyright 2024 AllFather + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let weights: Vec = challenge.weights.iter().map(|&w| w as usize).collect(); + let values: Vec = challenge.values.iter().map(|&v| v as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let mut dp = vec![0; max_weight + 1]; + let mut selected = vec![vec![false; max_weight + 1]; num_items]; + + for (i, &(item_index, _)) in sorted_items.iter().enumerate() { + let weight = weights[item_index]; + let value = values[item_index]; + + for w in (weight..=max_weight).rev() { + let new_value = dp[w - weight] + value; + if new_value > dp[w] { + dp[w] = new_value; + selected[i][w] = true; + } + } + + if dp[max_weight] >= min_value { + break; + } + } + + if dp[max_weight] < min_value { + return Ok(None); + } + + let mut items = Vec::new(); + let mut w = max_weight; + for i in (0..num_items).rev() { + if selected[i][w] { + let item_index = sorted_items[i].0; + items.push(item_index); + w -= weights[item_index]; + } + if w == 0 { + break; + } + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/knapheudp/commercial.rs b/tig-algorithms/src/knapsack/knapheudp/commercial.rs new file mode 100644 index 0000000..49af535 --- /dev/null +++ b/tig-algorithms/src/knapsack/knapheudp/commercial.rs @@ -0,0 +1,110 @@ +/*! +Copyright 2024 AllFather + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let weights: Vec = challenge.weights.iter().map(|&w| w as usize).collect(); + let values: Vec = challenge.values.iter().map(|&v| v as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let mut dp = vec![0; max_weight + 1]; + let mut selected = vec![vec![false; max_weight + 1]; num_items]; + + for (i, &(item_index, _)) in sorted_items.iter().enumerate() { + let weight = weights[item_index]; + let value = values[item_index]; + + for w in (weight..=max_weight).rev() { + let new_value = dp[w - weight] + value; + if new_value > dp[w] { + dp[w] = new_value; + selected[i][w] = true; + } + } + + if dp[max_weight] >= min_value { + break; + } + } + + if dp[max_weight] < min_value { + return Ok(None); + } + + let mut items = Vec::new(); + let mut w = max_weight; + for i in (0..num_items).rev() { + if selected[i][w] { + let item_index = sorted_items[i].0; + items.push(item_index); + w -= weights[item_index]; + } + if w == 0 { + break; + } + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/knapheudp/inbound.rs b/tig-algorithms/src/knapsack/knapheudp/inbound.rs new file mode 100644 index 0000000..7a516c2 --- /dev/null +++ b/tig-algorithms/src/knapsack/knapheudp/inbound.rs @@ -0,0 +1,110 @@ +/*! +Copyright 2024 AllFather + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let weights: Vec = challenge.weights.iter().map(|&w| w as usize).collect(); + let values: Vec = challenge.values.iter().map(|&v| v as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let mut dp = vec![0; max_weight + 1]; + let mut selected = vec![vec![false; max_weight + 1]; num_items]; + + for (i, &(item_index, _)) in sorted_items.iter().enumerate() { + let weight = weights[item_index]; + let value = values[item_index]; + + for w in (weight..=max_weight).rev() { + let new_value = dp[w - weight] + value; + if new_value > dp[w] { + dp[w] = new_value; + selected[i][w] = true; + } + } + + if dp[max_weight] >= min_value { + break; + } + } + + if dp[max_weight] < min_value { + return Ok(None); + } + + let mut items = Vec::new(); + let mut w = max_weight; + for i in (0..num_items).rev() { + if selected[i][w] { + let item_index = sorted_items[i].0; + items.push(item_index); + w -= weights[item_index]; + } + if w == 0 { + break; + } + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/knapheudp/innovator_outbound.rs b/tig-algorithms/src/knapsack/knapheudp/innovator_outbound.rs new file mode 100644 index 0000000..927bd2b --- /dev/null +++ b/tig-algorithms/src/knapsack/knapheudp/innovator_outbound.rs @@ -0,0 +1,110 @@ +/*! +Copyright 2024 AllFather + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let weights: Vec = challenge.weights.iter().map(|&w| w as usize).collect(); + let values: Vec = challenge.values.iter().map(|&v| v as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let mut dp = vec![0; max_weight + 1]; + let mut selected = vec![vec![false; max_weight + 1]; num_items]; + + for (i, &(item_index, _)) in sorted_items.iter().enumerate() { + let weight = weights[item_index]; + let value = values[item_index]; + + for w in (weight..=max_weight).rev() { + let new_value = dp[w - weight] + value; + if new_value > dp[w] { + dp[w] = new_value; + selected[i][w] = true; + } + } + + if dp[max_weight] >= min_value { + break; + } + } + + if dp[max_weight] < min_value { + return Ok(None); + } + + let mut items = Vec::new(); + let mut w = max_weight; + for i in (0..num_items).rev() { + if selected[i][w] { + let item_index = sorted_items[i].0; + items.push(item_index); + w -= weights[item_index]; + } + if w == 0 { + break; + } + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/knapheudp/mod.rs b/tig-algorithms/src/knapsack/knapheudp/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/knapsack/knapheudp/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/knapheudp/open_data.rs b/tig-algorithms/src/knapsack/knapheudp/open_data.rs new file mode 100644 index 0000000..f9a8ce3 --- /dev/null +++ b/tig-algorithms/src/knapsack/knapheudp/open_data.rs @@ -0,0 +1,110 @@ +/*! +Copyright 2024 AllFather + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let weights: Vec = challenge.weights.iter().map(|&w| w as usize).collect(); + let values: Vec = challenge.values.iter().map(|&v| v as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let mut dp = vec![0; max_weight + 1]; + let mut selected = vec![vec![false; max_weight + 1]; num_items]; + + for (i, &(item_index, _)) in sorted_items.iter().enumerate() { + let weight = weights[item_index]; + let value = values[item_index]; + + for w in (weight..=max_weight).rev() { + let new_value = dp[w - weight] + value; + if new_value > dp[w] { + dp[w] = new_value; + selected[i][w] = true; + } + } + + if dp[max_weight] >= min_value { + break; + } + } + + if dp[max_weight] < min_value { + return Ok(None); + } + + let mut items = Vec::new(); + let mut w = max_weight; + for i in (0..num_items).rev() { + if selected[i][w] { + let item_index = sorted_items[i].0; + items.push(item_index); + w -= weights[item_index]; + } + if w == 0 { + break; + } + } + + Ok(Some(Solution { items })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/mod.rs b/tig-algorithms/src/knapsack/mod.rs index 33ddf59..83e4ad1 100644 --- a/tig-algorithms/src/knapsack/mod.rs +++ b/tig-algorithms/src/knapsack/mod.rs @@ -34,7 +34,8 @@ // c003_a018 -// c003_a019 +pub mod knapheudp; +pub use knapheudp as c003_a019; // c003_a020 diff --git a/tig-algorithms/src/knapsack/template.rs b/tig-algorithms/src/knapsack/template.rs index 6386cbf..dab7384 100644 --- a/tig-algorithms/src/knapsack/template.rs +++ b/tig-algorithms/src/knapsack/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::knapsack::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/knapsack/knapheudp.wasm b/tig-algorithms/wasm/knapsack/knapheudp.wasm new file mode 100644 index 0000000000000000000000000000000000000000..d918cce9e26ea0dad4dc9c82eba31dace5e6d5ae GIT binary patch literal 156587 zcmeFa3!GoaRp)tM|M&mj{g>3XWw-3EdnMZ~%ZgYP(`qj%i>y>S#p@$q=-j`Z;1!|~xe z68jq;j&v1SX>>=%Gj3v4sOwsHwXt8GSS@}d-aVvu4_RB~3p{Aq8;1`aIuzZ}2-j*c zI}|FWJW*fVM(HQxkH=}2b<-q{<0$S;WO3Z$vK=S(Uz((~B#G0h#xbyAJOD7x0;`hbhy!ih3yyCyT z{2lxL!QzIA_NMc0-t+eV?z_I{=5N2_wckAXgK_ir`{NJ9C*t~rul?rlxa9ipJaEy* z>F>LE`-OA=)qm~(N&LpQe(P=j!#ltGUGsPE|Ht3|-X(u;eBt-M_n*eg@jdYm#7E*E zXU2ax{*m}c;~$G3i0_L(7y+&mO7ZH@es$wHBCi;~@ge}@NG z!?N8jnoatQb9wnacNdATIsV@>?c;@sTAb9D*}U$G%#>CW^Wfp^=;_JEQ0Zy*%1nRy9`}Z7wtdOdovsbTk`9 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++++++++++++++++++ .../src/knapsack/knapmaxxing/commercial.rs | 115 ++++++++++++++++++ .../src/knapsack/knapmaxxing/inbound.rs | 115 ++++++++++++++++++ .../knapmaxxing/innovator_outbound.rs | 115 ++++++++++++++++++ .../src/knapsack/knapmaxxing/mod.rs | 4 + .../src/knapsack/knapmaxxing/open_data.rs | 115 ++++++++++++++++++ tig-algorithms/src/knapsack/mod.rs | 3 +- tig-algorithms/src/knapsack/template.rs | 26 +--- tig-algorithms/wasm/knapsack/knapmaxxing.wasm | Bin 0 -> 156011 bytes 9 files changed, 583 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/knapsack/knapmaxxing/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/knapsack/knapmaxxing/commercial.rs create mode 100644 tig-algorithms/src/knapsack/knapmaxxing/inbound.rs create mode 100644 tig-algorithms/src/knapsack/knapmaxxing/innovator_outbound.rs create mode 100644 tig-algorithms/src/knapsack/knapmaxxing/mod.rs create mode 100644 tig-algorithms/src/knapsack/knapmaxxing/open_data.rs create mode 100644 tig-algorithms/wasm/knapsack/knapmaxxing.wasm diff --git a/tig-algorithms/src/knapsack/knapmaxxing/benchmarker_outbound.rs b/tig-algorithms/src/knapsack/knapmaxxing/benchmarker_outbound.rs new file mode 100644 index 0000000..86f9f7f --- /dev/null +++ b/tig-algorithms/src/knapsack/knapmaxxing/benchmarker_outbound.rs @@ -0,0 +1,115 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let max_weight_plus_one = max_weight + 1; + + let weights: Vec = challenge.weights.iter().map(|weight| *weight as usize).collect(); + let values: Vec = challenge.values.iter().map(|value| *value as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let num_states = (num_items + 1) * (max_weight_plus_one); + let mut dp = vec![0; num_states]; + + for i in 1..=num_items { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let i_minus_one_times_max_weight_plus_one = (i - 1) * max_weight_plus_one; + let i_times_max_weight_plus_one = i * max_weight_plus_one; + for w in (item_weight..=max_weight).rev() { + let prev_state = i_minus_one_times_max_weight_plus_one + w; + let curr_state = i_times_max_weight_plus_one + w; + dp[curr_state] = dp[prev_state].max(dp[prev_state - item_weight] + item_value); + } + } + + let mut items = Vec::with_capacity(num_items); + let mut i = num_items; + let mut w = max_weight; + let mut total_value = 0; + while i > 0 && total_value < min_value { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let prev_state = (i - 1) * (max_weight_plus_one) + w; + let curr_state = i * (max_weight_plus_one) + w; + if dp[curr_state] != dp[prev_state] { + items.push(item_index); + w -= item_weight; + total_value += item_value; + } + i -= 1; + } + + if total_value >= min_value { + Ok(Some(Solution { items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/knapmaxxing/commercial.rs b/tig-algorithms/src/knapsack/knapmaxxing/commercial.rs new file mode 100644 index 0000000..9b4962b --- /dev/null +++ b/tig-algorithms/src/knapsack/knapmaxxing/commercial.rs @@ -0,0 +1,115 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let max_weight_plus_one = max_weight + 1; + + let weights: Vec = challenge.weights.iter().map(|weight| *weight as usize).collect(); + let values: Vec = challenge.values.iter().map(|value| *value as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let num_states = (num_items + 1) * (max_weight_plus_one); + let mut dp = vec![0; num_states]; + + for i in 1..=num_items { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let i_minus_one_times_max_weight_plus_one = (i - 1) * max_weight_plus_one; + let i_times_max_weight_plus_one = i * max_weight_plus_one; + for w in (item_weight..=max_weight).rev() { + let prev_state = i_minus_one_times_max_weight_plus_one + w; + let curr_state = i_times_max_weight_plus_one + w; + dp[curr_state] = dp[prev_state].max(dp[prev_state - item_weight] + item_value); + } + } + + let mut items = Vec::with_capacity(num_items); + let mut i = num_items; + let mut w = max_weight; + let mut total_value = 0; + while i > 0 && total_value < min_value { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let prev_state = (i - 1) * (max_weight_plus_one) + w; + let curr_state = i * (max_weight_plus_one) + w; + if dp[curr_state] != dp[prev_state] { + items.push(item_index); + w -= item_weight; + total_value += item_value; + } + i -= 1; + } + + if total_value >= min_value { + Ok(Some(Solution { items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/knapmaxxing/inbound.rs b/tig-algorithms/src/knapsack/knapmaxxing/inbound.rs new file mode 100644 index 0000000..7fd304a --- /dev/null +++ b/tig-algorithms/src/knapsack/knapmaxxing/inbound.rs @@ -0,0 +1,115 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let max_weight_plus_one = max_weight + 1; + + let weights: Vec = challenge.weights.iter().map(|weight| *weight as usize).collect(); + let values: Vec = challenge.values.iter().map(|value| *value as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let num_states = (num_items + 1) * (max_weight_plus_one); + let mut dp = vec![0; num_states]; + + for i in 1..=num_items { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let i_minus_one_times_max_weight_plus_one = (i - 1) * max_weight_plus_one; + let i_times_max_weight_plus_one = i * max_weight_plus_one; + for w in (item_weight..=max_weight).rev() { + let prev_state = i_minus_one_times_max_weight_plus_one + w; + let curr_state = i_times_max_weight_plus_one + w; + dp[curr_state] = dp[prev_state].max(dp[prev_state - item_weight] + item_value); + } + } + + let mut items = Vec::with_capacity(num_items); + let mut i = num_items; + let mut w = max_weight; + let mut total_value = 0; + while i > 0 && total_value < min_value { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let prev_state = (i - 1) * (max_weight_plus_one) + w; + let curr_state = i * (max_weight_plus_one) + w; + if dp[curr_state] != dp[prev_state] { + items.push(item_index); + w -= item_weight; + total_value += item_value; + } + i -= 1; + } + + if total_value >= min_value { + Ok(Some(Solution { items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/knapmaxxing/innovator_outbound.rs b/tig-algorithms/src/knapsack/knapmaxxing/innovator_outbound.rs new file mode 100644 index 0000000..a730bb6 --- /dev/null +++ b/tig-algorithms/src/knapsack/knapmaxxing/innovator_outbound.rs @@ -0,0 +1,115 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let max_weight_plus_one = max_weight + 1; + + let weights: Vec = challenge.weights.iter().map(|weight| *weight as usize).collect(); + let values: Vec = challenge.values.iter().map(|value| *value as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let num_states = (num_items + 1) * (max_weight_plus_one); + let mut dp = vec![0; num_states]; + + for i in 1..=num_items { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let i_minus_one_times_max_weight_plus_one = (i - 1) * max_weight_plus_one; + let i_times_max_weight_plus_one = i * max_weight_plus_one; + for w in (item_weight..=max_weight).rev() { + let prev_state = i_minus_one_times_max_weight_plus_one + w; + let curr_state = i_times_max_weight_plus_one + w; + dp[curr_state] = dp[prev_state].max(dp[prev_state - item_weight] + item_value); + } + } + + let mut items = Vec::with_capacity(num_items); + let mut i = num_items; + let mut w = max_weight; + let mut total_value = 0; + while i > 0 && total_value < min_value { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let prev_state = (i - 1) * (max_weight_plus_one) + w; + let curr_state = i * (max_weight_plus_one) + w; + if dp[curr_state] != dp[prev_state] { + items.push(item_index); + w -= item_weight; + total_value += item_value; + } + i -= 1; + } + + if total_value >= min_value { + Ok(Some(Solution { items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/knapmaxxing/mod.rs b/tig-algorithms/src/knapsack/knapmaxxing/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/knapsack/knapmaxxing/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/knapmaxxing/open_data.rs b/tig-algorithms/src/knapsack/knapmaxxing/open_data.rs new file mode 100644 index 0000000..e2849d9 --- /dev/null +++ b/tig-algorithms/src/knapsack/knapmaxxing/open_data.rs @@ -0,0 +1,115 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::knapsack::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_weight = challenge.max_weight as usize; + let min_value = challenge.min_value as usize; + let num_items = challenge.difficulty.num_items; + + let max_weight_plus_one = max_weight + 1; + + let weights: Vec = challenge.weights.iter().map(|weight| *weight as usize).collect(); + let values: Vec = challenge.values.iter().map(|value| *value as usize).collect(); + + let mut sorted_items: Vec<(usize, f64)> = (0..num_items) + .map(|i| (i, values[i] as f64 / weights[i] as f64)) + .collect(); + sorted_items.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut upper_bound = 0; + let mut remaining_weight = max_weight; + for &(item_index, ratio) in &sorted_items { + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + if item_weight <= remaining_weight { + upper_bound += item_value; + remaining_weight -= item_weight; + } else { + upper_bound += (ratio * remaining_weight as f64).floor() as usize; + break; + } + } + + if upper_bound < min_value { + return Ok(None); + } + + let num_states = (num_items + 1) * (max_weight_plus_one); + let mut dp = vec![0; num_states]; + + for i in 1..=num_items { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let i_minus_one_times_max_weight_plus_one = (i - 1) * max_weight_plus_one; + let i_times_max_weight_plus_one = i * max_weight_plus_one; + for w in (item_weight..=max_weight).rev() { + let prev_state = i_minus_one_times_max_weight_plus_one + w; + let curr_state = i_times_max_weight_plus_one + w; + dp[curr_state] = dp[prev_state].max(dp[prev_state - item_weight] + item_value); + } + } + + let mut items = Vec::with_capacity(num_items); + let mut i = num_items; + let mut w = max_weight; + let mut total_value = 0; + while i > 0 && total_value < min_value { + let (item_index, _) = sorted_items[i - 1]; + let item_weight = weights[item_index]; + let item_value = values[item_index]; + + let prev_state = (i - 1) * (max_weight_plus_one) + w; + let curr_state = i * (max_weight_plus_one) + w; + if dp[curr_state] != dp[prev_state] { + items.push(item_index); + w -= item_weight; + total_value += item_value; + } + i -= 1; + } + + if total_value >= min_value { + Ok(Some(Solution { items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/knapsack/mod.rs b/tig-algorithms/src/knapsack/mod.rs index 33ddf59..7a46bee 100644 --- a/tig-algorithms/src/knapsack/mod.rs +++ b/tig-algorithms/src/knapsack/mod.rs @@ -10,7 +10,8 @@ // c003_a006 -// c003_a007 +pub mod knapmaxxing; +pub use knapmaxxing as c003_a007; // c003_a008 diff --git a/tig-algorithms/src/knapsack/template.rs b/tig-algorithms/src/knapsack/template.rs index 6386cbf..dab7384 100644 --- a/tig-algorithms/src/knapsack/template.rs +++ b/tig-algorithms/src/knapsack/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::knapsack::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/knapsack/knapmaxxing.wasm b/tig-algorithms/wasm/knapsack/knapmaxxing.wasm new file mode 100644 index 0000000000000000000000000000000000000000..b638fd0fd51e2ff422d3150c0ce8047cdc929282 GIT binary patch literal 156011 zcmeFa3!GoaRp)tM|M&mj{g>3TWw-3EdnMZ~%ZgeQ+8s&h`&jox+>b8@I9succX>Og3~+J6WF+U8|TsX&l#TwXEF?g>f2DT+7mgDm)ASHE5Mi zshp)*x`C@Wt^-^e@dFa0`o>LlI*s2M-+p@y05;THAj~f>pN=|>JgtBK_k909+W&(| zlzrcw-*+JTXx#apI}hyt!S8(MKl;8q4@AElx0d3_$DQwNUNYG@9=|94*2VY6n~VSU zvbXL32a6jf+M71twD+z5-S>RoP2YL@tG{*fhvVjL_r>pvPsH^LUj41#b@6rIeelAK z(?4+0jtl1gtN+^nllb*-{`On{hqr(4JLc~?@Q;7+-An$S_<|pN_dku7e7+yFG0jX&#Q3wnhHQWT8m6N6DVSzaxXI zVc8xR%_e=uxxDa(Ukt1iv~VhPySpZ%Q=3ov`%VD9!x5NiQRLJKpxFp4MX#p&$R`Uf^A8=l!zJ^f-ijJAjna2(?(CT_UwK;|xpv-iQ7`HM 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z+8vd??rn`JfnvY;a-b2LjC5VLiSIO-H(khoqM$EqPGifo%CYXy#A$P9^LwU0(F))5 zr(vpxoo7-`DVTNyQx1Dgx$0i*j+XXooY0gu)F{GN%_~W-)q9`6IOa8C@Lh^CgJQk9 z!iP2f=1Nre&s21s%wunP%(>v5Z0S>Q{-x*N`~H?~&F<;{&aTV*zVE!FmyLV+KhpEt zJ;mOkzN?(C^grOdr1z@c$9n#?cenFUztdCQ^}QaqKe_8}w)GqAo$mWa|9f{m-}mzV z-Tfcib-4esUFV#+T{rc9ulJkIXM11Y_XX!ey&L`4?W*^z^o#2mG6ryAeHZf) hh$QLYsaj)^4MO)_u-K`c9`pp(zTGW$;2LBa^M7#Oh0y>2 literal 0 HcmV?d00001 From 76ff526390b7aa14aa8e6c6dc3be9b3ca3796965 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:04:26 +0000 Subject: [PATCH 23/49] Compiled knapsack/quadkp_improved --- tig-algorithms/src/knapsack/mod.rs | 3 +- .../quadkp_improved/benchmarker_outbound.rs | 188 ++++++++++++++++++ .../knapsack/quadkp_improved/commercial.rs | 188 ++++++++++++++++++ .../src/knapsack/quadkp_improved/inbound.rs | 188 ++++++++++++++++++ .../quadkp_improved/innovator_outbound.rs | 188 ++++++++++++++++++ .../src/knapsack/quadkp_improved/mod.rs | 4 + .../src/knapsack/quadkp_improved/open_data.rs | 188 ++++++++++++++++++ tig-algorithms/src/knapsack/template.rs | 26 +-- .../wasm/knapsack/quadkp_improved.wasm | Bin 0 -> 167148 bytes 9 files changed, 948 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/knapsack/quadkp_improved/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/knapsack/quadkp_improved/commercial.rs create mode 100644 tig-algorithms/src/knapsack/quadkp_improved/inbound.rs create mode 100644 tig-algorithms/src/knapsack/quadkp_improved/innovator_outbound.rs create mode 100644 tig-algorithms/src/knapsack/quadkp_improved/mod.rs create mode 100644 tig-algorithms/src/knapsack/quadkp_improved/open_data.rs create mode 100644 tig-algorithms/wasm/knapsack/quadkp_improved.wasm diff --git a/tig-algorithms/src/knapsack/mod.rs b/tig-algorithms/src/knapsack/mod.rs index 33ddf59..7ac16f2 100644 --- a/tig-algorithms/src/knapsack/mod.rs +++ b/tig-algorithms/src/knapsack/mod.rs @@ -104,7 +104,8 @@ // c003_a053 -// c003_a054 +pub mod quadkp_improved; +pub use quadkp_improved as c003_a054; // c003_a055 diff --git a/tig-algorithms/src/knapsack/quadkp_improved/benchmarker_outbound.rs b/tig-algorithms/src/knapsack/quadkp_improved/benchmarker_outbound.rs new file mode 100644 index 0000000..f3b2d5c --- /dev/null +++ b/tig-algorithms/src/knapsack/quadkp_improved/benchmarker_outbound.rs @@ -0,0 +1,188 @@ +/*! +Copyright 2024 Rootz + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use anyhow::Result; +use rand::{SeedableRng, Rng, rngs::StdRng}; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut item_scores: Vec<(usize, f32)> = (0..vertex_count) + .map(|index| { + let interaction_sum: i32 = challenge.interaction_values[index].iter().sum(); + let secondary_score = challenge.values[index] as f32 / challenge.weights[index] as f32; + let combined_score = (challenge.values[index] as f32 * 0.75 + interaction_sum as f32 * 0.15 + secondary_score * 0.1) + / challenge.weights[index] as f32; + (index, combined_score) + }) + .collect(); + + item_scores.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut selected_items = Vec::with_capacity(vertex_count); + let mut unselected_items = Vec::with_capacity(vertex_count); + let mut current_weight = 0; + let mut current_value = 0; + + for &(index, _) in &item_scores { + if current_weight + challenge.weights[index] <= challenge.max_weight { + current_weight += challenge.weights[index]; + current_value += challenge.values[index] as i32; + + for &selected in &selected_items { + current_value += challenge.interaction_values[index][selected]; + } + selected_items.push(index); + } else { + unselected_items.push(index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for index in 0..vertex_count { + mutation_rates[index] = challenge.values[index] as i32; + for &selected in &selected_items { + mutation_rates[index] += challenge.interaction_values[index][selected]; + } + } + + let max_generations = 150; + let mut cooling_schedule = vec![0; vertex_count]; + let mut rng = StdRng::seed_from_u64(challenge.seed[0] as u64); + + for generation in 0..max_generations { + let mut best_gain = 0; + let mut best_swap = None; + + for (u_index, &mutant) in unselected_items.iter().enumerate() { + if cooling_schedule[mutant] > 0 { + continue; + } + + let mutant_fitness = mutation_rates[mutant]; + let extra_weight = challenge.weights[mutant] as i32 - (challenge.max_weight as i32 - current_weight as i32); + + if mutant_fitness < 0 { + continue; + } + + for (c_index, &selected) in selected_items.iter().enumerate() { + if cooling_schedule[selected] > 0 { + continue; + } + + if extra_weight > 0 && (challenge.weights[selected] as i32) < extra_weight { + continue; + } + + let interaction_penalty = (challenge.interaction_values[mutant][selected] as f32 * 0.3) as i32; + let fitness_gain = mutant_fitness - mutation_rates[selected] - interaction_penalty; + + if fitness_gain > best_gain { + best_gain = fitness_gain; + best_swap = Some((u_index, c_index)); + } + } + } + + if let Some((u_index, c_index)) = best_swap { + let added_item = unselected_items[u_index]; + let removed_item = selected_items[c_index]; + + selected_items.swap_remove(c_index); + unselected_items.swap_remove(u_index); + selected_items.push(added_item); + unselected_items.push(removed_item); + + current_value += best_gain; + current_weight = current_weight + challenge.weights[added_item] - challenge.weights[removed_item]; + + if current_weight > challenge.max_weight { + continue; + } + + for index in 0..vertex_count { + mutation_rates[index] += challenge.interaction_values[index][added_item] + - challenge.interaction_values[index][removed_item]; + } + + cooling_schedule[added_item] = 3; + cooling_schedule[removed_item] = 3; + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + + if current_value as u32 > (challenge.min_value * 9 / 10) { + let high_potential_items: Vec = unselected_items + .iter() + .filter(|&&i| challenge.values[i] as i32 > (challenge.min_value as i32 / 4)) + .copied() + .collect(); + + for &item in high_potential_items.iter().take(2) { + if current_weight + challenge.weights[item] <= challenge.max_weight { + selected_items.push(item); + unselected_items.retain(|&x| x != item); + current_weight += challenge.weights[item]; + current_value += challenge.values[item] as i32; + + for &selected in &selected_items { + if selected != item { + current_value += challenge.interaction_values[item][selected]; + } + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + } + } + } + } + + if current_value as u32 >= challenge.min_value && current_weight <= challenge.max_weight { + Ok(Some(Solution { items: selected_items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + pub const KERNEL: Option = None; + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/quadkp_improved/commercial.rs b/tig-algorithms/src/knapsack/quadkp_improved/commercial.rs new file mode 100644 index 0000000..6d7cbab --- /dev/null +++ b/tig-algorithms/src/knapsack/quadkp_improved/commercial.rs @@ -0,0 +1,188 @@ +/*! +Copyright 2024 Rootz + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use anyhow::Result; +use rand::{SeedableRng, Rng, rngs::StdRng}; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut item_scores: Vec<(usize, f32)> = (0..vertex_count) + .map(|index| { + let interaction_sum: i32 = challenge.interaction_values[index].iter().sum(); + let secondary_score = challenge.values[index] as f32 / challenge.weights[index] as f32; + let combined_score = (challenge.values[index] as f32 * 0.75 + interaction_sum as f32 * 0.15 + secondary_score * 0.1) + / challenge.weights[index] as f32; + (index, combined_score) + }) + .collect(); + + item_scores.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut selected_items = Vec::with_capacity(vertex_count); + let mut unselected_items = Vec::with_capacity(vertex_count); + let mut current_weight = 0; + let mut current_value = 0; + + for &(index, _) in &item_scores { + if current_weight + challenge.weights[index] <= challenge.max_weight { + current_weight += challenge.weights[index]; + current_value += challenge.values[index] as i32; + + for &selected in &selected_items { + current_value += challenge.interaction_values[index][selected]; + } + selected_items.push(index); + } else { + unselected_items.push(index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for index in 0..vertex_count { + mutation_rates[index] = challenge.values[index] as i32; + for &selected in &selected_items { + mutation_rates[index] += challenge.interaction_values[index][selected]; + } + } + + let max_generations = 150; + let mut cooling_schedule = vec![0; vertex_count]; + let mut rng = StdRng::seed_from_u64(challenge.seed[0] as u64); + + for generation in 0..max_generations { + let mut best_gain = 0; + let mut best_swap = None; + + for (u_index, &mutant) in unselected_items.iter().enumerate() { + if cooling_schedule[mutant] > 0 { + continue; + } + + let mutant_fitness = mutation_rates[mutant]; + let extra_weight = challenge.weights[mutant] as i32 - (challenge.max_weight as i32 - current_weight as i32); + + if mutant_fitness < 0 { + continue; + } + + for (c_index, &selected) in selected_items.iter().enumerate() { + if cooling_schedule[selected] > 0 { + continue; + } + + if extra_weight > 0 && (challenge.weights[selected] as i32) < extra_weight { + continue; + } + + let interaction_penalty = (challenge.interaction_values[mutant][selected] as f32 * 0.3) as i32; + let fitness_gain = mutant_fitness - mutation_rates[selected] - interaction_penalty; + + if fitness_gain > best_gain { + best_gain = fitness_gain; + best_swap = Some((u_index, c_index)); + } + } + } + + if let Some((u_index, c_index)) = best_swap { + let added_item = unselected_items[u_index]; + let removed_item = selected_items[c_index]; + + selected_items.swap_remove(c_index); + unselected_items.swap_remove(u_index); + selected_items.push(added_item); + unselected_items.push(removed_item); + + current_value += best_gain; + current_weight = current_weight + challenge.weights[added_item] - challenge.weights[removed_item]; + + if current_weight > challenge.max_weight { + continue; + } + + for index in 0..vertex_count { + mutation_rates[index] += challenge.interaction_values[index][added_item] + - challenge.interaction_values[index][removed_item]; + } + + cooling_schedule[added_item] = 3; + cooling_schedule[removed_item] = 3; + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + + if current_value as u32 > (challenge.min_value * 9 / 10) { + let high_potential_items: Vec = unselected_items + .iter() + .filter(|&&i| challenge.values[i] as i32 > (challenge.min_value as i32 / 4)) + .copied() + .collect(); + + for &item in high_potential_items.iter().take(2) { + if current_weight + challenge.weights[item] <= challenge.max_weight { + selected_items.push(item); + unselected_items.retain(|&x| x != item); + current_weight += challenge.weights[item]; + current_value += challenge.values[item] as i32; + + for &selected in &selected_items { + if selected != item { + current_value += challenge.interaction_values[item][selected]; + } + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + } + } + } + } + + if current_value as u32 >= challenge.min_value && current_weight <= challenge.max_weight { + Ok(Some(Solution { items: selected_items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + pub const KERNEL: Option = None; + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/quadkp_improved/inbound.rs b/tig-algorithms/src/knapsack/quadkp_improved/inbound.rs new file mode 100644 index 0000000..d82bd71 --- /dev/null +++ b/tig-algorithms/src/knapsack/quadkp_improved/inbound.rs @@ -0,0 +1,188 @@ +/*! +Copyright 2024 Rootz + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use anyhow::Result; +use rand::{SeedableRng, Rng, rngs::StdRng}; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut item_scores: Vec<(usize, f32)> = (0..vertex_count) + .map(|index| { + let interaction_sum: i32 = challenge.interaction_values[index].iter().sum(); + let secondary_score = challenge.values[index] as f32 / challenge.weights[index] as f32; + let combined_score = (challenge.values[index] as f32 * 0.75 + interaction_sum as f32 * 0.15 + secondary_score * 0.1) + / challenge.weights[index] as f32; + (index, combined_score) + }) + .collect(); + + item_scores.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut selected_items = Vec::with_capacity(vertex_count); + let mut unselected_items = Vec::with_capacity(vertex_count); + let mut current_weight = 0; + let mut current_value = 0; + + for &(index, _) in &item_scores { + if current_weight + challenge.weights[index] <= challenge.max_weight { + current_weight += challenge.weights[index]; + current_value += challenge.values[index] as i32; + + for &selected in &selected_items { + current_value += challenge.interaction_values[index][selected]; + } + selected_items.push(index); + } else { + unselected_items.push(index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for index in 0..vertex_count { + mutation_rates[index] = challenge.values[index] as i32; + for &selected in &selected_items { + mutation_rates[index] += challenge.interaction_values[index][selected]; + } + } + + let max_generations = 150; + let mut cooling_schedule = vec![0; vertex_count]; + let mut rng = StdRng::seed_from_u64(challenge.seed[0] as u64); + + for generation in 0..max_generations { + let mut best_gain = 0; + let mut best_swap = None; + + for (u_index, &mutant) in unselected_items.iter().enumerate() { + if cooling_schedule[mutant] > 0 { + continue; + } + + let mutant_fitness = mutation_rates[mutant]; + let extra_weight = challenge.weights[mutant] as i32 - (challenge.max_weight as i32 - current_weight as i32); + + if mutant_fitness < 0 { + continue; + } + + for (c_index, &selected) in selected_items.iter().enumerate() { + if cooling_schedule[selected] > 0 { + continue; + } + + if extra_weight > 0 && (challenge.weights[selected] as i32) < extra_weight { + continue; + } + + let interaction_penalty = (challenge.interaction_values[mutant][selected] as f32 * 0.3) as i32; + let fitness_gain = mutant_fitness - mutation_rates[selected] - interaction_penalty; + + if fitness_gain > best_gain { + best_gain = fitness_gain; + best_swap = Some((u_index, c_index)); + } + } + } + + if let Some((u_index, c_index)) = best_swap { + let added_item = unselected_items[u_index]; + let removed_item = selected_items[c_index]; + + selected_items.swap_remove(c_index); + unselected_items.swap_remove(u_index); + selected_items.push(added_item); + unselected_items.push(removed_item); + + current_value += best_gain; + current_weight = current_weight + challenge.weights[added_item] - challenge.weights[removed_item]; + + if current_weight > challenge.max_weight { + continue; + } + + for index in 0..vertex_count { + mutation_rates[index] += challenge.interaction_values[index][added_item] + - challenge.interaction_values[index][removed_item]; + } + + cooling_schedule[added_item] = 3; + cooling_schedule[removed_item] = 3; + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + + if current_value as u32 > (challenge.min_value * 9 / 10) { + let high_potential_items: Vec = unselected_items + .iter() + .filter(|&&i| challenge.values[i] as i32 > (challenge.min_value as i32 / 4)) + .copied() + .collect(); + + for &item in high_potential_items.iter().take(2) { + if current_weight + challenge.weights[item] <= challenge.max_weight { + selected_items.push(item); + unselected_items.retain(|&x| x != item); + current_weight += challenge.weights[item]; + current_value += challenge.values[item] as i32; + + for &selected in &selected_items { + if selected != item { + current_value += challenge.interaction_values[item][selected]; + } + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + } + } + } + } + + if current_value as u32 >= challenge.min_value && current_weight <= challenge.max_weight { + Ok(Some(Solution { items: selected_items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + pub const KERNEL: Option = None; + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/quadkp_improved/innovator_outbound.rs b/tig-algorithms/src/knapsack/quadkp_improved/innovator_outbound.rs new file mode 100644 index 0000000..8b43c0e --- /dev/null +++ b/tig-algorithms/src/knapsack/quadkp_improved/innovator_outbound.rs @@ -0,0 +1,188 @@ +/*! +Copyright 2024 Rootz + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use anyhow::Result; +use rand::{SeedableRng, Rng, rngs::StdRng}; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut item_scores: Vec<(usize, f32)> = (0..vertex_count) + .map(|index| { + let interaction_sum: i32 = challenge.interaction_values[index].iter().sum(); + let secondary_score = challenge.values[index] as f32 / challenge.weights[index] as f32; + let combined_score = (challenge.values[index] as f32 * 0.75 + interaction_sum as f32 * 0.15 + secondary_score * 0.1) + / challenge.weights[index] as f32; + (index, combined_score) + }) + .collect(); + + item_scores.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut selected_items = Vec::with_capacity(vertex_count); + let mut unselected_items = Vec::with_capacity(vertex_count); + let mut current_weight = 0; + let mut current_value = 0; + + for &(index, _) in &item_scores { + if current_weight + challenge.weights[index] <= challenge.max_weight { + current_weight += challenge.weights[index]; + current_value += challenge.values[index] as i32; + + for &selected in &selected_items { + current_value += challenge.interaction_values[index][selected]; + } + selected_items.push(index); + } else { + unselected_items.push(index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for index in 0..vertex_count { + mutation_rates[index] = challenge.values[index] as i32; + for &selected in &selected_items { + mutation_rates[index] += challenge.interaction_values[index][selected]; + } + } + + let max_generations = 150; + let mut cooling_schedule = vec![0; vertex_count]; + let mut rng = StdRng::seed_from_u64(challenge.seed[0] as u64); + + for generation in 0..max_generations { + let mut best_gain = 0; + let mut best_swap = None; + + for (u_index, &mutant) in unselected_items.iter().enumerate() { + if cooling_schedule[mutant] > 0 { + continue; + } + + let mutant_fitness = mutation_rates[mutant]; + let extra_weight = challenge.weights[mutant] as i32 - (challenge.max_weight as i32 - current_weight as i32); + + if mutant_fitness < 0 { + continue; + } + + for (c_index, &selected) in selected_items.iter().enumerate() { + if cooling_schedule[selected] > 0 { + continue; + } + + if extra_weight > 0 && (challenge.weights[selected] as i32) < extra_weight { + continue; + } + + let interaction_penalty = (challenge.interaction_values[mutant][selected] as f32 * 0.3) as i32; + let fitness_gain = mutant_fitness - mutation_rates[selected] - interaction_penalty; + + if fitness_gain > best_gain { + best_gain = fitness_gain; + best_swap = Some((u_index, c_index)); + } + } + } + + if let Some((u_index, c_index)) = best_swap { + let added_item = unselected_items[u_index]; + let removed_item = selected_items[c_index]; + + selected_items.swap_remove(c_index); + unselected_items.swap_remove(u_index); + selected_items.push(added_item); + unselected_items.push(removed_item); + + current_value += best_gain; + current_weight = current_weight + challenge.weights[added_item] - challenge.weights[removed_item]; + + if current_weight > challenge.max_weight { + continue; + } + + for index in 0..vertex_count { + mutation_rates[index] += challenge.interaction_values[index][added_item] + - challenge.interaction_values[index][removed_item]; + } + + cooling_schedule[added_item] = 3; + cooling_schedule[removed_item] = 3; + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + + if current_value as u32 > (challenge.min_value * 9 / 10) { + let high_potential_items: Vec = unselected_items + .iter() + .filter(|&&i| challenge.values[i] as i32 > (challenge.min_value as i32 / 4)) + .copied() + .collect(); + + for &item in high_potential_items.iter().take(2) { + if current_weight + challenge.weights[item] <= challenge.max_weight { + selected_items.push(item); + unselected_items.retain(|&x| x != item); + current_weight += challenge.weights[item]; + current_value += challenge.values[item] as i32; + + for &selected in &selected_items { + if selected != item { + current_value += challenge.interaction_values[item][selected]; + } + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + } + } + } + } + + if current_value as u32 >= challenge.min_value && current_weight <= challenge.max_weight { + Ok(Some(Solution { items: selected_items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + pub const KERNEL: Option = None; + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/quadkp_improved/mod.rs b/tig-algorithms/src/knapsack/quadkp_improved/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/knapsack/quadkp_improved/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/quadkp_improved/open_data.rs b/tig-algorithms/src/knapsack/quadkp_improved/open_data.rs new file mode 100644 index 0000000..560b774 --- /dev/null +++ b/tig-algorithms/src/knapsack/quadkp_improved/open_data.rs @@ -0,0 +1,188 @@ +/*! +Copyright 2024 Rootz + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use anyhow::Result; +use rand::{SeedableRng, Rng, rngs::StdRng}; +use tig_challenges::knapsack::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vertex_count = challenge.weights.len(); + + let mut item_scores: Vec<(usize, f32)> = (0..vertex_count) + .map(|index| { + let interaction_sum: i32 = challenge.interaction_values[index].iter().sum(); + let secondary_score = challenge.values[index] as f32 / challenge.weights[index] as f32; + let combined_score = (challenge.values[index] as f32 * 0.75 + interaction_sum as f32 * 0.15 + secondary_score * 0.1) + / challenge.weights[index] as f32; + (index, combined_score) + }) + .collect(); + + item_scores.sort_unstable_by(|a, b| b.1.partial_cmp(&a.1).unwrap()); + + let mut selected_items = Vec::with_capacity(vertex_count); + let mut unselected_items = Vec::with_capacity(vertex_count); + let mut current_weight = 0; + let mut current_value = 0; + + for &(index, _) in &item_scores { + if current_weight + challenge.weights[index] <= challenge.max_weight { + current_weight += challenge.weights[index]; + current_value += challenge.values[index] as i32; + + for &selected in &selected_items { + current_value += challenge.interaction_values[index][selected]; + } + selected_items.push(index); + } else { + unselected_items.push(index); + } + } + + let mut mutation_rates = vec![0; vertex_count]; + for index in 0..vertex_count { + mutation_rates[index] = challenge.values[index] as i32; + for &selected in &selected_items { + mutation_rates[index] += challenge.interaction_values[index][selected]; + } + } + + let max_generations = 150; + let mut cooling_schedule = vec![0; vertex_count]; + let mut rng = StdRng::seed_from_u64(challenge.seed[0] as u64); + + for generation in 0..max_generations { + let mut best_gain = 0; + let mut best_swap = None; + + for (u_index, &mutant) in unselected_items.iter().enumerate() { + if cooling_schedule[mutant] > 0 { + continue; + } + + let mutant_fitness = mutation_rates[mutant]; + let extra_weight = challenge.weights[mutant] as i32 - (challenge.max_weight as i32 - current_weight as i32); + + if mutant_fitness < 0 { + continue; + } + + for (c_index, &selected) in selected_items.iter().enumerate() { + if cooling_schedule[selected] > 0 { + continue; + } + + if extra_weight > 0 && (challenge.weights[selected] as i32) < extra_weight { + continue; + } + + let interaction_penalty = (challenge.interaction_values[mutant][selected] as f32 * 0.3) as i32; + let fitness_gain = mutant_fitness - mutation_rates[selected] - interaction_penalty; + + if fitness_gain > best_gain { + best_gain = fitness_gain; + best_swap = Some((u_index, c_index)); + } + } + } + + if let Some((u_index, c_index)) = best_swap { + let added_item = unselected_items[u_index]; + let removed_item = selected_items[c_index]; + + selected_items.swap_remove(c_index); + unselected_items.swap_remove(u_index); + selected_items.push(added_item); + unselected_items.push(removed_item); + + current_value += best_gain; + current_weight = current_weight + challenge.weights[added_item] - challenge.weights[removed_item]; + + if current_weight > challenge.max_weight { + continue; + } + + for index in 0..vertex_count { + mutation_rates[index] += challenge.interaction_values[index][added_item] + - challenge.interaction_values[index][removed_item]; + } + + cooling_schedule[added_item] = 3; + cooling_schedule[removed_item] = 3; + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + + for cooling_rate in cooling_schedule.iter_mut() { + *cooling_rate = if *cooling_rate > 0 { *cooling_rate - 1 } else { 0 }; + } + + if current_value as u32 > (challenge.min_value * 9 / 10) { + let high_potential_items: Vec = unselected_items + .iter() + .filter(|&&i| challenge.values[i] as i32 > (challenge.min_value as i32 / 4)) + .copied() + .collect(); + + for &item in high_potential_items.iter().take(2) { + if current_weight + challenge.weights[item] <= challenge.max_weight { + selected_items.push(item); + unselected_items.retain(|&x| x != item); + current_weight += challenge.weights[item]; + current_value += challenge.values[item] as i32; + + for &selected in &selected_items { + if selected != item { + current_value += challenge.interaction_values[item][selected]; + } + } + + if current_value as u32 >= challenge.min_value { + return Ok(Some(Solution { items: selected_items })); + } + } + } + } + } + + if current_value as u32 >= challenge.min_value && current_weight <= challenge.max_weight { + Ok(Some(Solution { items: selected_items })) + } else { + Ok(None) + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + pub const KERNEL: Option = None; + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/knapsack/template.rs b/tig-algorithms/src/knapsack/template.rs index 6386cbf..dab7384 100644 --- a/tig-algorithms/src/knapsack/template.rs +++ b/tig-algorithms/src/knapsack/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::knapsack::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/knapsack/quadkp_improved.wasm b/tig-algorithms/wasm/knapsack/quadkp_improved.wasm new file mode 100644 index 0000000000000000000000000000000000000000..d3790ec4f808e0d6d3db327b5cf5da5cd98c8f49 GIT binary patch literal 167148 zcmeFa4V+)sS>JhI{%`YtnfXf^$FfJxoO?aCN0t;TU}VQOvCi!JMUjo2;B`Yj?c#RJ z0yD;BWK{$*(u^(HD2Y?N#k)0Nh#)3%*(hIN(y+xQkm9;-&rsO4TKR zfduXM_ndqGFY_YF36TBlXRVLtfA6{Ho|or5@6U72jsE`q-xo(w6h9h|-;*vbF2;-Z zB=$F6jC2)QX>?DlCsit_s50s;>II8eUWrVlWN}P<1P#Ra_N|%DBuJs>v<9fYbY1GuV3gSwd6c2S@qd5kl zMyXY)RW?-UzLq2-4O)$>j4zG&0gh31)vEUkUt zcYWXY@B6_ds(kN#-@8A0CT@M#ef#(Q;CH_F`@i?T{n0PRBM0Kh$F1)?w0OgPOYx=M zjhilT|J`NxeE)my8yRcWFSzB_cYnt}_#bci@4xf98@dn1n~ubX3yz^b({y+TvfB0SB{oeU~6dbtm;E%*V_(Shs_{Z@F;`<+t 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z2mA`$2iz<;&<%(Hq(EOF7cc;`fvvy+fYLaP-)q1lz;mbv(-BAn1_5QjBw!6dVRzy8 z8{h`;67a1;y8=>x;lNB_EwBx!2O1CaVA=zzfEK6*CIa(-jlj3SFTg|K6+n43uSOgo z5=a8V88xSFGw)Qbm?TcMV+VG|Iz-d3ZX(`%GX_SIaI`7`*;rXw&HO_#Xa0i#K^#uG z_=h%O^RbTVOtM{tWZo8LBtUEejE%{DJMgALz`$rudIh&eMvq}%+hrWpMC%eaO-Ssa z=&TQ{cbjO&;4n2nL~cQsBoXruse<_L5MRtYvE zFz?WvnK!3H7>;b83otf~*H+R0&>7zHf%6URhMJf_ryVa<)a;b#Mp8lX=IijAFbo zy7di$K3#w7heCW*{H zrZe_r->LN3y=hFbDfVyPwrRv{LF_KRiN9+y&vqf}6WRL0n|Zu7tzvk$!^G*$&SveI z)`^y*pLH5W@8R7uDMoFZcG#vI{5>j_{ic1iR8Vf&R?mGv!>~)3 zwl7~U%gX`!+zN zT700WH@kIdfe%Yau7(wr8S`=QXO|}Vma@_?PJDG~WX4%nVVKl(X#qnIIxoybVKkdE OcX2PLHx035(f<#KT-{&* literal 0 HcmV?d00001 From 3e85764aee65efc658d0178bb6314cb7d7ff52c1 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:04:52 +0000 Subject: [PATCH 24/49] Compiled satisfiability/fast_walk_sat --- .../fast_walk_sat/benchmarker_outbound.rs | 250 ++++++++++++++++++ .../fast_walk_sat/commercial.rs | 250 ++++++++++++++++++ .../satisfiability/fast_walk_sat/inbound.rs | 250 ++++++++++++++++++ .../fast_walk_sat/innovator_outbound.rs | 250 ++++++++++++++++++ .../src/satisfiability/fast_walk_sat/mod.rs | 4 + .../satisfiability/fast_walk_sat/open_data.rs | 250 ++++++++++++++++++ tig-algorithms/src/satisfiability/mod.rs | 3 +- tig-algorithms/src/satisfiability/template.rs | 26 +- .../wasm/satisfiability/fast_walk_sat.wasm | Bin 0 -> 161544 bytes 9 files changed, 1258 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/satisfiability/fast_walk_sat/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/fast_walk_sat/commercial.rs create mode 100644 tig-algorithms/src/satisfiability/fast_walk_sat/inbound.rs create mode 100644 tig-algorithms/src/satisfiability/fast_walk_sat/innovator_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/fast_walk_sat/mod.rs create mode 100644 tig-algorithms/src/satisfiability/fast_walk_sat/open_data.rs create mode 100644 tig-algorithms/wasm/satisfiability/fast_walk_sat.wasm diff --git a/tig-algorithms/src/satisfiability/fast_walk_sat/benchmarker_outbound.rs b/tig-algorithms/src/satisfiability/fast_walk_sat/benchmarker_outbound.rs new file mode 100644 index 0000000..dceb0e8 --- /dev/null +++ b/tig-algorithms/src/satisfiability/fast_walk_sat/benchmarker_outbound.rs @@ -0,0 +1,250 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashSet; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual = HashSet::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual.insert(i); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if let Some(&i) = residual.iter().next() { + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + for &c in &n_clauses[v] { + if variables[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } else { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } + } + for &c in &p_clauses[v] { + if variables[v] { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } else { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/fast_walk_sat/commercial.rs b/tig-algorithms/src/satisfiability/fast_walk_sat/commercial.rs new file mode 100644 index 0000000..a71cfb8 --- /dev/null +++ b/tig-algorithms/src/satisfiability/fast_walk_sat/commercial.rs @@ -0,0 +1,250 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashSet; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual = HashSet::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual.insert(i); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if let Some(&i) = residual.iter().next() { + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + for &c in &n_clauses[v] { + if variables[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } else { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } + } + for &c in &p_clauses[v] { + if variables[v] { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } else { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/fast_walk_sat/inbound.rs b/tig-algorithms/src/satisfiability/fast_walk_sat/inbound.rs new file mode 100644 index 0000000..f8bed23 --- /dev/null +++ b/tig-algorithms/src/satisfiability/fast_walk_sat/inbound.rs @@ -0,0 +1,250 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashSet; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual = HashSet::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual.insert(i); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if let Some(&i) = residual.iter().next() { + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + for &c in &n_clauses[v] { + if variables[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } else { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } + } + for &c in &p_clauses[v] { + if variables[v] { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } else { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/fast_walk_sat/innovator_outbound.rs b/tig-algorithms/src/satisfiability/fast_walk_sat/innovator_outbound.rs new file mode 100644 index 0000000..560c2eb --- /dev/null +++ b/tig-algorithms/src/satisfiability/fast_walk_sat/innovator_outbound.rs @@ -0,0 +1,250 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashSet; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual = HashSet::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual.insert(i); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if let Some(&i) = residual.iter().next() { + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + for &c in &n_clauses[v] { + if variables[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } else { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } + } + for &c in &p_clauses[v] { + if variables[v] { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } else { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/fast_walk_sat/mod.rs b/tig-algorithms/src/satisfiability/fast_walk_sat/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/fast_walk_sat/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/fast_walk_sat/open_data.rs b/tig-algorithms/src/satisfiability/fast_walk_sat/open_data.rs new file mode 100644 index 0000000..c68797e --- /dev/null +++ b/tig-algorithms/src/satisfiability/fast_walk_sat/open_data.rs @@ -0,0 +1,250 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashSet; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual = HashSet::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual.insert(i); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if let Some(&i) = residual.iter().next() { + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + for &c in &n_clauses[v] { + if variables[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } else { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } + } + for &c in &p_clauses[v] { + if variables[v] { + if num_good_so_far[c] == 1 { + residual.insert(c); + } + num_good_so_far[c] -= 1; + } else { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + residual.remove(&c); + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/mod.rs b/tig-algorithms/src/satisfiability/mod.rs index 22d6805..fe1adc4 100644 --- a/tig-algorithms/src/satisfiability/mod.rs +++ b/tig-algorithms/src/satisfiability/mod.rs @@ -18,7 +18,8 @@ // c001_a010 -// c001_a011 +pub mod fast_walk_sat; +pub use fast_walk_sat as c001_a011; // c001_a012 diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/satisfiability/fast_walk_sat.wasm b/tig-algorithms/wasm/satisfiability/fast_walk_sat.wasm new file mode 100644 index 0000000000000000000000000000000000000000..55b1a251013359ac629c6f9c60c4c40e86b146f7 GIT binary patch literal 161544 zcmeFa51d`sQRjL8z5lPfpQM)5QcKpkSF+uwpIYgvccB!3oZi$v8v`22A1v&k!bN@dhU`Yz8t}?HAF zTFefGf+;!bi`OW9B>rfeW?45);y8}tkJOE&TEySDoi(VKW^pps&^s+3{O~{D_oGRa z{m|_{v_E<{?)<>*`}h6m+urfdf9Ur8(MRIeyW_~moqyVV`B>vU@lVEA-|^mf_2%#X zuK#}jKfPncXnWP_8}@wffA@p4H+&4?i?>+m9T0=Z_t{^R;)!>wfIu{}|sD|9E^PJ{te4 z_+Q39#hm|4d|&*t@lV9R5O?FP?R&q}_-Oo}wxo^2%|r3Q&5?gHnJv;SQL?N5?{NQV zkhaT3Q%R3u&dt5+jw10j$A7ygeLOo_i<4TKMI9GUB~hV2dTJKYtWR&scDdL^o{6x; zT*dT0vm^I&xlDBz#8;Q_TY8aP3xp!EnEtF}LJhso6m^lcs%f77M5M>aYLu_}?YH^uvw00l 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+Copyright 2024 Clifford Algueraz + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/inbound/commercial.rs b/tig-algorithms/src/satisfiability/inbound/commercial.rs new file mode 100644 index 0000000..b8c9432 --- /dev/null +++ b/tig-algorithms/src/satisfiability/inbound/commercial.rs @@ -0,0 +1,267 @@ +/*! +Copyright 2024 Clifford Algueraz + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/inbound/inbound.rs b/tig-algorithms/src/satisfiability/inbound/inbound.rs new file mode 100644 index 0000000..72ae4d8 --- /dev/null +++ b/tig-algorithms/src/satisfiability/inbound/inbound.rs @@ -0,0 +1,267 @@ +/*! +Copyright 2024 Clifford Algueraz + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/inbound/innovator_outbound.rs b/tig-algorithms/src/satisfiability/inbound/innovator_outbound.rs new file mode 100644 index 0000000..ca5c0a3 --- /dev/null +++ b/tig-algorithms/src/satisfiability/inbound/innovator_outbound.rs @@ -0,0 +1,267 @@ +/*! +Copyright 2024 Clifford Algueraz + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/inbound/mod.rs b/tig-algorithms/src/satisfiability/inbound/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/inbound/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/inbound/open_data.rs b/tig-algorithms/src/satisfiability/inbound/open_data.rs new file mode 100644 index 0000000..75d7647 --- /dev/null +++ b/tig-algorithms/src/satisfiability/inbound/open_data.rs @@ -0,0 +1,267 @@ +/*! +Copyright 2024 Clifford Algueraz + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/mod.rs b/tig-algorithms/src/satisfiability/mod.rs index 22d6805..86b7334 100644 --- a/tig-algorithms/src/satisfiability/mod.rs +++ b/tig-algorithms/src/satisfiability/mod.rs @@ -32,7 +32,8 @@ // c001_a017 -// c001_a018 +pub mod inbound; +pub use inbound as c001_a018; // c001_a019 diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/satisfiability/inbound.wasm b/tig-algorithms/wasm/satisfiability/inbound.wasm new file mode 100644 index 0000000000000000000000000000000000000000..ca5eb20141e1239be44870f450ae5daf6e3a2f27 GIT binary patch literal 162266 zcmeFa3z%KkRp)tL_kFACmQ=DzD#_aCNVa8LiWLa5EIYAwC4R`VW9KoMKrBF?C08RueJ8tYpuOc^gVa{U>rqJd~ZB)YkK(b;rQ^a ziT#ZaM|z4ZHM%t;kC#{l>U!2&6x~`YAHy%1R#2@RQr$z=R#}16mK8sI=+L3))<$?% zi`k)2FeOKQ@fxL%#2=5-EbFF89LG^SGMdG4o5xO^*#FWbt?>}YX(P$fW-H5@Nj;8R z8I_t@nx<6BY7u|qcGjR`n#IXjL+`Yd^+?gPlKGRyajjO%I;}D>BDI#K2_?u1|7*}F zy;3$y$N7!py1I|}0f$k2#mYK8#_x*X@s1k3kJnq^%g@c7j5>`xt^eTn{op^}_rpn) z{lM)%us?b@?tI_v`}h6uJKpurf8h50(Z}M}d*jH*oqyVV^;qM5@sGz>-|_x<)yD7q zw*PVeKfPmow7qiG4ST-(fB62{8@}hCz4q#{V{!BCABgXXe<7}~dF|ETd;V45w|2$U 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You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_allocd/commercial.rs b/tig-algorithms/src/satisfiability/sat_allocd/commercial.rs new file mode 100644 index 0000000..53644aa --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_allocd/commercial.rs @@ -0,0 +1,284 @@ +/*! +Copyright 2024 AllFather + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_allocd/inbound.rs b/tig-algorithms/src/satisfiability/sat_allocd/inbound.rs new file mode 100644 index 0000000..8cfe0c9 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_allocd/inbound.rs @@ -0,0 +1,284 @@ +/*! +Copyright 2024 AllFather + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_allocd/innovator_outbound.rs b/tig-algorithms/src/satisfiability/sat_allocd/innovator_outbound.rs new file mode 100644 index 0000000..6452120 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_allocd/innovator_outbound.rs @@ -0,0 +1,284 @@ +/*! +Copyright 2024 AllFather + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_allocd/mod.rs b/tig-algorithms/src/satisfiability/sat_allocd/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_allocd/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/sat_allocd/open_data.rs b/tig-algorithms/src/satisfiability/sat_allocd/open_data.rs new file mode 100644 index 0000000..5dc5c06 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_allocd/open_data.rs @@ -0,0 +1,284 @@ +/*! +Copyright 2024 AllFather + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/satisfiability/sat_allocd.wasm b/tig-algorithms/wasm/satisfiability/sat_allocd.wasm new file mode 100644 index 0000000000000000000000000000000000000000..38f99848b5dad7ab85fe73df71e34a44535b992d GIT binary patch literal 162728 zcmeFa3z%KkRp)tL_kFACmQ=DzD#_aCNVdzi6e|#9S$1OWO8ih{$IfFifo3|U2Zm5( z(vd8bKxCBF?C08RueJ8tYpuOc^gVa{U>rqJd~ZB)TYBWkk@(1M ziT#a_M0$!WHM%V$kC#{l>U!2&6x~)UAHy%1R#2@RR^7waR#}16mK8s8`0(NAwnlhX zi`n5&FeOKQ@fxL%#2<;%EbFF89LG^SGMdG4o5xO^*#FWbt?>}YX(P$fW-H5@Nj;8R zS(@;erD;m7tQPS%Zf6Z@rdd4J&?_xvJreY+RQ{xKT&vZxPOD6eNUhNnCCCc@YtSaW zQZ`G+`Hkbcx{vq)hf#gi>N*|9?}*>})*79U*IVGr&(EKVI*mN7|KRui;6LC0!%39= zz}tV|K=g3j`M$Rw*#E~oO$9v;7o4@nh z{>KCV^p5e-_UbizZ~E^4;rr+Ie$PL9)$Xz5aq}%d5#JO4Vq9PMs@>mv;VZvy{i>;V 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zJM*?54r~he*k^{1#xKr4iuoyEJrnE~<+tDGtX~VJK7jEt`rh?11xS47;W)=wzbyYt z0c(68_(uk`2-xa7G$7P>JfrnZ^SkSJnK|G$+5a$8=T{TZ!`I?d;n&UoLw^PH$mb)b z)~}Gcf^(o2`;YPO!wmOH@p+TkAF$FV!#C6CUI3^W=A(dZU~FUtPlE) Date: Thu, 16 Jan 2025 21:05:31 +0000 Subject: [PATCH 27/49] Compiled satisfiability/sat_global --- tig-algorithms/src/satisfiability/mod.rs | 3 +- .../sat_global/benchmarker_outbound.rs | 297 ++++++++++++++++++ .../satisfiability/sat_global/commercial.rs | 297 ++++++++++++++++++ .../src/satisfiability/sat_global/inbound.rs | 297 ++++++++++++++++++ .../sat_global/innovator_outbound.rs | 297 ++++++++++++++++++ .../src/satisfiability/sat_global/mod.rs | 4 + .../satisfiability/sat_global/open_data.rs | 297 ++++++++++++++++++ tig-algorithms/src/satisfiability/template.rs | 26 +- .../wasm/satisfiability/sat_global.wasm | Bin 0 -> 162197 bytes 9 files changed, 1493 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/satisfiability/sat_global/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global/commercial.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global/inbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global/innovator_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global/mod.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global/open_data.rs create mode 100644 tig-algorithms/wasm/satisfiability/sat_global.wasm diff --git a/tig-algorithms/src/satisfiability/mod.rs b/tig-algorithms/src/satisfiability/mod.rs index 22d6805..947c0be 100644 --- a/tig-algorithms/src/satisfiability/mod.rs +++ b/tig-algorithms/src/satisfiability/mod.rs @@ -64,7 +64,8 @@ // c001_a033 -// c001_a034 +pub mod sat_global; +pub use sat_global as c001_a034; // c001_a035 diff --git a/tig-algorithms/src/satisfiability/sat_global/benchmarker_outbound.rs b/tig-algorithms/src/satisfiability/sat_global/benchmarker_outbound.rs new file mode 100644 index 0000000..03d219f --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global/benchmarker_outbound.rs @@ -0,0 +1,297 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + + + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let vad = num_p as f32 / (num_p + num_n).max(1) as f32; + + if vad >= 1.8 { + variables[v] = true; + } else if vad <= 0.56 { + variables[v] = false; + } else { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 900.0 + 1.8 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_global/commercial.rs b/tig-algorithms/src/satisfiability/sat_global/commercial.rs new file mode 100644 index 0000000..d026725 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global/commercial.rs @@ -0,0 +1,297 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + + + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let vad = num_p as f32 / (num_p + num_n).max(1) as f32; + + if vad >= 1.8 { + variables[v] = true; + } else if vad <= 0.56 { + variables[v] = false; + } else { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 900.0 + 1.8 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_global/inbound.rs b/tig-algorithms/src/satisfiability/sat_global/inbound.rs new file mode 100644 index 0000000..fd50847 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global/inbound.rs @@ -0,0 +1,297 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + + + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let vad = num_p as f32 / (num_p + num_n).max(1) as f32; + + if vad >= 1.8 { + variables[v] = true; + } else if vad <= 0.56 { + variables[v] = false; + } else { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 900.0 + 1.8 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_global/innovator_outbound.rs b/tig-algorithms/src/satisfiability/sat_global/innovator_outbound.rs new file mode 100644 index 0000000..80b6edc --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global/innovator_outbound.rs @@ -0,0 +1,297 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + + + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let vad = num_p as f32 / (num_p + num_n).max(1) as f32; + + if vad >= 1.8 { + variables[v] = true; + } else if vad <= 0.56 { + variables[v] = false; + } else { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 900.0 + 1.8 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_global/mod.rs b/tig-algorithms/src/satisfiability/sat_global/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/sat_global/open_data.rs b/tig-algorithms/src/satisfiability/sat_global/open_data.rs new file mode 100644 index 0000000..f1cbf28 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global/open_data.rs @@ -0,0 +1,297 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + + + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let vad = num_p as f32 / (num_p + num_n).max(1) as f32; + + if vad >= 1.8 { + variables[v] = true; + } else if vad <= 0.56 { + variables[v] = false; + } else { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 900.0 + 1.8 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/satisfiability/sat_global.wasm b/tig-algorithms/wasm/satisfiability/sat_global.wasm new file mode 100644 index 0000000000000000000000000000000000000000..a36133b86c4ef6c6d25d57831654192501462273 GIT binary patch literal 162197 zcmeFa3z%KkRp)tL_kFACmQ=DzD#_aCNVdzi6f2O(w(P{(mH45^j-AJ30?l+x4-BEo zxFcC8fyh!Bm5f46;|9~?fExsa$Rw`D3C@sCJ46ZwOyUH06FO$_fRh-;4e5-h6M8Zq z2KDd1_Br?7DwQmo$H#m#6UBAU+0V7tUTf{O*IIj@=)3RufjEky_}+Np*7WeTH_&((?*h|%~qB*lX@Jt zvV>aAEKO5tWwnUEaXV{JGtJ_$hF)nY>ye;mrSd0@<65njby{U&L~1Qd6H1U3{@0*Q zdZlcZj`JJGb#)){0}iA5s?~KmjNcW%;~h0RAFsE-m!F?M8FdUJ$Ub}1TSloR3kH`1KKOfiEy>{34od25dUB7DT zJsY>IyY{;4U;mTwO>h10-uBM_>HBAI`@sY6{^5gnzW(lb-47r9pX0mYABm5|3-P~- z|7H9W%=u5n_r*UQ|7iSkaW~%DUi@m~WAT65k~R)E55)&JNB+rVu1L2;$*%st!~Lg0 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tig-algorithms/src/satisfiability/mod.rs | 3 +- .../sat_global_opt/benchmarker_outbound.rs | 292 ++++++++++++++++++ .../sat_global_opt/commercial.rs | 292 ++++++++++++++++++ .../satisfiability/sat_global_opt/inbound.rs | 292 ++++++++++++++++++ .../sat_global_opt/innovator_outbound.rs | 292 ++++++++++++++++++ .../src/satisfiability/sat_global_opt/mod.rs | 4 + .../sat_global_opt/open_data.rs | 292 ++++++++++++++++++ tig-algorithms/src/satisfiability/template.rs | 26 +- .../wasm/satisfiability/sat_global_opt.wasm | Bin 0 -> 155171 bytes 9 files changed, 1468 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/satisfiability/sat_global_opt/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global_opt/commercial.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global_opt/inbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global_opt/innovator_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global_opt/mod.rs create mode 100644 tig-algorithms/src/satisfiability/sat_global_opt/open_data.rs create mode 100644 tig-algorithms/wasm/satisfiability/sat_global_opt.wasm diff --git a/tig-algorithms/src/satisfiability/mod.rs b/tig-algorithms/src/satisfiability/mod.rs index 22d6805..1cbb8b0 100644 --- a/tig-algorithms/src/satisfiability/mod.rs +++ b/tig-algorithms/src/satisfiability/mod.rs @@ -78,7 +78,8 @@ // c001_a040 -// c001_a041 +pub mod sat_global_opt; +pub use sat_global_opt as c001_a041; // c001_a042 diff --git a/tig-algorithms/src/satisfiability/sat_global_opt/benchmarker_outbound.rs b/tig-algorithms/src/satisfiability/sat_global_opt/benchmarker_outbound.rs new file mode 100644 index 0000000..bf1e166 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global_opt/benchmarker_outbound.rs @@ -0,0 +1,292 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_global_opt/commercial.rs b/tig-algorithms/src/satisfiability/sat_global_opt/commercial.rs new file mode 100644 index 0000000..1a41da5 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global_opt/commercial.rs @@ -0,0 +1,292 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_global_opt/inbound.rs b/tig-algorithms/src/satisfiability/sat_global_opt/inbound.rs new file mode 100644 index 0000000..b39f496 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global_opt/inbound.rs @@ -0,0 +1,292 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_global_opt/innovator_outbound.rs b/tig-algorithms/src/satisfiability/sat_global_opt/innovator_outbound.rs new file mode 100644 index 0000000..18bbd68 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global_opt/innovator_outbound.rs @@ -0,0 +1,292 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_global_opt/mod.rs b/tig-algorithms/src/satisfiability/sat_global_opt/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global_opt/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/sat_global_opt/open_data.rs b/tig-algorithms/src/satisfiability/sat_global_opt/open_data.rs new file mode 100644 index 0000000..eb4a1c0 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_global_opt/open_data.rs @@ -0,0 +1,292 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(0.5) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/satisfiability/sat_global_opt.wasm b/tig-algorithms/wasm/satisfiability/sat_global_opt.wasm new file mode 100644 index 0000000000000000000000000000000000000000..94b46b5734d7a5e43a416b2933a9996dc8374365 GIT binary patch literal 155171 zcmeFa3zS{gS?75k_gVK=)h(%P%PQI0=cpW)A}LnDksRA3+STKSA{(3FaffuzU>IoN zmIx_XBLXR@%8_i8#A!Uiz1nFE5g|k-v4R5$;|7P65>I0q6U=HLX7GTMxEU{Cfd?|( zW;#Uk`+xhK`>2Oy10mgO##*j>&e{9y$G5-t_w9Y6@4E8`<0y*a`{Kzv(#6Hac=3+J z{>F=ut|EIH-BF>8o2q{Jja9K`H{4kv4?-yy5$_(;yT`1pq9O&&@XE1c$D%vx;aV+K zj)jVqMK07O<>@cQpNP{+rJW{m+>A#nV=@0laXbRFYOb=6S7> zq-jd6N;R^&l?E@Rm3X|N+e%#N6s_nXC21U2tJO-gW=+Rw#M5deO{iS{SEE+SAk=W2 zPE^!+tdhSSz7zS@BP6aI`kt+ zRQZ9o{lMYq7vk3UzU}a#ANkI=|IiP-?Qrx+-29O^@^S0Cb{;);Rd=zoar}ZyZr%0P 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.../sat_optima/innovator_outbound.rs | 285 ++++++++++++++++++ .../src/satisfiability/sat_optima/mod.rs | 4 + .../satisfiability/sat_optima/open_data.rs | 285 ++++++++++++++++++ tig-algorithms/src/satisfiability/template.rs | 26 +- .../wasm/satisfiability/sat_optima.wasm | Bin 0 -> 162922 bytes 9 files changed, 1433 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/satisfiability/sat_optima/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_optima/commercial.rs create mode 100644 tig-algorithms/src/satisfiability/sat_optima/inbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_optima/innovator_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_optima/mod.rs create mode 100644 tig-algorithms/src/satisfiability/sat_optima/open_data.rs create mode 100644 tig-algorithms/wasm/satisfiability/sat_optima.wasm diff --git a/tig-algorithms/src/satisfiability/mod.rs b/tig-algorithms/src/satisfiability/mod.rs index 22d6805..9e4eaf5 100644 --- a/tig-algorithms/src/satisfiability/mod.rs +++ b/tig-algorithms/src/satisfiability/mod.rs @@ -58,7 +58,8 @@ // c001_a030 -// c001_a031 +pub mod sat_optima; +pub use sat_optima as c001_a031; // c001_a032 diff --git a/tig-algorithms/src/satisfiability/sat_optima/benchmarker_outbound.rs b/tig-algorithms/src/satisfiability/sat_optima/benchmarker_outbound.rs new file mode 100644 index 0000000..6a20e5b --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_optima/benchmarker_outbound.rs @@ -0,0 +1,285 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + + let i = residual_[rng.gen_range(0..residual_.len())]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_optima/commercial.rs b/tig-algorithms/src/satisfiability/sat_optima/commercial.rs new file mode 100644 index 0000000..1c4e87a --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_optima/commercial.rs @@ -0,0 +1,285 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + + let i = residual_[rng.gen_range(0..residual_.len())]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_optima/inbound.rs b/tig-algorithms/src/satisfiability/sat_optima/inbound.rs new file mode 100644 index 0000000..4d56d06 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_optima/inbound.rs @@ -0,0 +1,285 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + + let i = residual_[rng.gen_range(0..residual_.len())]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_optima/innovator_outbound.rs b/tig-algorithms/src/satisfiability/sat_optima/innovator_outbound.rs new file mode 100644 index 0000000..d718930 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_optima/innovator_outbound.rs @@ -0,0 +1,285 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + + let i = residual_[rng.gen_range(0..residual_.len())]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_optima/mod.rs b/tig-algorithms/src/satisfiability/sat_optima/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_optima/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/sat_optima/open_data.rs b/tig-algorithms/src/satisfiability/sat_optima/open_data.rs new file mode 100644 index 0000000..19eda8b --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_optima/open_data.rs @@ -0,0 +1,285 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + loop { + if !residual_.is_empty() { + + let i = residual_[rng.gen_range(0..residual_.len())]; + let mut min_sad = clauses.len(); + let mut v_min_sad = Vec::with_capacity(clauses[i].len()); // Preallocate with capacity + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad.clear(); + v_min_sad.push((l.abs() - 1) as usize); + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..(c.len() as u32)) as usize]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..(v_min_sad.len() as u32)) as usize] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= num_variables * 35 { + return Ok(None); + } + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/satisfiability/sat_optima.wasm b/tig-algorithms/wasm/satisfiability/sat_optima.wasm new file mode 100644 index 0000000000000000000000000000000000000000..22350fe777bf4028f1fdb58326e1e394c20a3f21 GIT binary patch literal 162922 zcmeFa3z%KkRp)tL_kFACmQ=DzD#_aCNVdzi6e|#9S$1OWO8ih{$IfFifo3|U2Zm5( z(vd8bKxCBF?C08RueJ8tYpuOc^gVa{U>rqJd~ZB)TYBWkk@(1M ziT#a_M0$!WHM%V$kC#{l>U!2&6x~)UAHy%1R#2@RR^7waR#}16mK8s8`0(NAwnlhX zi`n5&FeOKQ@fxL%#2<;%EbFF89LG^SGMdG4o5xO^*#FWbt?>}YX(P$fW-H5@Nj;8R zS(@;erD;m7tQPS%Zf6Z@rdd4J&?_xvJreY+RQ{xKT&vZxPOD6eNUhNnCCCc@YtSaW zQZ`G+`Hkbcx{vq)hf#gi>N*|9?}*>})*79U*IVGr&(EKVI*mN7|KRui;6LC0!%39= zz}tV|K=g3j`M$Rw*#E~oO$9v;7o4@nh z{>KCV^p5e-_UbizZ~E^4;rr+Ie$PL9)$Xz5aq}%d5#JO4Vq9PMs@>mv;VZvy{i>;V 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zCC%ywX1@qwUZFcP^_N48hqPu~8n3IOU(y*G_`q3DJ7Vgq#Y}w(%aAtce4);%fUc_I z7Y>Y^UqRIS1cA}kQ@H3`4Yu*gc)%$JP#hSSHp}X<)P2}*SGcyLlCwR^t%G|gp3Ey& zWfY^{@`?@QT&8e+&!wHa^JcYuirPT6b$6RrEwPwaDu_&m^(DQ_qwV>1Am%b|am6Z_ zuJ03!1}cV!_Ux%SGcT{97<#XV?_F%r01a5M^+x*xXR3igKoj?@T%w>QlxF2%n7ZI?#O7R2r1yZEaX^Xw3EK9OrI)X$^AvWnr|9t)@X zgUvZIoeM2bKj$)x+TqnJDMssBcDR-t{5>j_`=)!g;2d_+^>n>F7Ls9!QNw;Oz%EE4}7jOM|~#xo@AI{Uuwt6-jb?O>{W z@|YVq7ix*`DBqsUP_IO<#mo`Em0l^{sa}u#K+iBA1#AIhBQtag-kjl&srG6LX<={_ z;Ki(W6BABQbzoWT<(r9*jF=WqL1HvSx630gR6;Kk{u`*GtNGuQ*+wJAHr08gzV^-|Q7r|5&Z8DAF{zW12H}F-PKrGeA z8l=*gSOdNj*C$E~C2_cpG05 Date: Thu, 16 Jan 2025 21:05:54 +0000 Subject: [PATCH 30/49] Compiled satisfiability/schnoing --- tig-algorithms/src/satisfiability/mod.rs | 3 +- .../schnoing/benchmarker_outbound.rs | 87 ++++++++++ .../src/satisfiability/schnoing/commercial.rs | 87 ++++++++++ .../satisfiability/schnoing/cuda_example.rs | 156 ++++++++++++++++++ .../src/satisfiability/schnoing/inbound.rs | 87 ++++++++++ .../schnoing/innovator_outbound.rs | 87 ++++++++++ .../src/satisfiability/schnoing/mod.rs | 4 + .../src/satisfiability/schnoing/open_data.rs | 87 ++++++++++ tig-algorithms/src/satisfiability/template.rs | 26 +-- .../wasm/satisfiability/schnoing.wasm | Bin 0 -> 152268 bytes 10 files changed, 599 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/satisfiability/schnoing/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/schnoing/commercial.rs create mode 100644 tig-algorithms/src/satisfiability/schnoing/cuda_example.rs create mode 100644 tig-algorithms/src/satisfiability/schnoing/inbound.rs create mode 100644 tig-algorithms/src/satisfiability/schnoing/innovator_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/schnoing/mod.rs create mode 100644 tig-algorithms/src/satisfiability/schnoing/open_data.rs create mode 100644 tig-algorithms/wasm/satisfiability/schnoing.wasm diff --git a/tig-algorithms/src/satisfiability/mod.rs b/tig-algorithms/src/satisfiability/mod.rs index 22d6805..e6bcd84 100644 --- a/tig-algorithms/src/satisfiability/mod.rs +++ b/tig-algorithms/src/satisfiability/mod.rs @@ -1,4 +1,5 @@ -// c001_a001 +pub mod schnoing; +pub use schnoing as c001_a001; // c001_a002 diff --git a/tig-algorithms/src/satisfiability/schnoing/benchmarker_outbound.rs b/tig-algorithms/src/satisfiability/schnoing/benchmarker_outbound.rs new file mode 100644 index 0000000..37de9a2 --- /dev/null +++ b/tig-algorithms/src/satisfiability/schnoing/benchmarker_outbound.rs @@ -0,0 +1,87 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + // Pre-generate a bunch of random integers + // IMPORTANT! When generating random numbers, never use usize! usize bytes varies from system to system + let rand_ints: Vec = (0..2 * num_variables) + .map(|_| rng.gen_range(0..1_000_000_000u32) as usize) + .collect(); + + for i in 0..num_variables { + // Evaluate clauses and find any that are unsatisfied + let substituted: Vec = challenge + .clauses + .iter() + .map(|clause| { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + let var_value = variables[var_idx]; + (literal > 0 && var_value) || (literal < 0 && !var_value) + }) + }) + .collect(); + + let unsatisfied_clauses: Vec = substituted + .iter() + .enumerate() + .filter_map(|(idx, &satisfied)| if !satisfied { Some(idx) } else { None }) + .collect(); + + let num_unsatisfied_clauses = unsatisfied_clauses.len(); + if num_unsatisfied_clauses == 0 { + break; + } + + // Flip the value of a random variable from a random unsatisfied clause + let rand_unsatisfied_clause_idx = rand_ints[2 * i] % num_unsatisfied_clauses; + let rand_unsatisfied_clause = unsatisfied_clauses[rand_unsatisfied_clause_idx]; + let rand_variable_idx = rand_ints[2 * i + 1] % 3; + let rand_variable = + challenge.clauses[rand_unsatisfied_clause][rand_variable_idx].abs() as usize - 1; + variables[rand_variable] = !variables[rand_variable]; + } + + Ok(Some(Solution { variables })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/schnoing/commercial.rs b/tig-algorithms/src/satisfiability/schnoing/commercial.rs new file mode 100644 index 0000000..fccc582 --- /dev/null +++ b/tig-algorithms/src/satisfiability/schnoing/commercial.rs @@ -0,0 +1,87 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + // Pre-generate a bunch of random integers + // IMPORTANT! When generating random numbers, never use usize! usize bytes varies from system to system + let rand_ints: Vec = (0..2 * num_variables) + .map(|_| rng.gen_range(0..1_000_000_000u32) as usize) + .collect(); + + for i in 0..num_variables { + // Evaluate clauses and find any that are unsatisfied + let substituted: Vec = challenge + .clauses + .iter() + .map(|clause| { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + let var_value = variables[var_idx]; + (literal > 0 && var_value) || (literal < 0 && !var_value) + }) + }) + .collect(); + + let unsatisfied_clauses: Vec = substituted + .iter() + .enumerate() + .filter_map(|(idx, &satisfied)| if !satisfied { Some(idx) } else { None }) + .collect(); + + let num_unsatisfied_clauses = unsatisfied_clauses.len(); + if num_unsatisfied_clauses == 0 { + break; + } + + // Flip the value of a random variable from a random unsatisfied clause + let rand_unsatisfied_clause_idx = rand_ints[2 * i] % num_unsatisfied_clauses; + let rand_unsatisfied_clause = unsatisfied_clauses[rand_unsatisfied_clause_idx]; + let rand_variable_idx = rand_ints[2 * i + 1] % 3; + let rand_variable = + challenge.clauses[rand_unsatisfied_clause][rand_variable_idx].abs() as usize - 1; + variables[rand_variable] = !variables[rand_variable]; + } + + Ok(Some(Solution { variables })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/schnoing/cuda_example.rs b/tig-algorithms/src/satisfiability/schnoing/cuda_example.rs new file mode 100644 index 0000000..908b9ed --- /dev/null +++ b/tig-algorithms/src/satisfiability/schnoing/cuda_example.rs @@ -0,0 +1,156 @@ +/*! +Copyright 2024 TIG Foundation + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(challenge.seeds[0] as u64); + let num_variables = challenge.difficulty.num_variables; + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + // Pre-generate a bunch of random integers + // IMPORTANT! When generating random numbers, never use usize! usize bytes varies from system to system + let rand_ints: Vec = (0..2 * num_variables) + .map(|_| rng.gen_range(0..1_000_000_000u32) as usize) + .collect(); + + for i in 0..num_variables { + // Evaluate clauses and find any that are unsatisfied + let substituted: Vec = challenge + .clauses + .iter() + .map(|clause| { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + let var_value = variables[var_idx]; + (literal > 0 && var_value) || (literal < 0 && !var_value) + }) + }) + .collect(); + + let unsatisfied_clauses: Vec = substituted + .iter() + .enumerate() + .filter_map(|(idx, &satisfied)| if !satisfied { Some(idx) } else { None }) + .collect(); + + let num_unsatisfied_clauses = unsatisfied_clauses.len(); + if num_unsatisfied_clauses == 0 { + break; + } + + // Flip the value of a random variable from a random unsatisfied clause + let rand_unsatisfied_clause_idx = rand_ints[2 * i] % num_unsatisfied_clauses; + let rand_unsatisfied_clause = unsatisfied_clauses[rand_unsatisfied_clause_idx]; + let rand_variable_idx = rand_ints[2 * i + 1] % 3; + let rand_variable = + challenge.clauses[rand_unsatisfied_clause][rand_variable_idx].abs() as usize - 1; + variables[rand_variable] = !variables[rand_variable]; + } + + Ok(Some(Solution { variables })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = Some(CudaKernel { + // Example CUDA code from https://github.com/coreylowman/cudarc/blob/main/examples/matmul-kernel.rs + src: r#" +extern "C" __global__ void matmul(float* A, float* B, float* C, int N) { + int ROW = blockIdx.y*blockDim.y+threadIdx.y; + int COL = blockIdx.x*blockDim.x+threadIdx.x; + + float tmpSum = 0; + + if (ROW < N && COL < N) { + // each thread computes one element of the block sub-matrix + for (int i = 0; i < N; i++) { + tmpSum += A[ROW * N + i] * B[i * N + COL]; + } + } + C[ROW * N + COL] = tmpSum; +} +"#, + funcs: &["matmul"], + }); + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + // Example CUDA code from https://github.com/coreylowman/cudarc/blob/main/examples/matmul-kernel.rs + let start = std::time::Instant::now(); + + let a_host = [1.0f32, 2.0, 3.0, 4.0]; + let b_host = [1.0f32, 2.0, 3.0, 4.0]; + let mut c_host = [0.0f32; 4]; + + let a_dev = dev.htod_sync_copy(&a_host)?; + let b_dev = dev.htod_sync_copy(&b_host)?; + let mut c_dev = dev.htod_sync_copy(&c_host)?; + + println!("Copied in {:?}", start.elapsed()); + + let cfg = LaunchConfig { + block_dim: (2, 2, 1), + grid_dim: (1, 1, 1), + shared_mem_bytes: 0, + }; + unsafe { + funcs + .remove("matmul") + .unwrap() + .launch(cfg, (&a_dev, &b_dev, &mut c_dev, 2i32)) + }?; + + dev.dtoh_sync_copy_into(&c_dev, &mut c_host)?; + println!("Found {:?} in {:?}", c_host, start.elapsed()); + + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/schnoing/inbound.rs b/tig-algorithms/src/satisfiability/schnoing/inbound.rs new file mode 100644 index 0000000..60efa83 --- /dev/null +++ b/tig-algorithms/src/satisfiability/schnoing/inbound.rs @@ -0,0 +1,87 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + // Pre-generate a bunch of random integers + // IMPORTANT! When generating random numbers, never use usize! usize bytes varies from system to system + let rand_ints: Vec = (0..2 * num_variables) + .map(|_| rng.gen_range(0..1_000_000_000u32) as usize) + .collect(); + + for i in 0..num_variables { + // Evaluate clauses and find any that are unsatisfied + let substituted: Vec = challenge + .clauses + .iter() + .map(|clause| { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + let var_value = variables[var_idx]; + (literal > 0 && var_value) || (literal < 0 && !var_value) + }) + }) + .collect(); + + let unsatisfied_clauses: Vec = substituted + .iter() + .enumerate() + .filter_map(|(idx, &satisfied)| if !satisfied { Some(idx) } else { None }) + .collect(); + + let num_unsatisfied_clauses = unsatisfied_clauses.len(); + if num_unsatisfied_clauses == 0 { + break; + } + + // Flip the value of a random variable from a random unsatisfied clause + let rand_unsatisfied_clause_idx = rand_ints[2 * i] % num_unsatisfied_clauses; + let rand_unsatisfied_clause = unsatisfied_clauses[rand_unsatisfied_clause_idx]; + let rand_variable_idx = rand_ints[2 * i + 1] % 3; + let rand_variable = + challenge.clauses[rand_unsatisfied_clause][rand_variable_idx].abs() as usize - 1; + variables[rand_variable] = !variables[rand_variable]; + } + + Ok(Some(Solution { variables })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/schnoing/innovator_outbound.rs b/tig-algorithms/src/satisfiability/schnoing/innovator_outbound.rs new file mode 100644 index 0000000..d925304 --- /dev/null +++ b/tig-algorithms/src/satisfiability/schnoing/innovator_outbound.rs @@ -0,0 +1,87 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + // Pre-generate a bunch of random integers + // IMPORTANT! When generating random numbers, never use usize! usize bytes varies from system to system + let rand_ints: Vec = (0..2 * num_variables) + .map(|_| rng.gen_range(0..1_000_000_000u32) as usize) + .collect(); + + for i in 0..num_variables { + // Evaluate clauses and find any that are unsatisfied + let substituted: Vec = challenge + .clauses + .iter() + .map(|clause| { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + let var_value = variables[var_idx]; + (literal > 0 && var_value) || (literal < 0 && !var_value) + }) + }) + .collect(); + + let unsatisfied_clauses: Vec = substituted + .iter() + .enumerate() + .filter_map(|(idx, &satisfied)| if !satisfied { Some(idx) } else { None }) + .collect(); + + let num_unsatisfied_clauses = unsatisfied_clauses.len(); + if num_unsatisfied_clauses == 0 { + break; + } + + // Flip the value of a random variable from a random unsatisfied clause + let rand_unsatisfied_clause_idx = rand_ints[2 * i] % num_unsatisfied_clauses; + let rand_unsatisfied_clause = unsatisfied_clauses[rand_unsatisfied_clause_idx]; + let rand_variable_idx = rand_ints[2 * i + 1] % 3; + let rand_variable = + challenge.clauses[rand_unsatisfied_clause][rand_variable_idx].abs() as usize - 1; + variables[rand_variable] = !variables[rand_variable]; + } + + Ok(Some(Solution { variables })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/schnoing/mod.rs b/tig-algorithms/src/satisfiability/schnoing/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/schnoing/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/schnoing/open_data.rs b/tig-algorithms/src/satisfiability/schnoing/open_data.rs new file mode 100644 index 0000000..93bef13 --- /dev/null +++ b/tig-algorithms/src/satisfiability/schnoing/open_data.rs @@ -0,0 +1,87 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + // Pre-generate a bunch of random integers + // IMPORTANT! When generating random numbers, never use usize! usize bytes varies from system to system + let rand_ints: Vec = (0..2 * num_variables) + .map(|_| rng.gen_range(0..1_000_000_000u32) as usize) + .collect(); + + for i in 0..num_variables { + // Evaluate clauses and find any that are unsatisfied + let substituted: Vec = challenge + .clauses + .iter() + .map(|clause| { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + let var_value = variables[var_idx]; + (literal > 0 && var_value) || (literal < 0 && !var_value) + }) + }) + .collect(); + + let unsatisfied_clauses: Vec = substituted + .iter() + .enumerate() + .filter_map(|(idx, &satisfied)| if !satisfied { Some(idx) } else { None }) + .collect(); + + let num_unsatisfied_clauses = unsatisfied_clauses.len(); + if num_unsatisfied_clauses == 0 { + break; + } + + // Flip the value of a random variable from a random unsatisfied clause + let rand_unsatisfied_clause_idx = rand_ints[2 * i] % num_unsatisfied_clauses; + let rand_unsatisfied_clause = unsatisfied_clauses[rand_unsatisfied_clause_idx]; + let rand_variable_idx = rand_ints[2 * i + 1] % 3; + let rand_variable = + challenge.clauses[rand_unsatisfied_clause][rand_variable_idx].abs() as usize - 1; + variables[rand_variable] = !variables[rand_variable]; + } + + Ok(Some(Solution { variables })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/satisfiability/schnoing.wasm b/tig-algorithms/wasm/satisfiability/schnoing.wasm new file mode 100644 index 0000000000000000000000000000000000000000..4d572671c884d4d3f9b03d0085d01aa3099f5d2e GIT binary patch literal 152268 zcmeFa3z%Kib?12=_x&i{k_r%%p!PW`Ko2k?#*z@&XqOE_$QW$XjN{3)Nl!9ystB=! zMs~&}sfbEMwsFEFWImjbL`fWrw5bkG$h4E-SaOquB*bAR&e)yM35n?pop?M6(Ula-#WkYrA(cI3brl6jXj+NGhYlTz?y84-)mS+c zGNz=cEgqxvpT&>GX{FLhlQ@o}xI0pb<0iMQII;iIB(0K^){{!wZd5Amq!!1GS|v?W zvRA4Rzj3otr$}0flT~#+Q%9x88{O+QKWQ9StJO-YQM`p z$3GI^7e5?#;wxG|{FC*^;(s%l)(Zry(?uJS9$lQ9 z`_R34;;W8-yT^SzGg6I{YFdffF5Zwtxqful$fFsb-dfq|Vi$P^!Cq#{rswIcNkeZc zE}HgV|Lf0uwYD{iIwPTyj4W41(TyrRTwSHCt~y*@s05h)@c7ASLljXFz*VOGAMs2z 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RkNE;?_qGlvNsY0L`5#gfATj^| literal 0 HcmV?d00001 From efa645e3f63c9ba7f7d7e7922f9c2612377f0ce5 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:05:56 +0000 Subject: [PATCH 31/49] Compiled satisfiability/sprint_sat --- tig-algorithms/src/satisfiability/mod.rs | 3 +- .../sprint_sat/benchmarker_outbound.rs | 267 ++++++++++++++++++ .../satisfiability/sprint_sat/commercial.rs | 267 ++++++++++++++++++ .../src/satisfiability/sprint_sat/inbound.rs | 267 ++++++++++++++++++ .../sprint_sat/innovator_outbound.rs | 267 ++++++++++++++++++ .../src/satisfiability/sprint_sat/mod.rs | 4 + .../satisfiability/sprint_sat/open_data.rs | 267 ++++++++++++++++++ tig-algorithms/src/satisfiability/template.rs | 26 +- .../wasm/satisfiability/sprint_sat.wasm | Bin 0 -> 162266 bytes 9 files changed, 1343 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/satisfiability/sprint_sat/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sprint_sat/commercial.rs create mode 100644 tig-algorithms/src/satisfiability/sprint_sat/inbound.rs create mode 100644 tig-algorithms/src/satisfiability/sprint_sat/innovator_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sprint_sat/mod.rs create mode 100644 tig-algorithms/src/satisfiability/sprint_sat/open_data.rs create mode 100644 tig-algorithms/wasm/satisfiability/sprint_sat.wasm diff --git a/tig-algorithms/src/satisfiability/mod.rs b/tig-algorithms/src/satisfiability/mod.rs index 22d6805..0b1d5ba 100644 --- a/tig-algorithms/src/satisfiability/mod.rs +++ b/tig-algorithms/src/satisfiability/mod.rs @@ -20,7 +20,8 @@ // c001_a011 -// c001_a012 +pub mod sprint_sat; +pub use sprint_sat as c001_a012; // c001_a013 diff --git a/tig-algorithms/src/satisfiability/sprint_sat/benchmarker_outbound.rs b/tig-algorithms/src/satisfiability/sprint_sat/benchmarker_outbound.rs new file mode 100644 index 0000000..648e969 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sprint_sat/benchmarker_outbound.rs @@ -0,0 +1,267 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sprint_sat/commercial.rs b/tig-algorithms/src/satisfiability/sprint_sat/commercial.rs new file mode 100644 index 0000000..24e2b1a --- /dev/null +++ b/tig-algorithms/src/satisfiability/sprint_sat/commercial.rs @@ -0,0 +1,267 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sprint_sat/inbound.rs b/tig-algorithms/src/satisfiability/sprint_sat/inbound.rs new file mode 100644 index 0000000..18ade15 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sprint_sat/inbound.rs @@ -0,0 +1,267 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sprint_sat/innovator_outbound.rs b/tig-algorithms/src/satisfiability/sprint_sat/innovator_outbound.rs new file mode 100644 index 0000000..e71c8bb --- /dev/null +++ b/tig-algorithms/src/satisfiability/sprint_sat/innovator_outbound.rs @@ -0,0 +1,267 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sprint_sat/mod.rs b/tig-algorithms/src/satisfiability/sprint_sat/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sprint_sat/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/sprint_sat/open_data.rs b/tig-algorithms/src/satisfiability/sprint_sat/open_data.rs new file mode 100644 index 0000000..61e8321 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sprint_sat/open_data.rs @@ -0,0 +1,267 @@ +/*! +Copyright 2024 Dominic Kennedy + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::StdRng, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![vec![]; num_variables]; + let mut n_clauses: Vec> = vec![vec![]; num_variables]; + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + if p_single[v] { + variables[v] = true + } else if n_single[v] { + variables[v] = false + } else { + variables[v] = rng.gen_bool(0.5) + } + } + let mut num_good_so_far: Vec = vec![0; num_clauses]; + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + if variables[var] { + num_good_so_far[i] += 1 + } + } else { + n_clauses[var].push(i); + if !variables[var] { + num_good_so_far[i] += 1 + } + } + } + } + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = HashMap::with_capacity(num_clauses); + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices.insert(i, residual_.len() - 1); + } + } + + let mut attempts = 0; + loop { + if attempts >= num_variables * 25 { + return Ok(None); + } + if !residual_.is_empty() { + let i = residual_[0]; + let mut min_sad = clauses.len(); + let mut v_min_sad = vec![]; + let c = &clauses[i]; + for &l in c { + let mut sad = 0 as usize; + if variables[(l.abs() - 1) as usize] { + for &c in &p_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } else { + for &c in &n_clauses[(l.abs() - 1) as usize] { + if num_good_so_far[c] == 1 { + sad += 1; + if sad > min_sad { + break; + } + } + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = vec![(l.abs() - 1) as usize]; + } else if sad == min_sad { + v_min_sad.push((l.abs() - 1) as usize); + } + } + let v = if min_sad == 0 { + if v_min_sad.len() == 1 { + v_min_sad[0] + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + } else { + if rng.gen_bool(0.5) { + let l = c[rng.gen_range(0..c.len())]; + (l.abs() - 1) as usize + } else { + v_min_sad[rng.gen_range(0..v_min_sad.len())] + } + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices.insert(c, residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices.remove(&c).unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices.insert(last, i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + attempts += 1; + } + + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/satisfiability/sprint_sat.wasm b/tig-algorithms/wasm/satisfiability/sprint_sat.wasm new file mode 100644 index 0000000000000000000000000000000000000000..38fb4ad39afe81c64e15e0114ebcd959df1dc37a GIT binary patch literal 162266 zcmeFa3z%KkRp)tL_kFACmQ=DzD#_aCNVa8LiWLa5EIYAwC4R`VW9KoMKrBF?C08RueJ8tYpuOc^gVa{U>rqJd~ZB)YkK(b;rQ^a ziT#ZaM|z4ZHM%t;kC#{l>U!2&6x~`YAHy%1R#2@RQr$z=R#}16mK8sI=+L3))<$?% zi`k)2FeOKQ@fxL%#2=5-EbFF89LG^SGMdG4o5xO^*#FWbt?>}YX(P$fW-H5@Nj;8R z8I_t@nx<6BY7u|qcGjR`n#IXjL+`Yd^+?gPlKGRyajjO%I;}D>BDI#K2_?u1|7*}F zy;3$y$N7!py1I|}0f$k2#mYK8#_x*X@s1k3kJnq^%g@c7j5>`xt^eTn{op^}_rpn) z{lM)%us?b@?tI_v`}h6uJKpurf8h50(Z}M}d*jH*oqyVV^;qM5@sGz>-|_x<)yD7q zw*PVeKfPmow7qiG4ST-(fB62{8@}hCz4q#{V{!BCABgXXe<7}~dF|ETd;V45w|2$U 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satisfiability/walk_sat --- tig-algorithms/src/satisfiability/mod.rs | 3 +- tig-algorithms/src/satisfiability/template.rs | 26 +----- .../walk_sat/benchmarker_outbound.rs | 76 ++++++++++++++++++ .../src/satisfiability/walk_sat/commercial.rs | 76 ++++++++++++++++++ .../src/satisfiability/walk_sat/inbound.rs | 76 ++++++++++++++++++ .../walk_sat/innovator_outbound.rs | 76 ++++++++++++++++++ .../src/satisfiability/walk_sat/mod.rs | 4 + .../src/satisfiability/walk_sat/open_data.rs | 76 ++++++++++++++++++ .../wasm/satisfiability/walk_sat.wasm | Bin 0 -> 152453 bytes 9 files changed, 388 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/satisfiability/walk_sat/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/walk_sat/commercial.rs create mode 100644 tig-algorithms/src/satisfiability/walk_sat/inbound.rs create mode 100644 tig-algorithms/src/satisfiability/walk_sat/innovator_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/walk_sat/mod.rs create mode 100644 tig-algorithms/src/satisfiability/walk_sat/open_data.rs create mode 100644 tig-algorithms/wasm/satisfiability/walk_sat.wasm diff --git a/tig-algorithms/src/satisfiability/mod.rs b/tig-algorithms/src/satisfiability/mod.rs index 22d6805..4e5d854 100644 --- a/tig-algorithms/src/satisfiability/mod.rs +++ b/tig-algorithms/src/satisfiability/mod.rs @@ -6,7 +6,8 @@ // c001_a004 -// c001_a005 +pub mod walk_sat; +pub use walk_sat as c001_a005; // c001_a006 diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/src/satisfiability/walk_sat/benchmarker_outbound.rs b/tig-algorithms/src/satisfiability/walk_sat/benchmarker_outbound.rs new file mode 100644 index 0000000..68650eb --- /dev/null +++ b/tig-algorithms/src/satisfiability/walk_sat/benchmarker_outbound.rs @@ -0,0 +1,76 @@ +/*! +Copyright 2024 Chad Blanchard + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use rand::prelude::*; +use rand::rngs::StdRng; +use rand::SeedableRng; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let max_flips = 1000; + + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + for _ in 0..max_flips { + let mut unsatisfied_clauses: Vec<&Vec> = challenge + .clauses + .iter() + .filter(|clause| !clause_satisfied(clause, &variables)) + .collect(); + + if unsatisfied_clauses.is_empty() { + return Ok(Some(Solution { variables })); + } + + let clause = unsatisfied_clauses.choose(&mut rng).unwrap(); + let literal = clause.choose(&mut rng).unwrap(); + let var_idx = literal.abs() as usize - 1; + variables[var_idx] = !variables[var_idx]; + } + + Ok(None) +} + +fn clause_satisfied(clause: &Vec, variables: &[bool]) -> bool { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + (literal > 0 && variables[var_idx]) || (literal < 0 && !variables[var_idx]) + }) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/walk_sat/commercial.rs b/tig-algorithms/src/satisfiability/walk_sat/commercial.rs new file mode 100644 index 0000000..9b49cbe --- /dev/null +++ b/tig-algorithms/src/satisfiability/walk_sat/commercial.rs @@ -0,0 +1,76 @@ +/*! +Copyright 2024 Chad Blanchard + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use rand::prelude::*; +use rand::rngs::StdRng; +use rand::SeedableRng; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let max_flips = 1000; + + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + for _ in 0..max_flips { + let mut unsatisfied_clauses: Vec<&Vec> = challenge + .clauses + .iter() + .filter(|clause| !clause_satisfied(clause, &variables)) + .collect(); + + if unsatisfied_clauses.is_empty() { + return Ok(Some(Solution { variables })); + } + + let clause = unsatisfied_clauses.choose(&mut rng).unwrap(); + let literal = clause.choose(&mut rng).unwrap(); + let var_idx = literal.abs() as usize - 1; + variables[var_idx] = !variables[var_idx]; + } + + Ok(None) +} + +fn clause_satisfied(clause: &Vec, variables: &[bool]) -> bool { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + (literal > 0 && variables[var_idx]) || (literal < 0 && !variables[var_idx]) + }) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/walk_sat/inbound.rs b/tig-algorithms/src/satisfiability/walk_sat/inbound.rs new file mode 100644 index 0000000..9f6ebfe --- /dev/null +++ b/tig-algorithms/src/satisfiability/walk_sat/inbound.rs @@ -0,0 +1,76 @@ +/*! +Copyright 2024 Chad Blanchard + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use rand::prelude::*; +use rand::rngs::StdRng; +use rand::SeedableRng; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let max_flips = 1000; + + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + for _ in 0..max_flips { + let mut unsatisfied_clauses: Vec<&Vec> = challenge + .clauses + .iter() + .filter(|clause| !clause_satisfied(clause, &variables)) + .collect(); + + if unsatisfied_clauses.is_empty() { + return Ok(Some(Solution { variables })); + } + + let clause = unsatisfied_clauses.choose(&mut rng).unwrap(); + let literal = clause.choose(&mut rng).unwrap(); + let var_idx = literal.abs() as usize - 1; + variables[var_idx] = !variables[var_idx]; + } + + Ok(None) +} + +fn clause_satisfied(clause: &Vec, variables: &[bool]) -> bool { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + (literal > 0 && variables[var_idx]) || (literal < 0 && !variables[var_idx]) + }) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/walk_sat/innovator_outbound.rs b/tig-algorithms/src/satisfiability/walk_sat/innovator_outbound.rs new file mode 100644 index 0000000..2eba628 --- /dev/null +++ b/tig-algorithms/src/satisfiability/walk_sat/innovator_outbound.rs @@ -0,0 +1,76 @@ +/*! +Copyright 2024 Chad Blanchard + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use rand::prelude::*; +use rand::rngs::StdRng; +use rand::SeedableRng; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let max_flips = 1000; + + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + for _ in 0..max_flips { + let mut unsatisfied_clauses: Vec<&Vec> = challenge + .clauses + .iter() + .filter(|clause| !clause_satisfied(clause, &variables)) + .collect(); + + if unsatisfied_clauses.is_empty() { + return Ok(Some(Solution { variables })); + } + + let clause = unsatisfied_clauses.choose(&mut rng).unwrap(); + let literal = clause.choose(&mut rng).unwrap(); + let var_idx = literal.abs() as usize - 1; + variables[var_idx] = !variables[var_idx]; + } + + Ok(None) +} + +fn clause_satisfied(clause: &Vec, variables: &[bool]) -> bool { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + (literal > 0 && variables[var_idx]) || (literal < 0 && !variables[var_idx]) + }) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/walk_sat/mod.rs b/tig-algorithms/src/satisfiability/walk_sat/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/walk_sat/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/walk_sat/open_data.rs b/tig-algorithms/src/satisfiability/walk_sat/open_data.rs new file mode 100644 index 0000000..4bdcf81 --- /dev/null +++ b/tig-algorithms/src/satisfiability/walk_sat/open_data.rs @@ -0,0 +1,76 @@ +/*! +Copyright 2024 Chad Blanchard + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +// TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge +use rand::prelude::*; +use rand::rngs::StdRng; +use rand::SeedableRng; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + let num_variables = challenge.difficulty.num_variables; + let max_flips = 1000; + + let mut variables: Vec = (0..num_variables).map(|_| rng.gen::()).collect(); + + for _ in 0..max_flips { + let mut unsatisfied_clauses: Vec<&Vec> = challenge + .clauses + .iter() + .filter(|clause| !clause_satisfied(clause, &variables)) + .collect(); + + if unsatisfied_clauses.is_empty() { + return Ok(Some(Solution { variables })); + } + + let clause = unsatisfied_clauses.choose(&mut rng).unwrap(); + let literal = clause.choose(&mut rng).unwrap(); + let var_idx = literal.abs() as usize - 1; + variables[var_idx] = !variables[var_idx]; + } + + Ok(None) +} + +fn clause_satisfied(clause: &Vec, variables: &[bool]) -> bool { + clause.iter().any(|&literal| { + let var_idx = literal.abs() as usize - 1; + (literal > 0 && variables[var_idx]) || (literal < 0 && !variables[var_idx]) + }) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/wasm/satisfiability/walk_sat.wasm b/tig-algorithms/wasm/satisfiability/walk_sat.wasm new file mode 100644 index 0000000000000000000000000000000000000000..e0a7d8d83a05eaf0beda08e01d92a2ad1571a6ed GIT binary patch literal 152453 zcmeFa3z%Kib?12=_x&i{k_r%%p!PW`Ko2k?#*z@&XqOE_$QW$XjN{3)Nl!9ystB=! zMs~&}sfbEMwsD6^$b2{6(Ula-#WkYrA(cI3brl6jXj+NGhYlTz?y84-)mS+c zGNz=cEgqxvpU02KX{FLhlQ@o}xI0pb<0iMQII;iIB(0K^){{!wZd5Amq!!1GS|v?W zvRA4Rzj3otr$}0flT~#+Q%9x88{O+QKWQ9StJO-YQM`}AAL%IS$TuG(=9-(1|$hB4Cc&85+_mHj|udRX(Mk=J^01w z4m!}xTC|z&bg6$VnRfAXPVZ-&rO)N9sh2*T9&*ju6TpA^bh_8S5Z#kke7xU3|BI)h z>8qmXI+~AsGUF=#k;8YpWIE(qk|U;4+HOvrKK13R?#v?B%9<|fMr{BdK-G5bsT+PM zZyt5cgNs4zd@Dh0x09VN+6>x8-RO6Mw9(H3-Rbhb=*X1lj{jac9lfg?w_R0K!A~QP z3nHsJFaTBPWu_c@HX`Fq1+)){j1lZtUO$qxc)I4;2}54=?rycyY->;(Syh)xMqygZ zHO}@vPi_K2kyuQ7mNTJ*o~QGg$XdlTPJb-Yqi0p}SN)c|{g#=m3MEiu50aun#;JN^ zH(_YEOw*7m%M)FqmD?C@;R?|tqj9K@0frtWa-WpB)l{=!xAgrv`N!u(LSq<{3i7y7UT1u>`TcUJ_QG{?$Hy~Ie zwaD+B$!o506QmaTSEB+kk$)xJGFd>{Ya{V9$j>lSb(JOkszKWVvfnqTv)?)TW8`Yh zS_S;DVrXa}L>n5tDVK{t0KBo~!UzC_z=03*w^M&pv1>u6r&sOGVIxgLq z9!qBPF7F<+cdp~QcP`9=q`Lp~zxtJlyZxF2Ga4*^I@;?`M)&4%?-$g-XQ+rKU9>QD z_~>le>|?y`xAm~Kbuu2b^;jI*dcxk7Z51`X7-|ggf5j?lhKDa$MK6VlS_1kjRMZBF z=R-v;*A~#BqK)Yn;>^1CaGcSzjp-|K#<2V3Hj%8q;kIrCTpf1F!u`O0f z?N%KmU*uNZzBhw(^$tYRcI;2RSh?G;7V6VIGXd*~KmOdw$oeK$4(eM0TF9AJ9xD?& zu6X|-n zhtQXIcxi0Jvb6IelaN4-)OCQWDtizmL^UJt+$X5u^ zhwt474qETr>no@aO*rrEbdFSbWIFm?J5{`g=wH}4#8=PWXjAH8|6 z7Bgs?b;tL1BO*w9Lib`VXJmGDtHUz|;q2dI&E3=` z9i*~mzc6cBFJ%bgTWGG-RU8t~7DSd$#ute_Ycdw~_<#PqX(pPF%)N%Hij^Ix%RJQA zpTr5x!q21L&(ncv{{{f-)Q2W{#Xs?!D)f-qjEQfy3Rg7UfxrW=Pj4Cb$xHwyw1$*D z)LI8Yt|E~sGWka<36+6J|JCRg5EuLA+v%LRZnON~a&Q_zX&&x^ZbiluK!{8(HC6Kc^wYYkvJ4K>10 z?1Pk~GDq*W@Ph|BbZ^3?bcHeGf)IrvOYPc|SdEK=s+4R+zev3k*gsK=n}=1c6c{e1 zniyXJ3wkY`Ht?A|?dwD^=3MMYePj`jvu0|r0(Mj`W693Ne=M^tW?2*75cKBit_oSK zpVUxOKOwPO5uKSkdSs&6s)VCb%S=OpY>(>shVBA-&F(# zjgHt@a~eUd=AVmZMlOn*`mfHvn%y;>ukvq6Bl8E@Sc4r^zE$avLuL_$?#M}!%~KlhiX?n`B4B>Ob2_@_gA zwYL9!xU07P$#7R```-w6*aMylcSw|=3x&Fpu&#pRzffEhvi)P&9@&V?$);p{)i#QYW9ph-lV->v{vZc(*M$y>UX6Ukv zqF9BNVgAPQsZ2T2Wpmi_Z~{gHNYE?2NNu{wn3^y#C1M%SDG*T$!cE2C7N=Tdp+(1? 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zzhgZ;^58&vaAx3@5p9VlgIA$!v_5zVO3TlBWZ+Fui+Zod0@&c5CMQky5UU4f=lO!I3u4YQHjRALIMiM3V1pr}%>H)}skN*PR+acy#>Gd?_62dmD(+L(3Q)##vE)mv;hRX!YbIz#)f=A#Kw((&b{yTpN-`>$N; THP^;Hg0+87hZC#D5o7)b_%KUE literal 0 HcmV?d00001 From 3529c344d0b11d25e4dc21d45dac94fa24e82842 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:06:14 +0000 Subject: [PATCH 33/49] Compiled vector_search/brute_force_bacalhau --- .../benchmarker_outbound.rs | 113 ++++++++++++++++++ .../brute_force_bacalhau/commercial.rs | 113 ++++++++++++++++++ .../brute_force_bacalhau/inbound.rs | 113 ++++++++++++++++++ .../innovator_outbound.rs | 113 ++++++++++++++++++ .../vector_search/brute_force_bacalhau/mod.rs | 4 + .../brute_force_bacalhau/open_data.rs | 113 ++++++++++++++++++ tig-algorithms/src/vector_search/mod.rs | 3 +- tig-algorithms/src/vector_search/template.rs | 26 +--- .../vector_search/brute_force_bacalhau.wasm | Bin 0 -> 125121 bytes 9 files changed, 573 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vector_search/brute_force_bacalhau/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vector_search/brute_force_bacalhau/commercial.rs create mode 100644 tig-algorithms/src/vector_search/brute_force_bacalhau/inbound.rs create mode 100644 tig-algorithms/src/vector_search/brute_force_bacalhau/innovator_outbound.rs create mode 100644 tig-algorithms/src/vector_search/brute_force_bacalhau/mod.rs create mode 100644 tig-algorithms/src/vector_search/brute_force_bacalhau/open_data.rs create mode 100644 tig-algorithms/wasm/vector_search/brute_force_bacalhau.wasm diff --git a/tig-algorithms/src/vector_search/brute_force_bacalhau/benchmarker_outbound.rs b/tig-algorithms/src/vector_search/brute_force_bacalhau/benchmarker_outbound.rs new file mode 100644 index 0000000..8a58f5c --- /dev/null +++ b/tig-algorithms/src/vector_search/brute_force_bacalhau/benchmarker_outbound.rs @@ -0,0 +1,113 @@ +/*! +Copyright 2024 Louis Silva + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. + */ + +use anyhow::Result; + +use tig_challenges::vector_search::*; + +#[inline] +fn l2_norm(x: &[f32]) -> f32 { + x.iter().map(|&val| val * val).sum::().sqrt() +} + +#[inline] +fn euclidean_distance_with_precomputed_norm( + a_norm_sq: f32, + b_norm_sq: f32, + ab_dot_product: f32 +) -> f32 { + (a_norm_sq + b_norm_sq - 2.0 * ab_dot_product).sqrt() +} + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vector_database: &Vec> = &challenge.vector_database; + let query_vectors: &Vec> = &challenge.query_vectors; + let max_distance: f32 = challenge.max_distance; + + let mut indexes: Vec = Vec::with_capacity(query_vectors.len()); + let mut vector_norms_sq: Vec = Vec::with_capacity(vector_database.len()); + + let mut sum_norms_sq: f32 = 0.0; + let mut sum_squares: f32 = 0.0; + + for vector in vector_database { + let norm_sq: f32 = vector.iter().map(|&val| val * val).sum(); + sum_norms_sq += norm_sq.sqrt(); + sum_squares += norm_sq; + vector_norms_sq.push(norm_sq); + } + + let vector_norms_len: f32 = vector_norms_sq.len() as f32; + let std_dev: f32 = ((sum_squares / vector_norms_len) - (sum_norms_sq / vector_norms_len).powi(2)).sqrt(); + let norm_threshold: f32 = 2.0 * std_dev; + + for query in query_vectors { + let query_norm_sq: f32 = query.iter().map(|&val| val * val).sum(); + + let mut closest_index: Option = None; + let mut closest_distance: f32 = f32::MAX; + + for (idx, vector) in vector_database.iter().enumerate() { + let vector_norm_sq = vector_norms_sq[idx]; + if ((vector_norm_sq.sqrt() - query_norm_sq.sqrt()).abs()) > norm_threshold { + continue; + } + + let ab_dot_product: f32 = query.iter().zip(vector).map(|(&x1, &x2)| x1 * x2).sum(); + let distance: f32 = euclidean_distance_with_precomputed_norm( + query_norm_sq, + vector_norm_sq, + ab_dot_product, + ); + + if distance <= max_distance { + closest_index = Some(idx); + break; // Early exit + } else if distance < closest_distance { + closest_index = Some(idx); + closest_distance = distance; + } + } + + if let Some(index) = closest_index { + indexes.push(index); + } else { + return Ok(None); + } + } + + Ok(Some(Solution { indexes })) +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/brute_force_bacalhau/commercial.rs b/tig-algorithms/src/vector_search/brute_force_bacalhau/commercial.rs new file mode 100644 index 0000000..bacee69 --- /dev/null +++ b/tig-algorithms/src/vector_search/brute_force_bacalhau/commercial.rs @@ -0,0 +1,113 @@ +/*! +Copyright 2024 Louis Silva + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. + */ + +use anyhow::Result; + +use tig_challenges::vector_search::*; + +#[inline] +fn l2_norm(x: &[f32]) -> f32 { + x.iter().map(|&val| val * val).sum::().sqrt() +} + +#[inline] +fn euclidean_distance_with_precomputed_norm( + a_norm_sq: f32, + b_norm_sq: f32, + ab_dot_product: f32 +) -> f32 { + (a_norm_sq + b_norm_sq - 2.0 * ab_dot_product).sqrt() +} + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vector_database: &Vec> = &challenge.vector_database; + let query_vectors: &Vec> = &challenge.query_vectors; + let max_distance: f32 = challenge.max_distance; + + let mut indexes: Vec = Vec::with_capacity(query_vectors.len()); + let mut vector_norms_sq: Vec = Vec::with_capacity(vector_database.len()); + + let mut sum_norms_sq: f32 = 0.0; + let mut sum_squares: f32 = 0.0; + + for vector in vector_database { + let norm_sq: f32 = vector.iter().map(|&val| val * val).sum(); + sum_norms_sq += norm_sq.sqrt(); + sum_squares += norm_sq; + vector_norms_sq.push(norm_sq); + } + + let vector_norms_len: f32 = vector_norms_sq.len() as f32; + let std_dev: f32 = ((sum_squares / vector_norms_len) - (sum_norms_sq / vector_norms_len).powi(2)).sqrt(); + let norm_threshold: f32 = 2.0 * std_dev; + + for query in query_vectors { + let query_norm_sq: f32 = query.iter().map(|&val| val * val).sum(); + + let mut closest_index: Option = None; + let mut closest_distance: f32 = f32::MAX; + + for (idx, vector) in vector_database.iter().enumerate() { + let vector_norm_sq = vector_norms_sq[idx]; + if ((vector_norm_sq.sqrt() - query_norm_sq.sqrt()).abs()) > norm_threshold { + continue; + } + + let ab_dot_product: f32 = query.iter().zip(vector).map(|(&x1, &x2)| x1 * x2).sum(); + let distance: f32 = euclidean_distance_with_precomputed_norm( + query_norm_sq, + vector_norm_sq, + ab_dot_product, + ); + + if distance <= max_distance { + closest_index = Some(idx); + break; // Early exit + } else if distance < closest_distance { + closest_index = Some(idx); + closest_distance = distance; + } + } + + if let Some(index) = closest_index { + indexes.push(index); + } else { + return Ok(None); + } + } + + Ok(Some(Solution { indexes })) +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/brute_force_bacalhau/inbound.rs b/tig-algorithms/src/vector_search/brute_force_bacalhau/inbound.rs new file mode 100644 index 0000000..fd0bf1d --- /dev/null +++ b/tig-algorithms/src/vector_search/brute_force_bacalhau/inbound.rs @@ -0,0 +1,113 @@ +/*! +Copyright 2024 Louis Silva + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. + */ + +use anyhow::Result; + +use tig_challenges::vector_search::*; + +#[inline] +fn l2_norm(x: &[f32]) -> f32 { + x.iter().map(|&val| val * val).sum::().sqrt() +} + +#[inline] +fn euclidean_distance_with_precomputed_norm( + a_norm_sq: f32, + b_norm_sq: f32, + ab_dot_product: f32 +) -> f32 { + (a_norm_sq + b_norm_sq - 2.0 * ab_dot_product).sqrt() +} + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vector_database: &Vec> = &challenge.vector_database; + let query_vectors: &Vec> = &challenge.query_vectors; + let max_distance: f32 = challenge.max_distance; + + let mut indexes: Vec = Vec::with_capacity(query_vectors.len()); + let mut vector_norms_sq: Vec = Vec::with_capacity(vector_database.len()); + + let mut sum_norms_sq: f32 = 0.0; + let mut sum_squares: f32 = 0.0; + + for vector in vector_database { + let norm_sq: f32 = vector.iter().map(|&val| val * val).sum(); + sum_norms_sq += norm_sq.sqrt(); + sum_squares += norm_sq; + vector_norms_sq.push(norm_sq); + } + + let vector_norms_len: f32 = vector_norms_sq.len() as f32; + let std_dev: f32 = ((sum_squares / vector_norms_len) - (sum_norms_sq / vector_norms_len).powi(2)).sqrt(); + let norm_threshold: f32 = 2.0 * std_dev; + + for query in query_vectors { + let query_norm_sq: f32 = query.iter().map(|&val| val * val).sum(); + + let mut closest_index: Option = None; + let mut closest_distance: f32 = f32::MAX; + + for (idx, vector) in vector_database.iter().enumerate() { + let vector_norm_sq = vector_norms_sq[idx]; + if ((vector_norm_sq.sqrt() - query_norm_sq.sqrt()).abs()) > norm_threshold { + continue; + } + + let ab_dot_product: f32 = query.iter().zip(vector).map(|(&x1, &x2)| x1 * x2).sum(); + let distance: f32 = euclidean_distance_with_precomputed_norm( + query_norm_sq, + vector_norm_sq, + ab_dot_product, + ); + + if distance <= max_distance { + closest_index = Some(idx); + break; // Early exit + } else if distance < closest_distance { + closest_index = Some(idx); + closest_distance = distance; + } + } + + if let Some(index) = closest_index { + indexes.push(index); + } else { + return Ok(None); + } + } + + Ok(Some(Solution { indexes })) +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/brute_force_bacalhau/innovator_outbound.rs b/tig-algorithms/src/vector_search/brute_force_bacalhau/innovator_outbound.rs new file mode 100644 index 0000000..a0996b6 --- /dev/null +++ b/tig-algorithms/src/vector_search/brute_force_bacalhau/innovator_outbound.rs @@ -0,0 +1,113 @@ +/*! +Copyright 2024 Louis Silva + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. + */ + +use anyhow::Result; + +use tig_challenges::vector_search::*; + +#[inline] +fn l2_norm(x: &[f32]) -> f32 { + x.iter().map(|&val| val * val).sum::().sqrt() +} + +#[inline] +fn euclidean_distance_with_precomputed_norm( + a_norm_sq: f32, + b_norm_sq: f32, + ab_dot_product: f32 +) -> f32 { + (a_norm_sq + b_norm_sq - 2.0 * ab_dot_product).sqrt() +} + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vector_database: &Vec> = &challenge.vector_database; + let query_vectors: &Vec> = &challenge.query_vectors; + let max_distance: f32 = challenge.max_distance; + + let mut indexes: Vec = Vec::with_capacity(query_vectors.len()); + let mut vector_norms_sq: Vec = Vec::with_capacity(vector_database.len()); + + let mut sum_norms_sq: f32 = 0.0; + let mut sum_squares: f32 = 0.0; + + for vector in vector_database { + let norm_sq: f32 = vector.iter().map(|&val| val * val).sum(); + sum_norms_sq += norm_sq.sqrt(); + sum_squares += norm_sq; + vector_norms_sq.push(norm_sq); + } + + let vector_norms_len: f32 = vector_norms_sq.len() as f32; + let std_dev: f32 = ((sum_squares / vector_norms_len) - (sum_norms_sq / vector_norms_len).powi(2)).sqrt(); + let norm_threshold: f32 = 2.0 * std_dev; + + for query in query_vectors { + let query_norm_sq: f32 = query.iter().map(|&val| val * val).sum(); + + let mut closest_index: Option = None; + let mut closest_distance: f32 = f32::MAX; + + for (idx, vector) in vector_database.iter().enumerate() { + let vector_norm_sq = vector_norms_sq[idx]; + if ((vector_norm_sq.sqrt() - query_norm_sq.sqrt()).abs()) > norm_threshold { + continue; + } + + let ab_dot_product: f32 = query.iter().zip(vector).map(|(&x1, &x2)| x1 * x2).sum(); + let distance: f32 = euclidean_distance_with_precomputed_norm( + query_norm_sq, + vector_norm_sq, + ab_dot_product, + ); + + if distance <= max_distance { + closest_index = Some(idx); + break; // Early exit + } else if distance < closest_distance { + closest_index = Some(idx); + closest_distance = distance; + } + } + + if let Some(index) = closest_index { + indexes.push(index); + } else { + return Ok(None); + } + } + + Ok(Some(Solution { indexes })) +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/brute_force_bacalhau/mod.rs b/tig-algorithms/src/vector_search/brute_force_bacalhau/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vector_search/brute_force_bacalhau/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vector_search/brute_force_bacalhau/open_data.rs b/tig-algorithms/src/vector_search/brute_force_bacalhau/open_data.rs new file mode 100644 index 0000000..c55800e --- /dev/null +++ b/tig-algorithms/src/vector_search/brute_force_bacalhau/open_data.rs @@ -0,0 +1,113 @@ +/*! +Copyright 2024 Louis Silva + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. + */ + +use anyhow::Result; + +use tig_challenges::vector_search::*; + +#[inline] +fn l2_norm(x: &[f32]) -> f32 { + x.iter().map(|&val| val * val).sum::().sqrt() +} + +#[inline] +fn euclidean_distance_with_precomputed_norm( + a_norm_sq: f32, + b_norm_sq: f32, + ab_dot_product: f32 +) -> f32 { + (a_norm_sq + b_norm_sq - 2.0 * ab_dot_product).sqrt() +} + +pub fn solve_challenge(challenge: &Challenge) -> Result> { + let vector_database: &Vec> = &challenge.vector_database; + let query_vectors: &Vec> = &challenge.query_vectors; + let max_distance: f32 = challenge.max_distance; + + let mut indexes: Vec = Vec::with_capacity(query_vectors.len()); + let mut vector_norms_sq: Vec = Vec::with_capacity(vector_database.len()); + + let mut sum_norms_sq: f32 = 0.0; + let mut sum_squares: f32 = 0.0; + + for vector in vector_database { + let norm_sq: f32 = vector.iter().map(|&val| val * val).sum(); + sum_norms_sq += norm_sq.sqrt(); + sum_squares += norm_sq; + vector_norms_sq.push(norm_sq); + } + + let vector_norms_len: f32 = vector_norms_sq.len() as f32; + let std_dev: f32 = ((sum_squares / vector_norms_len) - (sum_norms_sq / vector_norms_len).powi(2)).sqrt(); + let norm_threshold: f32 = 2.0 * std_dev; + + for query in query_vectors { + let query_norm_sq: f32 = query.iter().map(|&val| val * val).sum(); + + let mut closest_index: Option = None; + let mut closest_distance: f32 = f32::MAX; + + for (idx, vector) in vector_database.iter().enumerate() { + let vector_norm_sq = vector_norms_sq[idx]; + if ((vector_norm_sq.sqrt() - query_norm_sq.sqrt()).abs()) > norm_threshold { + continue; + } + + let ab_dot_product: f32 = query.iter().zip(vector).map(|(&x1, &x2)| x1 * x2).sum(); + let distance: f32 = euclidean_distance_with_precomputed_norm( + query_norm_sq, + vector_norm_sq, + ab_dot_product, + ); + + if distance <= max_distance { + closest_index = Some(idx); + break; // Early exit + } else if distance < closest_distance { + closest_index = Some(idx); + closest_distance = distance; + } + } + + if let Some(index) = closest_index { + indexes.push(index); + } else { + return Ok(None); + } + } + + Ok(Some(Solution { indexes })) +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/mod.rs b/tig-algorithms/src/vector_search/mod.rs index ae3472a..4990304 100644 --- a/tig-algorithms/src/vector_search/mod.rs +++ b/tig-algorithms/src/vector_search/mod.rs @@ -24,7 +24,8 @@ // c004_a013 -// c004_a014 +pub mod brute_force_bacalhau; +pub use brute_force_bacalhau as c004_a014; // c004_a015 diff --git a/tig-algorithms/src/vector_search/template.rs b/tig-algorithms/src/vector_search/template.rs index eddf8a0..0f5fa1e 100644 --- a/tig-algorithms/src/vector_search/template.rs +++ b/tig-algorithms/src/vector_search/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vector_search::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vector_search/brute_force_bacalhau.wasm b/tig-algorithms/wasm/vector_search/brute_force_bacalhau.wasm new file mode 100644 index 0000000000000000000000000000000000000000..9c34e1557e8df39838143d92a078300a6100dd96 GIT binary patch literal 125121 zcmeFa3!Gg?edl>z_kC~oZK)--)v{gpSb+RctSn#&;|!|a_#tFtCwLepGrKXL$?Q0{ z9paWX9TJSywq;|C0fm^vW_SqVAY)K4gUL9=K${qF5+I%#7|17{#EHGEhsiRY;AA|X zA!xt9f1PvgZFS2JCYjyO?z*je>eQ)I^{;>Z>;I~M)rsD4&CkVA6vYSQ^;e_^4jhON zT#?vsd>~RPvfSv3jAz`$Do|6Jn{Xw=9&)YQ1D-^@d!63B&gv@iDbWZeR7-YUc$glz z?mE@aZIr$*es7#+SvO7MIEs4{SsXXxIBvy>{g)eL@!9bbNVjkc!hEpXwNmX@PVW02N=?w|hL&+q%iB+7pFjX%3Tx+CuV(>Lzl_lrM$ z_0Rw88}~e?zH6pm)bF*{P0#($7hm#|ubX?#&R0zQd}Gf) z-~ZNlX6Gv|J^7r={@0VHHvjaNm(2XVSMC0>UpoJS3t#&am;e2L@P>c#Ge7ooGjI4O 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Cy6b!Z literal 0 HcmV?d00001 From c537c58e4c9920df8d1ba0d144cde403c89ee2f8 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:06:39 +0000 Subject: [PATCH 34/49] Compiled vector_search/invector --- .../invector/benchmarker_outbound.rs | 367 ++++++++++++++++++ .../src/vector_search/invector/commercial.rs | 367 ++++++++++++++++++ .../src/vector_search/invector/inbound.rs | 367 ++++++++++++++++++ .../invector/innovator_outbound.rs | 367 ++++++++++++++++++ .../src/vector_search/invector/mod.rs | 4 + .../src/vector_search/invector/open_data.rs | 367 ++++++++++++++++++ tig-algorithms/src/vector_search/mod.rs | 3 +- tig-algorithms/src/vector_search/template.rs | 26 +- .../wasm/vector_search/invector.wasm | Bin 0 -> 129340 bytes 9 files changed, 1843 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vector_search/invector/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vector_search/invector/commercial.rs create mode 100644 tig-algorithms/src/vector_search/invector/inbound.rs create mode 100644 tig-algorithms/src/vector_search/invector/innovator_outbound.rs create mode 100644 tig-algorithms/src/vector_search/invector/mod.rs create mode 100644 tig-algorithms/src/vector_search/invector/open_data.rs create mode 100644 tig-algorithms/wasm/vector_search/invector.wasm diff --git a/tig-algorithms/src/vector_search/invector/benchmarker_outbound.rs b/tig-algorithms/src/vector_search/invector/benchmarker_outbound.rs new file mode 100644 index 0000000..c7d3dac --- /dev/null +++ b/tig-algorithms/src/vector_search/invector/benchmarker_outbound.rs @@ -0,0 +1,367 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} + +#[inline(always)] +fn squared_euclidean_distance_limited(a: &[f32], b: &[f32], c : f32) -> f32 { + let mut sum = 0.0; + for i in 0..180 { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + if sum > c { + sum; + } + for i in 180..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = squared_euclidean_distance_limited(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let subset_size = ((max_fuel - base_fuel) / alpha) as usize; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/invector/commercial.rs b/tig-algorithms/src/vector_search/invector/commercial.rs new file mode 100644 index 0000000..5ab2af2 --- /dev/null +++ b/tig-algorithms/src/vector_search/invector/commercial.rs @@ -0,0 +1,367 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} + +#[inline(always)] +fn squared_euclidean_distance_limited(a: &[f32], b: &[f32], c : f32) -> f32 { + let mut sum = 0.0; + for i in 0..180 { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + if sum > c { + sum; + } + for i in 180..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = squared_euclidean_distance_limited(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let subset_size = ((max_fuel - base_fuel) / alpha) as usize; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/invector/inbound.rs b/tig-algorithms/src/vector_search/invector/inbound.rs new file mode 100644 index 0000000..bbc5f58 --- /dev/null +++ b/tig-algorithms/src/vector_search/invector/inbound.rs @@ -0,0 +1,367 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} + +#[inline(always)] +fn squared_euclidean_distance_limited(a: &[f32], b: &[f32], c : f32) -> f32 { + let mut sum = 0.0; + for i in 0..180 { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + if sum > c { + sum; + } + for i in 180..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = squared_euclidean_distance_limited(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let subset_size = ((max_fuel - base_fuel) / alpha) as usize; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/invector/innovator_outbound.rs b/tig-algorithms/src/vector_search/invector/innovator_outbound.rs new file mode 100644 index 0000000..3496425 --- /dev/null +++ b/tig-algorithms/src/vector_search/invector/innovator_outbound.rs @@ -0,0 +1,367 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} + +#[inline(always)] +fn squared_euclidean_distance_limited(a: &[f32], b: &[f32], c : f32) -> f32 { + let mut sum = 0.0; + for i in 0..180 { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + if sum > c { + sum; + } + for i in 180..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = squared_euclidean_distance_limited(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let subset_size = ((max_fuel - base_fuel) / alpha) as usize; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/invector/mod.rs b/tig-algorithms/src/vector_search/invector/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vector_search/invector/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vector_search/invector/open_data.rs b/tig-algorithms/src/vector_search/invector/open_data.rs new file mode 100644 index 0000000..cada64f --- /dev/null +++ b/tig-algorithms/src/vector_search/invector/open_data.rs @@ -0,0 +1,367 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} + +#[inline(always)] +fn squared_euclidean_distance_limited(a: &[f32], b: &[f32], c : f32) -> f32 { + let mut sum = 0.0; + for i in 0..180 { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + if sum > c { + sum; + } + for i in 180..a.len() { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + } + sum +} +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = squared_euclidean_distance_limited(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let subset_size = ((max_fuel - base_fuel) / alpha) as usize; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/mod.rs b/tig-algorithms/src/vector_search/mod.rs index ae3472a..cb6b177 100644 --- a/tig-algorithms/src/vector_search/mod.rs +++ b/tig-algorithms/src/vector_search/mod.rs @@ -64,7 +64,8 @@ // c004_a033 -// c004_a034 +pub mod invector; +pub use invector as c004_a034; // c004_a035 diff --git a/tig-algorithms/src/vector_search/template.rs b/tig-algorithms/src/vector_search/template.rs index eddf8a0..0f5fa1e 100644 --- a/tig-algorithms/src/vector_search/template.rs +++ b/tig-algorithms/src/vector_search/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vector_search::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vector_search/invector.wasm b/tig-algorithms/wasm/vector_search/invector.wasm new file mode 100644 index 0000000000000000000000000000000000000000..87fc64c13e02bf6ec48e6d9486b0eba4f9c1623b GIT binary patch literal 129340 zcmeFa3%q4jedoDf=l!_nR^6())GerWHlS}~Z01QiXjO0*)jXcNVFQB*W&m<~oV%_Jn!4taE%w5CHoZLI$O z{%h}Z&aHckhi!W1Gc#Q3?6uckd#(TaumAeL)_<)X{nXVz8%I$TABfjpk?!BWKi+>u zV!!eJNU6wjqbo9=aTBXRMQLurl?;2xwQ3J|67lY}diPqZtH`HBEtF6#*|p(ey8qg1 zRX?{;`o8!*ahhfAG>PLV?sT&_uE%lQh!guSP0~tIjng!)CUM$KvaFV6t)x}sX-aMs zXO)Owdx=XHXA``hQjK1UE0s#tsF9AysbpzF8M2H2np8mx6wJ~d*I2DkmJ29~s*`J~ z)E{3JUw(OowkE3$(BT&sm!ej!pH_eNpZ)B=*!%NIl>N+`er8|vYjNwJy=mXxpa03L z{>9I{XV4<_%Y~^ey-s7z)cF@&dfET>{7_ 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Subject: [PATCH 35/49] Compiled vector_search/invector_hybrid --- .../invector_hybrid/benchmarker_outbound.rs | 395 ++++++++++++++++++ .../invector_hybrid/commercial.rs | 395 ++++++++++++++++++ .../vector_search/invector_hybrid/inbound.rs | 395 ++++++++++++++++++ .../invector_hybrid/innovator_outbound.rs | 395 ++++++++++++++++++ .../src/vector_search/invector_hybrid/mod.rs | 4 + .../invector_hybrid/open_data.rs | 395 ++++++++++++++++++ tig-algorithms/src/vector_search/mod.rs | 3 +- tig-algorithms/src/vector_search/template.rs | 26 +- .../wasm/vector_search/invector_hybrid.wasm | Bin 0 -> 144198 bytes 9 files changed, 1983 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vector_search/invector_hybrid/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vector_search/invector_hybrid/commercial.rs create mode 100644 tig-algorithms/src/vector_search/invector_hybrid/inbound.rs create mode 100644 tig-algorithms/src/vector_search/invector_hybrid/innovator_outbound.rs create mode 100644 tig-algorithms/src/vector_search/invector_hybrid/mod.rs create mode 100644 tig-algorithms/src/vector_search/invector_hybrid/open_data.rs create mode 100644 tig-algorithms/wasm/vector_search/invector_hybrid.wasm diff --git a/tig-algorithms/src/vector_search/invector_hybrid/benchmarker_outbound.rs b/tig-algorithms/src/vector_search/invector_hybrid/benchmarker_outbound.rs new file mode 100644 index 0000000..6a1aa55 --- /dev/null +++ b/tig-algorithms/src/vector_search/invector_hybrid/benchmarker_outbound.rs @@ -0,0 +1,395 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0f64; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i] as f64; + } + } + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f64; + } + mean_vector.into_iter().map(|x| x as f32).collect() +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(f32, &'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = early_stopping_distance(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + heap.into_sorted_vec() + .into_iter() + .map(|(FloatOrd(dist), index)| (dist, &database[index][..], index)) + .collect() +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let m = ((max_fuel - base_fuel) / alpha) as usize; + let n = (m as f32 * 1.2) as usize; + let r = n - m; + + let closest_vectors = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + n, + ); + + let (m_slice, r_slice) = closest_vectors.split_at(m); + let m_vectors: Vec<_> = m_slice.to_vec(); + let r_vectors: Vec<_> = r_slice.to_vec(); + + let mut kd_tree_vectors: Vec<(&[f32], usize)> = m_vectors.iter().map(|&(_, v, i)| (v, i)).collect(); + let kd_tree = build_kd_tree(&mut kd_tree_vectors); + + let mut best_indexes = Vec::with_capacity(query_count); + let mut distances = Vec::with_capacity(query_count); + + for query in &challenge.query_vectors { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + distances.push(best.0); + best_indexes.push(best.1.unwrap_or(0)); + } + + let brute_force_count = (query_count as f32 * 0.1) as usize; + let mut distance_indices: Vec<_> = distances.iter().enumerate().collect(); + distance_indices.sort_unstable_by(|a, b| b.1.partial_cmp(a.1).unwrap()); + let high_distance_indices: Vec<_> = distance_indices.into_iter() + .take(brute_force_count) + .map(|(index, _)| index) + .collect(); + + for &query_index in &high_distance_indices { + let query = &challenge.query_vectors[query_index]; + let mut best = (distances[query_index], best_indexes[query_index]); + + for &(_, vec, index) in &r_vectors { + let dist = squared_euclidean_distance(query, vec); + if dist < best.0 { + best = (dist, index); + } + } + + best_indexes[query_index] = best.1; + } + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/invector_hybrid/commercial.rs b/tig-algorithms/src/vector_search/invector_hybrid/commercial.rs new file mode 100644 index 0000000..c82abc3 --- /dev/null +++ b/tig-algorithms/src/vector_search/invector_hybrid/commercial.rs @@ -0,0 +1,395 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0f64; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i] as f64; + } + } + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f64; + } + mean_vector.into_iter().map(|x| x as f32).collect() +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(f32, &'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = early_stopping_distance(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + heap.into_sorted_vec() + .into_iter() + .map(|(FloatOrd(dist), index)| (dist, &database[index][..], index)) + .collect() +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let m = ((max_fuel - base_fuel) / alpha) as usize; + let n = (m as f32 * 1.2) as usize; + let r = n - m; + + let closest_vectors = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + n, + ); + + let (m_slice, r_slice) = closest_vectors.split_at(m); + let m_vectors: Vec<_> = m_slice.to_vec(); + let r_vectors: Vec<_> = r_slice.to_vec(); + + let mut kd_tree_vectors: Vec<(&[f32], usize)> = m_vectors.iter().map(|&(_, v, i)| (v, i)).collect(); + let kd_tree = build_kd_tree(&mut kd_tree_vectors); + + let mut best_indexes = Vec::with_capacity(query_count); + let mut distances = Vec::with_capacity(query_count); + + for query in &challenge.query_vectors { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + distances.push(best.0); + best_indexes.push(best.1.unwrap_or(0)); + } + + let brute_force_count = (query_count as f32 * 0.1) as usize; + let mut distance_indices: Vec<_> = distances.iter().enumerate().collect(); + distance_indices.sort_unstable_by(|a, b| b.1.partial_cmp(a.1).unwrap()); + let high_distance_indices: Vec<_> = distance_indices.into_iter() + .take(brute_force_count) + .map(|(index, _)| index) + .collect(); + + for &query_index in &high_distance_indices { + let query = &challenge.query_vectors[query_index]; + let mut best = (distances[query_index], best_indexes[query_index]); + + for &(_, vec, index) in &r_vectors { + let dist = squared_euclidean_distance(query, vec); + if dist < best.0 { + best = (dist, index); + } + } + + best_indexes[query_index] = best.1; + } + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/invector_hybrid/inbound.rs b/tig-algorithms/src/vector_search/invector_hybrid/inbound.rs new file mode 100644 index 0000000..9a283e9 --- /dev/null +++ b/tig-algorithms/src/vector_search/invector_hybrid/inbound.rs @@ -0,0 +1,395 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0f64; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i] as f64; + } + } + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f64; + } + mean_vector.into_iter().map(|x| x as f32).collect() +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(f32, &'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = early_stopping_distance(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + heap.into_sorted_vec() + .into_iter() + .map(|(FloatOrd(dist), index)| (dist, &database[index][..], index)) + .collect() +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let m = ((max_fuel - base_fuel) / alpha) as usize; + let n = (m as f32 * 1.2) as usize; + let r = n - m; + + let closest_vectors = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + n, + ); + + let (m_slice, r_slice) = closest_vectors.split_at(m); + let m_vectors: Vec<_> = m_slice.to_vec(); + let r_vectors: Vec<_> = r_slice.to_vec(); + + let mut kd_tree_vectors: Vec<(&[f32], usize)> = m_vectors.iter().map(|&(_, v, i)| (v, i)).collect(); + let kd_tree = build_kd_tree(&mut kd_tree_vectors); + + let mut best_indexes = Vec::with_capacity(query_count); + let mut distances = Vec::with_capacity(query_count); + + for query in &challenge.query_vectors { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + distances.push(best.0); + best_indexes.push(best.1.unwrap_or(0)); + } + + let brute_force_count = (query_count as f32 * 0.1) as usize; + let mut distance_indices: Vec<_> = distances.iter().enumerate().collect(); + distance_indices.sort_unstable_by(|a, b| b.1.partial_cmp(a.1).unwrap()); + let high_distance_indices: Vec<_> = distance_indices.into_iter() + .take(brute_force_count) + .map(|(index, _)| index) + .collect(); + + for &query_index in &high_distance_indices { + let query = &challenge.query_vectors[query_index]; + let mut best = (distances[query_index], best_indexes[query_index]); + + for &(_, vec, index) in &r_vectors { + let dist = squared_euclidean_distance(query, vec); + if dist < best.0 { + best = (dist, index); + } + } + + best_indexes[query_index] = best.1; + } + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/invector_hybrid/innovator_outbound.rs b/tig-algorithms/src/vector_search/invector_hybrid/innovator_outbound.rs new file mode 100644 index 0000000..624671f --- /dev/null +++ b/tig-algorithms/src/vector_search/invector_hybrid/innovator_outbound.rs @@ -0,0 +1,395 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0f64; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i] as f64; + } + } + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f64; + } + mean_vector.into_iter().map(|x| x as f32).collect() +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(f32, &'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = early_stopping_distance(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + heap.into_sorted_vec() + .into_iter() + .map(|(FloatOrd(dist), index)| (dist, &database[index][..], index)) + .collect() +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let m = ((max_fuel - base_fuel) / alpha) as usize; + let n = (m as f32 * 1.2) as usize; + let r = n - m; + + let closest_vectors = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + n, + ); + + let (m_slice, r_slice) = closest_vectors.split_at(m); + let m_vectors: Vec<_> = m_slice.to_vec(); + let r_vectors: Vec<_> = r_slice.to_vec(); + + let mut kd_tree_vectors: Vec<(&[f32], usize)> = m_vectors.iter().map(|&(_, v, i)| (v, i)).collect(); + let kd_tree = build_kd_tree(&mut kd_tree_vectors); + + let mut best_indexes = Vec::with_capacity(query_count); + let mut distances = Vec::with_capacity(query_count); + + for query in &challenge.query_vectors { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + distances.push(best.0); + best_indexes.push(best.1.unwrap_or(0)); + } + + let brute_force_count = (query_count as f32 * 0.1) as usize; + let mut distance_indices: Vec<_> = distances.iter().enumerate().collect(); + distance_indices.sort_unstable_by(|a, b| b.1.partial_cmp(a.1).unwrap()); + let high_distance_indices: Vec<_> = distance_indices.into_iter() + .take(brute_force_count) + .map(|(index, _)| index) + .collect(); + + for &query_index in &high_distance_indices { + let query = &challenge.query_vectors[query_index]; + let mut best = (distances[query_index], best_indexes[query_index]); + + for &(_, vec, index) in &r_vectors { + let dist = squared_euclidean_distance(query, vec); + if dist < best.0 { + best = (dist, index); + } + } + + best_indexes[query_index] = best.1; + } + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/invector_hybrid/mod.rs b/tig-algorithms/src/vector_search/invector_hybrid/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vector_search/invector_hybrid/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vector_search/invector_hybrid/open_data.rs b/tig-algorithms/src/vector_search/invector_hybrid/open_data.rs new file mode 100644 index 0000000..e528466 --- /dev/null +++ b/tig-algorithms/src/vector_search/invector_hybrid/open_data.rs @@ -0,0 +1,395 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + let len = a.len(); + + if a.len() != b.len() || a.len() < 8 { + return f32::MAX; + } + + while i + 7 < len { + unsafe { + let diff0 = *a.get_unchecked(i) - *b.get_unchecked(i); + let diff1 = *a.get_unchecked(i + 1) - *b.get_unchecked(i + 1); + let diff2 = *a.get_unchecked(i + 2) - *b.get_unchecked(i + 2); + let diff3 = *a.get_unchecked(i + 3) - *b.get_unchecked(i + 3); + let diff4 = *a.get_unchecked(i + 4) - *b.get_unchecked(i + 4); + let diff5 = *a.get_unchecked(i + 5) - *b.get_unchecked(i + 5); + let diff6 = *a.get_unchecked(i + 6) - *b.get_unchecked(i + 6); + let diff7 = *a.get_unchecked(i + 7) - *b.get_unchecked(i + 7); + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3 + + diff4 * diff4 + diff5 * diff5 + diff6 * diff6 + diff7 * diff7; + } + + if sum > current_min { + return f32::MAX; + } + + i += 8; + } + + while i < len { + unsafe { + let diff = *a.get_unchecked(i) - *b.get_unchecked(i); + sum += diff * diff; + } + i += 1; + } + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + let (nearer, farther) = if diff < 0.0 { + (&node.left, &node.right) + } else { + (&node.right, &node.left) + }; + + if let Some(nearer_node) = nearer { + stack.push((nearer_node.as_ref(), depth + 1)); + } + + if sqr_diff < best.0 { + if let Some(farther_node) = farther { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + } +} +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0f64; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i] as f64; + } + } + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f64; + } + mean_vector.into_iter().map(|x| x as f32).collect() +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(f32, &'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + if heap.len() < k + { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() + { + let dist = early_stopping_distance(&mean_query_vector, vector, top_dist); + let ord_dist = FloatOrd(dist); + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + heap.into_sorted_vec() + .into_iter() + .map(|(FloatOrd(dist), index)| (dist, &database[index][..], index)) + .collect() +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let max_fuel = 2000000000.0; + let base_fuel = 760000000.0; + let alpha = 1700.0 * challenge.difficulty.num_queries as f64; + + let m = ((max_fuel - base_fuel) / alpha) as usize; + let n = (m as f32 * 1.2) as usize; + let r = n - m; + + let closest_vectors = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + n, + ); + + let (m_slice, r_slice) = closest_vectors.split_at(m); + let m_vectors: Vec<_> = m_slice.to_vec(); + let r_vectors: Vec<_> = r_slice.to_vec(); + + let mut kd_tree_vectors: Vec<(&[f32], usize)> = m_vectors.iter().map(|&(_, v, i)| (v, i)).collect(); + let kd_tree = build_kd_tree(&mut kd_tree_vectors); + + let mut best_indexes = Vec::with_capacity(query_count); + let mut distances = Vec::with_capacity(query_count); + + for query in &challenge.query_vectors { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + distances.push(best.0); + best_indexes.push(best.1.unwrap_or(0)); + } + + let brute_force_count = (query_count as f32 * 0.1) as usize; + let mut distance_indices: Vec<_> = distances.iter().enumerate().collect(); + distance_indices.sort_unstable_by(|a, b| b.1.partial_cmp(a.1).unwrap()); + let high_distance_indices: Vec<_> = distance_indices.into_iter() + .take(brute_force_count) + .map(|(index, _)| index) + .collect(); + + for &query_index in &high_distance_indices { + let query = &challenge.query_vectors[query_index]; + let mut best = (distances[query_index], best_indexes[query_index]); + + for &(_, vec, index) in &r_vectors { + let dist = squared_euclidean_distance(query, vec); + if dist < best.0 { + best = (dist, index); + } + } + + best_indexes[query_index] = best.1; + } + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/mod.rs b/tig-algorithms/src/vector_search/mod.rs index ae3472a..c627189 100644 --- a/tig-algorithms/src/vector_search/mod.rs +++ b/tig-algorithms/src/vector_search/mod.rs @@ -80,7 +80,8 @@ // c004_a041 -// c004_a042 +pub mod invector_hybrid; +pub use invector_hybrid as c004_a042; // c004_a043 diff --git a/tig-algorithms/src/vector_search/template.rs b/tig-algorithms/src/vector_search/template.rs index eddf8a0..0f5fa1e 100644 --- a/tig-algorithms/src/vector_search/template.rs +++ b/tig-algorithms/src/vector_search/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vector_search::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vector_search/invector_hybrid.wasm b/tig-algorithms/wasm/vector_search/invector_hybrid.wasm new file mode 100644 index 0000000000000000000000000000000000000000..2ba2b5f4c35a61e1706f8fce70b6f92e2d8094b0 GIT binary patch literal 144198 zcmeFa51d^`b?15iy}$Rp?$=UFYN=cHeHRfVB*mJ=NRI6pRK1aHAsZXwVKTFu#hFb$ zA+H_cmNg0qMrvEKV-o`!a1wj4L3Ri-V1f9T@q!60;(#FroEaErH{L`9hGAGX(G1Lt z**IvwzfW-TtXh%-{$X;E zgqk^UAc)@`J`l!9(u$+79=4N_kpF@(Y=)8ki!0SIj>Bpc#%Yu!wIqoejhY^i8-z(E z@P+N2BpD6E2DMQIkHbo(lGJOY19B=!98quozZx~rrs}EGlem+pxmsG++cd{ZT)=OJ{!JwZGWA z?w=ocFNXFH#fw!gY^U9O)m)C6VXf_ZwR8D z{lA0#)F5xCt7O5(s6$ug=3ake9%YfMTs`T+nUP8uRpKN_vv6Y+QyPtcixtM#kSy7v16VCx+0oZrH{+0I&Fnix%ZnX;#D;`Ng3PUzC z5`|Hax^aQNDX!;@=_6kZuB8KwZj(0SomuQ2jCxtv%jx}0X3yvH=Je?g#Rsy+O~-)$ 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zYBiSN39p1;gvS{Plc5okisNCwA}X(=X$)WfrKwDO0G9@@x9V$Xn`mh3tMY`RBz$#@ zHFY#~X(|zkiE#<0T3X>?tWOHj1XGn(bo4ULANqnK`T#0T*O0EmFf`QGVZi&VbQ8lc zeLXnVW*Ea8uptI?eO)6%Z8}psR25?)2H!V?r-wmdOgg-_$4tP_7K7I;5GU0Lk7k9y aCmd%Bg(O8sX>b_UZ2pAE@K_B{WBwn#1|Dkw literal 0 HcmV?d00001 From 853c769430dc8483b9d994bccb6f5bf00c8e1852 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:07:02 +0000 Subject: [PATCH 36/49] Compiled vector_search/optimax_gpu --- tig-algorithms/src/vector_search/mod.rs | 3 +- .../optimax_gpu/benchmarker_outbound.rs | 468 ++++++++++++++++++ .../vector_search/optimax_gpu/commercial.rs | 468 ++++++++++++++++++ .../src/vector_search/optimax_gpu/inbound.rs | 468 ++++++++++++++++++ .../optimax_gpu/innovator_outbound.rs | 468 ++++++++++++++++++ .../src/vector_search/optimax_gpu/mod.rs | 4 + .../vector_search/optimax_gpu/open_data.rs | 468 ++++++++++++++++++ tig-algorithms/src/vector_search/template.rs | 26 +- .../wasm/vector_search/optimax_gpu.wasm | Bin 0 -> 129208 bytes 9 files changed, 2348 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vector_search/optimax_gpu/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vector_search/optimax_gpu/commercial.rs create mode 100644 tig-algorithms/src/vector_search/optimax_gpu/inbound.rs create mode 100644 tig-algorithms/src/vector_search/optimax_gpu/innovator_outbound.rs create mode 100644 tig-algorithms/src/vector_search/optimax_gpu/mod.rs create mode 100644 tig-algorithms/src/vector_search/optimax_gpu/open_data.rs create mode 100644 tig-algorithms/wasm/vector_search/optimax_gpu.wasm diff --git a/tig-algorithms/src/vector_search/mod.rs b/tig-algorithms/src/vector_search/mod.rs index ae3472a..1f1c315 100644 --- a/tig-algorithms/src/vector_search/mod.rs +++ b/tig-algorithms/src/vector_search/mod.rs @@ -48,7 +48,8 @@ // c004_a025 -// c004_a026 +pub mod optimax_gpu; +pub use optimax_gpu as c004_a026; // c004_a027 diff --git a/tig-algorithms/src/vector_search/optimax_gpu/benchmarker_outbound.rs b/tig-algorithms/src/vector_search/optimax_gpu/benchmarker_outbound.rs new file mode 100644 index 0000000..dd9b614 --- /dev/null +++ b/tig-algorithms/src/vector_search/optimax_gpu/benchmarker_outbound.rs @@ -0,0 +1,468 @@ +/*! +Copyright 2024 bw-dev36 + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + while i + 3 < a.len() { + let diff0 = a[i] - b[i]; + let diff1 = a[i + 1] - b[i + 1]; + let diff2 = a[i + 2] - b[i + 2]; + let diff3 = a[i + 3] - b[i + 3]; + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; + + if sum > current_min { + return f32::MAX; + } + + i += 4; + } + + while i < a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + + if sum > current_min { + return f32::MAX; + } + + i += 1; + } + + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + if sqr_diff < best.0 { + if let Some(farther_node) = if diff < 0.0 { &node.right } else { &node.left } { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + + if let Some(nearer_node) = if diff < 0.0 { &node.left } else { &node.right } { + stack.push((nearer_node.as_ref(), depth + 1)); + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + pub const KERNEL: Option = Some(CudaKernel { + src: r#" + + extern "C" __global__ void filter_vectors(float* query_mean, float* vectors, float* distances, int num_vectors, int num_dimensions) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_vectors) { + float dist = 0.0; + for (int d = 0; d < num_dimensions; ++d) { + float diff = query_mean[d] - vectors[idx * num_dimensions + d]; + dist += diff * diff; + } + distances[idx] = dist; + } + } + + "#, + + funcs: &["filter_vectors"], + }); + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = cuda_filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + dev, + funcs, + )?; + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + + + + Ok(Some(Solution { + indexes: best_indexes, + })) + } + + #[cfg(feature = "cuda")] + fn cuda_filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let num_vectors = database.len(); + let num_dimensions = 250; + let flattened_database: Vec = database.iter().flatten().cloned().collect(); + let database_dev = dev.htod_sync_copy(&flattened_database)?; + let mean_query_dev = dev.htod_sync_copy(&mean_query_vector)?; + let mut distances_dev = dev.alloc_zeros::(num_vectors)?; + let cfg = LaunchConfig { + block_dim: (256, 1, 1), + grid_dim: ((num_vectors as u32 + 255) / 256, 1, 1), + shared_mem_bytes: 0, + }; + unsafe { + funcs.remove("filter_vectors").unwrap().launch( + cfg, + ( + &mean_query_dev, + &database_dev, + &mut distances_dev, + num_vectors as i32, + num_dimensions as i32, + ), + ) + }?; + let mut distances_host = vec![0.0f32; num_vectors]; + dev.dtoh_sync_copy_into(&distances_dev, &mut distances_host)?; + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, &distance) in distances_host.iter().enumerate() { + let ord_dist = FloatOrd(distance); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if distance < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&[f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + Ok(result) + } + + #[cfg(feature = "cuda")] + fn cuda_build_kd_tree<'a>(subset: &mut [(&'a [f32], usize)], + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> Option>> { + None + } + + #[cfg(feature = "cuda")] + fn cuda_nearest_neighbor_search( + kd_tree: &Option>>, + query: &[f32], + best: &mut (f32, Option), + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result<()> { + Ok(()) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/optimax_gpu/commercial.rs b/tig-algorithms/src/vector_search/optimax_gpu/commercial.rs new file mode 100644 index 0000000..4bdaec5 --- /dev/null +++ b/tig-algorithms/src/vector_search/optimax_gpu/commercial.rs @@ -0,0 +1,468 @@ +/*! +Copyright 2024 bw-dev36 + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + while i + 3 < a.len() { + let diff0 = a[i] - b[i]; + let diff1 = a[i + 1] - b[i + 1]; + let diff2 = a[i + 2] - b[i + 2]; + let diff3 = a[i + 3] - b[i + 3]; + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; + + if sum > current_min { + return f32::MAX; + } + + i += 4; + } + + while i < a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + + if sum > current_min { + return f32::MAX; + } + + i += 1; + } + + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + if sqr_diff < best.0 { + if let Some(farther_node) = if diff < 0.0 { &node.right } else { &node.left } { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + + if let Some(nearer_node) = if diff < 0.0 { &node.left } else { &node.right } { + stack.push((nearer_node.as_ref(), depth + 1)); + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + pub const KERNEL: Option = Some(CudaKernel { + src: r#" + + extern "C" __global__ void filter_vectors(float* query_mean, float* vectors, float* distances, int num_vectors, int num_dimensions) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_vectors) { + float dist = 0.0; + for (int d = 0; d < num_dimensions; ++d) { + float diff = query_mean[d] - vectors[idx * num_dimensions + d]; + dist += diff * diff; + } + distances[idx] = dist; + } + } + + "#, + + funcs: &["filter_vectors"], + }); + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = cuda_filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + dev, + funcs, + )?; + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + + + + Ok(Some(Solution { + indexes: best_indexes, + })) + } + + #[cfg(feature = "cuda")] + fn cuda_filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let num_vectors = database.len(); + let num_dimensions = 250; + let flattened_database: Vec = database.iter().flatten().cloned().collect(); + let database_dev = dev.htod_sync_copy(&flattened_database)?; + let mean_query_dev = dev.htod_sync_copy(&mean_query_vector)?; + let mut distances_dev = dev.alloc_zeros::(num_vectors)?; + let cfg = LaunchConfig { + block_dim: (256, 1, 1), + grid_dim: ((num_vectors as u32 + 255) / 256, 1, 1), + shared_mem_bytes: 0, + }; + unsafe { + funcs.remove("filter_vectors").unwrap().launch( + cfg, + ( + &mean_query_dev, + &database_dev, + &mut distances_dev, + num_vectors as i32, + num_dimensions as i32, + ), + ) + }?; + let mut distances_host = vec![0.0f32; num_vectors]; + dev.dtoh_sync_copy_into(&distances_dev, &mut distances_host)?; + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, &distance) in distances_host.iter().enumerate() { + let ord_dist = FloatOrd(distance); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if distance < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&[f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + Ok(result) + } + + #[cfg(feature = "cuda")] + fn cuda_build_kd_tree<'a>(subset: &mut [(&'a [f32], usize)], + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> Option>> { + None + } + + #[cfg(feature = "cuda")] + fn cuda_nearest_neighbor_search( + kd_tree: &Option>>, + query: &[f32], + best: &mut (f32, Option), + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result<()> { + Ok(()) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/optimax_gpu/inbound.rs b/tig-algorithms/src/vector_search/optimax_gpu/inbound.rs new file mode 100644 index 0000000..23ae821 --- /dev/null +++ b/tig-algorithms/src/vector_search/optimax_gpu/inbound.rs @@ -0,0 +1,468 @@ +/*! +Copyright 2024 bw-dev36 + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + while i + 3 < a.len() { + let diff0 = a[i] - b[i]; + let diff1 = a[i + 1] - b[i + 1]; + let diff2 = a[i + 2] - b[i + 2]; + let diff3 = a[i + 3] - b[i + 3]; + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; + + if sum > current_min { + return f32::MAX; + } + + i += 4; + } + + while i < a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + + if sum > current_min { + return f32::MAX; + } + + i += 1; + } + + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + if sqr_diff < best.0 { + if let Some(farther_node) = if diff < 0.0 { &node.right } else { &node.left } { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + + if let Some(nearer_node) = if diff < 0.0 { &node.left } else { &node.right } { + stack.push((nearer_node.as_ref(), depth + 1)); + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + pub const KERNEL: Option = Some(CudaKernel { + src: r#" + + extern "C" __global__ void filter_vectors(float* query_mean, float* vectors, float* distances, int num_vectors, int num_dimensions) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_vectors) { + float dist = 0.0; + for (int d = 0; d < num_dimensions; ++d) { + float diff = query_mean[d] - vectors[idx * num_dimensions + d]; + dist += diff * diff; + } + distances[idx] = dist; + } + } + + "#, + + funcs: &["filter_vectors"], + }); + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = cuda_filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + dev, + funcs, + )?; + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + + + + Ok(Some(Solution { + indexes: best_indexes, + })) + } + + #[cfg(feature = "cuda")] + fn cuda_filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let num_vectors = database.len(); + let num_dimensions = 250; + let flattened_database: Vec = database.iter().flatten().cloned().collect(); + let database_dev = dev.htod_sync_copy(&flattened_database)?; + let mean_query_dev = dev.htod_sync_copy(&mean_query_vector)?; + let mut distances_dev = dev.alloc_zeros::(num_vectors)?; + let cfg = LaunchConfig { + block_dim: (256, 1, 1), + grid_dim: ((num_vectors as u32 + 255) / 256, 1, 1), + shared_mem_bytes: 0, + }; + unsafe { + funcs.remove("filter_vectors").unwrap().launch( + cfg, + ( + &mean_query_dev, + &database_dev, + &mut distances_dev, + num_vectors as i32, + num_dimensions as i32, + ), + ) + }?; + let mut distances_host = vec![0.0f32; num_vectors]; + dev.dtoh_sync_copy_into(&distances_dev, &mut distances_host)?; + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, &distance) in distances_host.iter().enumerate() { + let ord_dist = FloatOrd(distance); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if distance < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&[f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + Ok(result) + } + + #[cfg(feature = "cuda")] + fn cuda_build_kd_tree<'a>(subset: &mut [(&'a [f32], usize)], + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> Option>> { + None + } + + #[cfg(feature = "cuda")] + fn cuda_nearest_neighbor_search( + kd_tree: &Option>>, + query: &[f32], + best: &mut (f32, Option), + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result<()> { + Ok(()) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/optimax_gpu/innovator_outbound.rs b/tig-algorithms/src/vector_search/optimax_gpu/innovator_outbound.rs new file mode 100644 index 0000000..dc1c7e1 --- /dev/null +++ b/tig-algorithms/src/vector_search/optimax_gpu/innovator_outbound.rs @@ -0,0 +1,468 @@ +/*! +Copyright 2024 bw-dev36 + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + while i + 3 < a.len() { + let diff0 = a[i] - b[i]; + let diff1 = a[i + 1] - b[i + 1]; + let diff2 = a[i + 2] - b[i + 2]; + let diff3 = a[i + 3] - b[i + 3]; + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; + + if sum > current_min { + return f32::MAX; + } + + i += 4; + } + + while i < a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + + if sum > current_min { + return f32::MAX; + } + + i += 1; + } + + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + if sqr_diff < best.0 { + if let Some(farther_node) = if diff < 0.0 { &node.right } else { &node.left } { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + + if let Some(nearer_node) = if diff < 0.0 { &node.left } else { &node.right } { + stack.push((nearer_node.as_ref(), depth + 1)); + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + pub const KERNEL: Option = Some(CudaKernel { + src: r#" + + extern "C" __global__ void filter_vectors(float* query_mean, float* vectors, float* distances, int num_vectors, int num_dimensions) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_vectors) { + float dist = 0.0; + for (int d = 0; d < num_dimensions; ++d) { + float diff = query_mean[d] - vectors[idx * num_dimensions + d]; + dist += diff * diff; + } + distances[idx] = dist; + } + } + + "#, + + funcs: &["filter_vectors"], + }); + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = cuda_filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + dev, + funcs, + )?; + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + + + + Ok(Some(Solution { + indexes: best_indexes, + })) + } + + #[cfg(feature = "cuda")] + fn cuda_filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let num_vectors = database.len(); + let num_dimensions = 250; + let flattened_database: Vec = database.iter().flatten().cloned().collect(); + let database_dev = dev.htod_sync_copy(&flattened_database)?; + let mean_query_dev = dev.htod_sync_copy(&mean_query_vector)?; + let mut distances_dev = dev.alloc_zeros::(num_vectors)?; + let cfg = LaunchConfig { + block_dim: (256, 1, 1), + grid_dim: ((num_vectors as u32 + 255) / 256, 1, 1), + shared_mem_bytes: 0, + }; + unsafe { + funcs.remove("filter_vectors").unwrap().launch( + cfg, + ( + &mean_query_dev, + &database_dev, + &mut distances_dev, + num_vectors as i32, + num_dimensions as i32, + ), + ) + }?; + let mut distances_host = vec![0.0f32; num_vectors]; + dev.dtoh_sync_copy_into(&distances_dev, &mut distances_host)?; + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, &distance) in distances_host.iter().enumerate() { + let ord_dist = FloatOrd(distance); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if distance < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&[f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + Ok(result) + } + + #[cfg(feature = "cuda")] + fn cuda_build_kd_tree<'a>(subset: &mut [(&'a [f32], usize)], + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> Option>> { + None + } + + #[cfg(feature = "cuda")] + fn cuda_nearest_neighbor_search( + kd_tree: &Option>>, + query: &[f32], + best: &mut (f32, Option), + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result<()> { + Ok(()) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/optimax_gpu/mod.rs b/tig-algorithms/src/vector_search/optimax_gpu/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vector_search/optimax_gpu/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vector_search/optimax_gpu/open_data.rs b/tig-algorithms/src/vector_search/optimax_gpu/open_data.rs new file mode 100644 index 0000000..37097ab --- /dev/null +++ b/tig-algorithms/src/vector_search/optimax_gpu/open_data.rs @@ -0,0 +1,468 @@ +/*! +Copyright 2024 bw-dev36 + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use anyhow::Ok; +use tig_challenges::vector_search::*; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +struct KDNode<'a> { + point: &'a [f32], + left: Option>>, + right: Option>>, + index: usize, +} + +impl<'a> KDNode<'a> { + fn new(point: &'a [f32], index: usize) -> Self { + KDNode { + point, + left: None, + right: None, + index, + } + } +} +fn quickselect_by(arr: &mut [(&[f32], usize)], k: usize, compare: &F) +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + if arr.len() <= 1 { + return; + } + + let pivot_index = partition(arr, compare); + if k < pivot_index { + quickselect_by(&mut arr[..pivot_index], k, compare); + } else if k > pivot_index { + quickselect_by(&mut arr[pivot_index + 1..], k - pivot_index - 1, compare); + } +} + +fn partition(arr: &mut [(&[f32], usize)], compare: &F) -> usize +where + F: Fn(&(&[f32], usize), &(&[f32], usize)) -> Ordering, +{ + let pivot_index = arr.len() >> 1; + arr.swap(pivot_index, arr.len() - 1); + + let mut store_index = 0; + for i in 0..arr.len() - 1 { + if compare(&arr[i], &arr[arr.len() - 1]) == Ordering::Less { + arr.swap(i, store_index); + store_index += 1; + } + } + arr.swap(store_index, arr.len() - 1); + store_index +} + +fn build_kd_tree<'a>(points: &mut [(&'a [f32], usize)]) -> Option>> { + if points.is_empty() { + return None; + } + + const NUM_DIMENSIONS: usize = 250; + let mut stack: Vec<(usize, usize, usize, Option<*mut KDNode<'a>>, bool)> = Vec::new(); + let mut root: Option>> = None; + + stack.push((0, points.len(), 0, None, false)); + + while let Some((start, end, depth, parent_ptr, is_left)) = stack.pop() { + if start >= end { + continue; + } + + let axis = depth % NUM_DIMENSIONS; + let median = (start + end) / 2; + quickselect_by(&mut points[start..end], median - start, &|a, b| { + a.0[axis].partial_cmp(&b.0[axis]).unwrap() + }); + + let (median_point, median_index) = points[median]; + let mut new_node = Box::new(KDNode::new(median_point, median_index)); + let new_node_ptr: *mut KDNode = &mut *new_node; + + if let Some(parent_ptr) = parent_ptr { + unsafe { + if is_left { + (*parent_ptr).left = Some(new_node); + } else { + (*parent_ptr).right = Some(new_node); + } + } + } else { + root = Some(new_node); + } + + stack.push((median + 1, end, depth + 1, Some(new_node_ptr), false)); + stack.push((start, median, depth + 1, Some(new_node_ptr), true)); + } + + root +} + +#[inline(always)] +fn squared_euclidean_distance(a: &[f32], b: &[f32]) -> f32 { + let mut sum = 0.0; + for i in 0..a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + } + sum +} + +#[inline(always)] +fn early_stopping_distance(a: &[f32], b: &[f32], current_min: f32) -> f32 { + let mut sum = 0.0; + let mut i = 0; + while i + 3 < a.len() { + let diff0 = a[i] - b[i]; + let diff1 = a[i + 1] - b[i + 1]; + let diff2 = a[i + 2] - b[i + 2]; + let diff3 = a[i + 3] - b[i + 3]; + + sum += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3; + + if sum > current_min { + return f32::MAX; + } + + i += 4; + } + + while i < a.len() { + let diff = a[i] - b[i]; + sum += diff * diff; + + if sum > current_min { + return f32::MAX; + } + + i += 1; + } + + sum +} + +fn nearest_neighbor_search<'a>( + root: &Option>>, + target: &[f32], + best: &mut (f32, Option), +) { + let num_dimensions = target.len(); + let mut stack = Vec::with_capacity(64); + + if let Some(node) = root { + stack.push((node.as_ref(), 0)); + } + + while let Some((node, depth)) = stack.pop() { + let axis = depth % num_dimensions; + let dist = early_stopping_distance(&node.point, target, best.0); + + if dist < best.0 { + best.0 = dist; + best.1 = Some(node.index); + } + + let diff = target[axis] - node.point[axis]; + let sqr_diff = diff * diff; + + if sqr_diff < best.0 { + if let Some(farther_node) = if diff < 0.0 { &node.right } else { &node.left } { + stack.push((farther_node.as_ref(), depth + 1)); + } + } + + if let Some(nearer_node) = if diff < 0.0 { &node.left } else { &node.right } { + stack.push((nearer_node.as_ref(), depth + 1)); + } + } +} + +fn calculate_mean_vector(vectors: &[&[f32]]) -> Vec { + let num_vectors = vectors.len(); + let num_dimensions = 250; + + let mut mean_vector = vec![0.0; num_dimensions]; + + for vector in vectors { + for i in 0..num_dimensions { + mean_vector[i] += vector[i]; + } + } + + for i in 0..num_dimensions { + mean_vector[i] /= num_vectors as f32; + } + + mean_vector +} + +#[derive(Debug)] +struct FloatOrd(f32); + +impl PartialEq for FloatOrd { + fn eq(&self, other: &Self) -> bool { + self.0 == other.0 + } +} + +impl Eq for FloatOrd {} + +impl PartialOrd for FloatOrd { + fn partial_cmp(&self, other: &Self) -> Option { + self.0.partial_cmp(&other.0) + } +} + +impl Ord for FloatOrd { + fn cmp(&self, other: &Self) -> Ordering { + + self.partial_cmp(other).unwrap_or(Ordering::Equal) + } +} + +fn filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, +) -> Vec<(&'a [f32], usize)> { + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, vector) in database.iter().enumerate() { + let dist = squared_euclidean_distance(&mean_query_vector, vector); + let ord_dist = FloatOrd(dist); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if dist < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&'a [f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + result +} + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + ); + + + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + Ok(Some(Solution { + indexes: best_indexes, + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + pub const KERNEL: Option = Some(CudaKernel { + src: r#" + + extern "C" __global__ void filter_vectors(float* query_mean, float* vectors, float* distances, int num_vectors, int num_dimensions) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < num_vectors) { + float dist = 0.0; + for (int d = 0; d < num_dimensions; ++d) { + float diff = query_mean[d] - vectors[idx * num_dimensions + d]; + dist += diff * diff; + } + distances[idx] = dist; + } + } + + "#, + + funcs: &["filter_vectors"], + }); + + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + let query_count = challenge.query_vectors.len(); + + let subset_size = match query_count { + 10..=19 if challenge.difficulty.better_than_baseline <= 470 => 4200, + 10..=19 if challenge.difficulty.better_than_baseline > 470 => 4200, + 20..=28 if challenge.difficulty.better_than_baseline <= 465 => 3000, + 20..=28 if challenge.difficulty.better_than_baseline > 465 => 6000, // need more fuel + 29..=50 if challenge.difficulty.better_than_baseline <= 480 => 2000, + 29..=45 if challenge.difficulty.better_than_baseline > 480 => 6000, + 46..=50 if challenge.difficulty.better_than_baseline > 480 => 5000, // need more fuel + 51..=70 if challenge.difficulty.better_than_baseline <= 480 => 3000, + 51..=70 if challenge.difficulty.better_than_baseline > 480 => 3000, // need more fuel + 71..=100 if challenge.difficulty.better_than_baseline <= 480 => 1500, + 71..=100 if challenge.difficulty.better_than_baseline > 480 => 2500, // need more fuel + _ => 1000, // need more fuel + }; + let subset = cuda_filter_relevant_vectors( + &challenge.vector_database, + &challenge.query_vectors, + subset_size, + dev, + funcs, + )?; + let kd_tree = build_kd_tree(&mut subset.clone()); + + + let mut best_indexes = Vec::with_capacity(challenge.query_vectors.len()); + + for query in challenge.query_vectors.iter() { + let mut best = (std::f32::MAX, None); + nearest_neighbor_search(&kd_tree, query, &mut best); + + if let Some(best_index) = best.1 { + best_indexes.push(best_index); + } + } + + + + + + Ok(Some(Solution { + indexes: best_indexes, + })) + } + + #[cfg(feature = "cuda")] + fn cuda_filter_relevant_vectors<'a>( + database: &'a [Vec], + query_vectors: &[Vec], + k: usize, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + + let query_refs: Vec<&[f32]> = query_vectors.iter().map(|v| &v[..]).collect(); + let mean_query_vector = calculate_mean_vector(&query_refs); + + let num_vectors = database.len(); + let num_dimensions = 250; + let flattened_database: Vec = database.iter().flatten().cloned().collect(); + let database_dev = dev.htod_sync_copy(&flattened_database)?; + let mean_query_dev = dev.htod_sync_copy(&mean_query_vector)?; + let mut distances_dev = dev.alloc_zeros::(num_vectors)?; + let cfg = LaunchConfig { + block_dim: (256, 1, 1), + grid_dim: ((num_vectors as u32 + 255) / 256, 1, 1), + shared_mem_bytes: 0, + }; + unsafe { + funcs.remove("filter_vectors").unwrap().launch( + cfg, + ( + &mean_query_dev, + &database_dev, + &mut distances_dev, + num_vectors as i32, + num_dimensions as i32, + ), + ) + }?; + let mut distances_host = vec![0.0f32; num_vectors]; + dev.dtoh_sync_copy_into(&distances_dev, &mut distances_host)?; + let mut heap: BinaryHeap<(FloatOrd, usize)> = BinaryHeap::with_capacity(k); + + for (index, &distance) in distances_host.iter().enumerate() { + let ord_dist = FloatOrd(distance); + if heap.len() < k { + heap.push((ord_dist, index)); + } else if let Some(&(FloatOrd(top_dist), _)) = heap.peek() { + if distance < top_dist { + heap.pop(); + heap.push((ord_dist, index)); + } + } + } + let result: Vec<(&[f32], usize)> = heap + .into_iter() + .map(|(_, index)| (&database[index][..], index)) + .collect(); + + Ok(result) + } + + #[cfg(feature = "cuda")] + fn cuda_build_kd_tree<'a>(subset: &mut [(&'a [f32], usize)], + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> Option>> { + None + } + + #[cfg(feature = "cuda")] + fn cuda_nearest_neighbor_search( + kd_tree: &Option>>, + query: &[f32], + best: &mut (f32, Option), + dev: &Arc, + funcs: &mut HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result<()> { + Ok(()) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vector_search/template.rs b/tig-algorithms/src/vector_search/template.rs index eddf8a0..0f5fa1e 100644 --- a/tig-algorithms/src/vector_search/template.rs +++ b/tig-algorithms/src/vector_search/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vector_search::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vector_search/optimax_gpu.wasm b/tig-algorithms/wasm/vector_search/optimax_gpu.wasm new file mode 100644 index 0000000000000000000000000000000000000000..4653787f4517c69087b9f5b20ef6b72ef81d0e30 GIT binary patch literal 129208 zcmeFa3%q4jedoDf=l!_nR^6())Gc70O{oP%h03TOmf_LASKz2Hqm-Ro5MI%})Qr$jB3P%GJW;bpq-y6ew3%dCEz4R-tH#@u+$hc} z5r3@`KUthjP(7s?Rf;Q>O4g{6j>xHGX+jyYi~lugf*vTCr9FOQ^+H*GKv7hkTvMg} z`0DtID=PFgS#5w0zqq&*wQBve`m_J+XaB{XpHHIfXWsNPd!t{ATmS4$d-weOPhR~m ze&$Vkqr2k9KZzqBw_aQ8cdOUzJ?~#GOl|IU8f&I5xbU*e|A+tSf*(8gyzZXbC2#(@ zTjS|-&-?L{f8@14b<*U<|7Fw5rv2=Hzw@mZ?|Sv?uJ|8c|Bv4A&wqO7)zfeI=QqT2 zSN-gpu8*(%g|}R{Z!w;~C2q$rZGQ9rth^`w)6HpZU;VoH+S4NcNHUkFo1^MzXm0=4k(TZxlOnnf)aZ%LwDf0U}{(VR~&&33xjMLz4r 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.../clarke_wright/benchmarker_outbound.rs | 124 ++++++++++++++++++ .../clarke_wright/commercial.rs | 124 ++++++++++++++++++ .../vehicle_routing/clarke_wright/inbound.rs | 124 ++++++++++++++++++ .../clarke_wright/innovator_outbound.rs | 124 ++++++++++++++++++ .../src/vehicle_routing/clarke_wright/mod.rs | 4 + .../clarke_wright/open_data.rs | 124 ++++++++++++++++++ tig-algorithms/src/vehicle_routing/mod.rs | 3 +- .../src/vehicle_routing/template.rs | 26 +--- .../wasm/vehicle_routing/clarke_wright.wasm | Bin 0 -> 153037 bytes 9 files changed, 628 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright/commercial.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright/inbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright/innovator_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright/mod.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright/open_data.rs create mode 100644 tig-algorithms/wasm/vehicle_routing/clarke_wright.wasm diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright/benchmarker_outbound.rs b/tig-algorithms/src/vehicle_routing/clarke_wright/benchmarker_outbound.rs new file mode 100644 index 0000000..f6fdcb7 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright/benchmarker_outbound.rs @@ -0,0 +1,124 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::new(); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + scores.sort_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + Ok(Some(Solution { + routes: routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(), + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright/commercial.rs b/tig-algorithms/src/vehicle_routing/clarke_wright/commercial.rs new file mode 100644 index 0000000..03acd66 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright/commercial.rs @@ -0,0 +1,124 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::new(); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + scores.sort_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + Ok(Some(Solution { + routes: routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(), + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright/inbound.rs b/tig-algorithms/src/vehicle_routing/clarke_wright/inbound.rs new file mode 100644 index 0000000..728e063 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright/inbound.rs @@ -0,0 +1,124 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::new(); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + scores.sort_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + Ok(Some(Solution { + routes: routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(), + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright/innovator_outbound.rs b/tig-algorithms/src/vehicle_routing/clarke_wright/innovator_outbound.rs new file mode 100644 index 0000000..d523425 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright/innovator_outbound.rs @@ -0,0 +1,124 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::new(); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + scores.sort_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + Ok(Some(Solution { + routes: routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(), + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright/mod.rs b/tig-algorithms/src/vehicle_routing/clarke_wright/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright/open_data.rs b/tig-algorithms/src/vehicle_routing/clarke_wright/open_data.rs new file mode 100644 index 0000000..101edeb --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright/open_data.rs @@ -0,0 +1,124 @@ +/*! +Copyright 2024 Uncharted Trading Limited + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::new(); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + scores.sort_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + Ok(Some(Solution { + routes: routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(), + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/mod.rs b/tig-algorithms/src/vehicle_routing/mod.rs index 930b4fb..8976e9b 100644 --- a/tig-algorithms/src/vehicle_routing/mod.rs +++ b/tig-algorithms/src/vehicle_routing/mod.rs @@ -1,4 +1,5 @@ -// c002_a001 +pub mod clarke_wright; +pub use clarke_wright as c002_a001; // c002_a002 diff --git a/tig-algorithms/src/vehicle_routing/template.rs b/tig-algorithms/src/vehicle_routing/template.rs index 02a7cab..b5c872b 100644 --- a/tig-algorithms/src/vehicle_routing/template.rs +++ b/tig-algorithms/src/vehicle_routing/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vehicle_routing::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vehicle_routing/clarke_wright.wasm b/tig-algorithms/wasm/vehicle_routing/clarke_wright.wasm new file mode 100644 index 0000000000000000000000000000000000000000..c778d20a973980c14abfbf166fd3603e3b4e6430 GIT binary patch literal 153037 zcmeFa3!GoaRp)sh|M&mj-T#)_wyc)jb+6iX%aUUS64`MwiK@Yt6~(a$9#6>5Zp>_0 zLbow#Su4V%CADMOD2V|Nm>CC5qJ$(e!~$pHKn5Jr3T7sN3EoYZ81R50Ue*gt))pdvSMdT5n)b$#!-o$?H`l|tReU%U zOnDN~43|;*Z{lB!(@Le2CUG1`@yKW;j+>me;xtXle`!r;akUz!`l~0EO1r`Tq{?qv zNmJge)FOW4W~I*CX(dj^>bj2ON>{%+=!n5$Np7Jl@ ze_Tn&ImU4n0Mm#Ah>WTmHdf5s<$BK}BR+dTWNZ_oVqEKY9N za>%`G^WN9*yY^qj-}W7E`5$hX``%mM@dN+-T}N(@-yL83zIZby_s4g|KNug2-xL32 z{4e8s;~$Uji{BsrM0|Jr({U%>(R%O$^o-SlF-7S&W|Q>61Dw= zK;M=&^5*P4Ux;p`1I?^Oo9S+s`UjFZ7tiJNe%{&hrMxx!>?hL0uDN&u_)nis7yQ%F 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.../advanced_heuristics/innovator_outbound.rs | 240 ++++++++++++++++++ .../advanced_heuristics/mod.rs | 4 + .../advanced_heuristics/open_data.rs | 240 ++++++++++++++++++ tig-algorithms/src/vehicle_routing/mod.rs | 3 +- .../src/vehicle_routing/template.rs | 26 +- .../vehicle_routing/advanced_heuristics.wasm | Bin 0 -> 173571 bytes 9 files changed, 1208 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vehicle_routing/advanced_heuristics/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_heuristics/commercial.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_heuristics/inbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_heuristics/innovator_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_heuristics/mod.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_heuristics/open_data.rs create mode 100644 tig-algorithms/wasm/vehicle_routing/advanced_heuristics.wasm diff --git a/tig-algorithms/src/vehicle_routing/advanced_heuristics/benchmarker_outbound.rs b/tig-algorithms/src/vehicle_routing/advanced_heuristics/benchmarker_outbound.rs new file mode 100644 index 0000000..09f6324 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_heuristics/benchmarker_outbound.rs @@ -0,0 +1,240 @@ +/*! +Copyright 2024 CodeAlchemist + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_heuristics/commercial.rs b/tig-algorithms/src/vehicle_routing/advanced_heuristics/commercial.rs new file mode 100644 index 0000000..3a77824 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_heuristics/commercial.rs @@ -0,0 +1,240 @@ +/*! +Copyright 2024 CodeAlchemist + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_heuristics/inbound.rs b/tig-algorithms/src/vehicle_routing/advanced_heuristics/inbound.rs new file mode 100644 index 0000000..a666f12 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_heuristics/inbound.rs @@ -0,0 +1,240 @@ +/*! +Copyright 2024 CodeAlchemist + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_heuristics/innovator_outbound.rs b/tig-algorithms/src/vehicle_routing/advanced_heuristics/innovator_outbound.rs new file mode 100644 index 0000000..35ca488 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_heuristics/innovator_outbound.rs @@ -0,0 +1,240 @@ +/*! +Copyright 2024 CodeAlchemist + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_heuristics/mod.rs b/tig-algorithms/src/vehicle_routing/advanced_heuristics/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_heuristics/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_heuristics/open_data.rs b/tig-algorithms/src/vehicle_routing/advanced_heuristics/open_data.rs new file mode 100644 index 0000000..2fc0fee --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_heuristics/open_data.rs @@ -0,0 +1,240 @@ +/*! +Copyright 2024 CodeAlchemist + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/mod.rs b/tig-algorithms/src/vehicle_routing/mod.rs index 930b4fb..c407db7 100644 --- a/tig-algorithms/src/vehicle_routing/mod.rs +++ b/tig-algorithms/src/vehicle_routing/mod.rs @@ -104,7 +104,8 @@ // c002_a053 -// c002_a054 +pub mod advanced_heuristics; +pub use advanced_heuristics as c002_a054; // c002_a055 diff --git a/tig-algorithms/src/vehicle_routing/template.rs b/tig-algorithms/src/vehicle_routing/template.rs index 02a7cab..b5c872b 100644 --- a/tig-algorithms/src/vehicle_routing/template.rs +++ b/tig-algorithms/src/vehicle_routing/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vehicle_routing::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vehicle_routing/advanced_heuristics.wasm b/tig-algorithms/wasm/vehicle_routing/advanced_heuristics.wasm new file mode 100644 index 0000000000000000000000000000000000000000..1c505285da0850916d05ffae0087b976737944a8 GIT binary patch literal 173571 zcmeFadz@X@Rp)sg_f_{%bxSJSvPz2fIg)M3l42z>lKesuU}yt|l+xxi?ht~zhwiw+FoL)le;`e( zr$e`a1kLxi_Br?7dPsIMH2gDBT=$&)Tzl=c*Iuu^PxSZi|DiaFqWFJ}FS$28dh}>~ z^xnk&#z!MPMV1=f8!M+m0aB{^;kRbLJXkvUQJJS!j;QVtYpWPV<^RyJC)HmZpCpFC;DGnNoz@5t;VVTHIhoD zS+7)*X0_g^RVzuFrj)Eiaa5sJr9lH}B~Hc~q{eZj%b%t_DkF{KTCG+YsjGD*#FaF$ zC%wgSoz?(DZB~-nNTpsGuK+#L@8~)K{&2@*?Uv=r5Z@y*vj(h%xcYfdh^gn<94<5MxAPMiEKKj4J zADBCQt<7Kj<;E|^FKkH~N9#v6NB+5F zCQr9S$yEQ}(f-pQZOT_&v^D86#`*d8-=8Ng@zuK~eLOQ-jgx9xiCQk+nnbz&=&6xM 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z&|s*}gJTly)PmKYYOGfMtZuHMrLjYGgoc4?E)k~csrE*#kvOiFuYR7WQp?k@R?SdJ zSL>s`MctqHrm~fop%zCp!FfxI)yJt1B1WmWt1KjrX_Tw@sRpQg)F7DO=N}`qrROV8 zfz?v@5qnj-!Z^W~B)|f*s#Y4DJSqXzgsOTNY;hvg{OEL|yKEd0@a^LBR5e84kYGWl zxjE7ts5Xf%0%tf!96Bu6*Uqq`JJ`{kZ8Z`wo>;mYjZSm4!^LwuF+Y`Rlbn^6DfY0p zhttB6CGj*+{@JIcWlR6??A7tG!;b3c!lUzDTpZ|p*ulIA79)6 literal 0 HcmV?d00001 From 4d8ba0816599af62eaa70caaeed0516ab38ca583 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:23:16 +0000 Subject: [PATCH 43/49] Compiled vehicle_routing/enhanced_routing --- .../enhanced_routing/benchmarker_outbound.rs | 328 ++++++++++++++++++ .../enhanced_routing/commercial.rs | 328 ++++++++++++++++++ .../enhanced_routing/inbound.rs | 328 ++++++++++++++++++ .../enhanced_routing/innovator_outbound.rs | 328 ++++++++++++++++++ .../vehicle_routing/enhanced_routing/mod.rs | 4 + .../enhanced_routing/open_data.rs | 328 ++++++++++++++++++ tig-algorithms/src/vehicle_routing/mod.rs | 3 +- .../src/vehicle_routing/template.rs | 26 +- .../vehicle_routing/enhanced_routing.wasm | Bin 0 -> 176643 bytes 9 files changed, 1648 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vehicle_routing/enhanced_routing/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/enhanced_routing/commercial.rs create mode 100644 tig-algorithms/src/vehicle_routing/enhanced_routing/inbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/enhanced_routing/innovator_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/enhanced_routing/mod.rs create mode 100644 tig-algorithms/src/vehicle_routing/enhanced_routing/open_data.rs create mode 100644 tig-algorithms/wasm/vehicle_routing/enhanced_routing.wasm diff --git a/tig-algorithms/src/vehicle_routing/enhanced_routing/benchmarker_outbound.rs b/tig-algorithms/src/vehicle_routing/enhanced_routing/benchmarker_outbound.rs new file mode 100644 index 0000000..fa025be --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/enhanced_routing/benchmarker_outbound.rs @@ -0,0 +1,328 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + if let Some(best_sol) = &best_solution { + let mut solution = Solution { + routes: best_sol.routes.clone() + }; + if try_inter_route_swap(&mut solution, &challenge.distance_matrix, &challenge.demands, challenge.max_capacity) { + let new_cost = calculate_solution_cost(&solution, &challenge.distance_matrix); + if new_cost < best_cost { + best_solution = Some(solution); + } + } + } + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + + let max_distance = challenge.distance_matrix.iter().flat_map(|row| row.iter()).cloned().max().unwrap_or(0); + let threshold = max_distance / 2; + + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + if challenge.distance_matrix[i][j] <= threshold { + savings.push((0.0, i as u8, j as u8)); + } + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + + +#[inline] +fn try_inter_route_swap( + solution: &mut Solution, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> bool { + let mut improved = false; + let num_routes = solution.routes.len(); + + for i in 0..num_routes { + for j in i + 1..num_routes { + if let Some(better_routes) = find_best_swap( + &solution.routes[i], + &solution.routes[j], + distance_matrix, + demands, + max_capacity + ) { + solution.routes[i] = better_routes.0; + solution.routes[j] = better_routes.1; + improved = true; + } + } + } + + improved +} + +#[inline] +fn find_best_swap( + route1: &Vec, + route2: &Vec, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> Option<(Vec, Vec)> { + let mut best_improvement = 0; + let mut best_swap = None; + + for i in 1..route1.len() - 1 { + for j in 1..route2.len() - 1 { + let route1_demand: i32 = route1.iter().map(|&n| demands[n]).sum(); + let route2_demand: i32 = route2.iter().map(|&n| demands[n]).sum(); + let demand_delta = demands[route2[j]] - demands[route1[i]]; + + if route1_demand + demand_delta > max_capacity || + route2_demand - demand_delta > max_capacity { + continue; + } + + let old_cost = distance_matrix[route1[i-1]][route1[i]] + + distance_matrix[route1[i]][route1[i+1]] + + distance_matrix[route2[j-1]][route2[j]] + + distance_matrix[route2[j]][route2[j+1]]; + + let new_cost = distance_matrix[route1[i-1]][route2[j]] + + distance_matrix[route2[j]][route1[i+1]] + + distance_matrix[route2[j-1]][route1[i]] + + distance_matrix[route1[i]][route2[j+1]]; + + let improvement = old_cost - new_cost; + if improvement > best_improvement { + best_improvement = improvement; + let mut new_route1 = route1.clone(); + let mut new_route2 = route2.clone(); + new_route1[i] = route2[j]; + new_route2[j] = route1[i]; + best_swap = Some((new_route1, new_route2)); + } + } + } + + best_swap +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/enhanced_routing/commercial.rs b/tig-algorithms/src/vehicle_routing/enhanced_routing/commercial.rs new file mode 100644 index 0000000..56a44bf --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/enhanced_routing/commercial.rs @@ -0,0 +1,328 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + if let Some(best_sol) = &best_solution { + let mut solution = Solution { + routes: best_sol.routes.clone() + }; + if try_inter_route_swap(&mut solution, &challenge.distance_matrix, &challenge.demands, challenge.max_capacity) { + let new_cost = calculate_solution_cost(&solution, &challenge.distance_matrix); + if new_cost < best_cost { + best_solution = Some(solution); + } + } + } + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + + let max_distance = challenge.distance_matrix.iter().flat_map(|row| row.iter()).cloned().max().unwrap_or(0); + let threshold = max_distance / 2; + + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + if challenge.distance_matrix[i][j] <= threshold { + savings.push((0.0, i as u8, j as u8)); + } + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + + +#[inline] +fn try_inter_route_swap( + solution: &mut Solution, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> bool { + let mut improved = false; + let num_routes = solution.routes.len(); + + for i in 0..num_routes { + for j in i + 1..num_routes { + if let Some(better_routes) = find_best_swap( + &solution.routes[i], + &solution.routes[j], + distance_matrix, + demands, + max_capacity + ) { + solution.routes[i] = better_routes.0; + solution.routes[j] = better_routes.1; + improved = true; + } + } + } + + improved +} + +#[inline] +fn find_best_swap( + route1: &Vec, + route2: &Vec, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> Option<(Vec, Vec)> { + let mut best_improvement = 0; + let mut best_swap = None; + + for i in 1..route1.len() - 1 { + for j in 1..route2.len() - 1 { + let route1_demand: i32 = route1.iter().map(|&n| demands[n]).sum(); + let route2_demand: i32 = route2.iter().map(|&n| demands[n]).sum(); + let demand_delta = demands[route2[j]] - demands[route1[i]]; + + if route1_demand + demand_delta > max_capacity || + route2_demand - demand_delta > max_capacity { + continue; + } + + let old_cost = distance_matrix[route1[i-1]][route1[i]] + + distance_matrix[route1[i]][route1[i+1]] + + distance_matrix[route2[j-1]][route2[j]] + + distance_matrix[route2[j]][route2[j+1]]; + + let new_cost = distance_matrix[route1[i-1]][route2[j]] + + distance_matrix[route2[j]][route1[i+1]] + + distance_matrix[route2[j-1]][route1[i]] + + distance_matrix[route1[i]][route2[j+1]]; + + let improvement = old_cost - new_cost; + if improvement > best_improvement { + best_improvement = improvement; + let mut new_route1 = route1.clone(); + let mut new_route2 = route2.clone(); + new_route1[i] = route2[j]; + new_route2[j] = route1[i]; + best_swap = Some((new_route1, new_route2)); + } + } + } + + best_swap +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/enhanced_routing/inbound.rs b/tig-algorithms/src/vehicle_routing/enhanced_routing/inbound.rs new file mode 100644 index 0000000..ed90db1 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/enhanced_routing/inbound.rs @@ -0,0 +1,328 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + if let Some(best_sol) = &best_solution { + let mut solution = Solution { + routes: best_sol.routes.clone() + }; + if try_inter_route_swap(&mut solution, &challenge.distance_matrix, &challenge.demands, challenge.max_capacity) { + let new_cost = calculate_solution_cost(&solution, &challenge.distance_matrix); + if new_cost < best_cost { + best_solution = Some(solution); + } + } + } + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + + let max_distance = challenge.distance_matrix.iter().flat_map(|row| row.iter()).cloned().max().unwrap_or(0); + let threshold = max_distance / 2; + + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + if challenge.distance_matrix[i][j] <= threshold { + savings.push((0.0, i as u8, j as u8)); + } + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + + +#[inline] +fn try_inter_route_swap( + solution: &mut Solution, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> bool { + let mut improved = false; + let num_routes = solution.routes.len(); + + for i in 0..num_routes { + for j in i + 1..num_routes { + if let Some(better_routes) = find_best_swap( + &solution.routes[i], + &solution.routes[j], + distance_matrix, + demands, + max_capacity + ) { + solution.routes[i] = better_routes.0; + solution.routes[j] = better_routes.1; + improved = true; + } + } + } + + improved +} + +#[inline] +fn find_best_swap( + route1: &Vec, + route2: &Vec, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> Option<(Vec, Vec)> { + let mut best_improvement = 0; + let mut best_swap = None; + + for i in 1..route1.len() - 1 { + for j in 1..route2.len() - 1 { + let route1_demand: i32 = route1.iter().map(|&n| demands[n]).sum(); + let route2_demand: i32 = route2.iter().map(|&n| demands[n]).sum(); + let demand_delta = demands[route2[j]] - demands[route1[i]]; + + if route1_demand + demand_delta > max_capacity || + route2_demand - demand_delta > max_capacity { + continue; + } + + let old_cost = distance_matrix[route1[i-1]][route1[i]] + + distance_matrix[route1[i]][route1[i+1]] + + distance_matrix[route2[j-1]][route2[j]] + + distance_matrix[route2[j]][route2[j+1]]; + + let new_cost = distance_matrix[route1[i-1]][route2[j]] + + distance_matrix[route2[j]][route1[i+1]] + + distance_matrix[route2[j-1]][route1[i]] + + distance_matrix[route1[i]][route2[j+1]]; + + let improvement = old_cost - new_cost; + if improvement > best_improvement { + best_improvement = improvement; + let mut new_route1 = route1.clone(); + let mut new_route2 = route2.clone(); + new_route1[i] = route2[j]; + new_route2[j] = route1[i]; + best_swap = Some((new_route1, new_route2)); + } + } + } + + best_swap +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/enhanced_routing/innovator_outbound.rs b/tig-algorithms/src/vehicle_routing/enhanced_routing/innovator_outbound.rs new file mode 100644 index 0000000..50327a4 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/enhanced_routing/innovator_outbound.rs @@ -0,0 +1,328 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + if let Some(best_sol) = &best_solution { + let mut solution = Solution { + routes: best_sol.routes.clone() + }; + if try_inter_route_swap(&mut solution, &challenge.distance_matrix, &challenge.demands, challenge.max_capacity) { + let new_cost = calculate_solution_cost(&solution, &challenge.distance_matrix); + if new_cost < best_cost { + best_solution = Some(solution); + } + } + } + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + + let max_distance = challenge.distance_matrix.iter().flat_map(|row| row.iter()).cloned().max().unwrap_or(0); + let threshold = max_distance / 2; + + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + if challenge.distance_matrix[i][j] <= threshold { + savings.push((0.0, i as u8, j as u8)); + } + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + + +#[inline] +fn try_inter_route_swap( + solution: &mut Solution, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> bool { + let mut improved = false; + let num_routes = solution.routes.len(); + + for i in 0..num_routes { + for j in i + 1..num_routes { + if let Some(better_routes) = find_best_swap( + &solution.routes[i], + &solution.routes[j], + distance_matrix, + demands, + max_capacity + ) { + solution.routes[i] = better_routes.0; + solution.routes[j] = better_routes.1; + improved = true; + } + } + } + + improved +} + +#[inline] +fn find_best_swap( + route1: &Vec, + route2: &Vec, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> Option<(Vec, Vec)> { + let mut best_improvement = 0; + let mut best_swap = None; + + for i in 1..route1.len() - 1 { + for j in 1..route2.len() - 1 { + let route1_demand: i32 = route1.iter().map(|&n| demands[n]).sum(); + let route2_demand: i32 = route2.iter().map(|&n| demands[n]).sum(); + let demand_delta = demands[route2[j]] - demands[route1[i]]; + + if route1_demand + demand_delta > max_capacity || + route2_demand - demand_delta > max_capacity { + continue; + } + + let old_cost = distance_matrix[route1[i-1]][route1[i]] + + distance_matrix[route1[i]][route1[i+1]] + + distance_matrix[route2[j-1]][route2[j]] + + distance_matrix[route2[j]][route2[j+1]]; + + let new_cost = distance_matrix[route1[i-1]][route2[j]] + + distance_matrix[route2[j]][route1[i+1]] + + distance_matrix[route2[j-1]][route1[i]] + + distance_matrix[route1[i]][route2[j+1]]; + + let improvement = old_cost - new_cost; + if improvement > best_improvement { + best_improvement = improvement; + let mut new_route1 = route1.clone(); + let mut new_route2 = route2.clone(); + new_route1[i] = route2[j]; + new_route2[j] = route1[i]; + best_swap = Some((new_route1, new_route2)); + } + } + } + + best_swap +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/enhanced_routing/mod.rs b/tig-algorithms/src/vehicle_routing/enhanced_routing/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/enhanced_routing/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/enhanced_routing/open_data.rs b/tig-algorithms/src/vehicle_routing/enhanced_routing/open_data.rs new file mode 100644 index 0000000..22705ca --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/enhanced_routing/open_data.rs @@ -0,0 +1,328 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + if let Some(best_sol) = &best_solution { + let mut solution = Solution { + routes: best_sol.routes.clone() + }; + if try_inter_route_swap(&mut solution, &challenge.distance_matrix, &challenge.demands, challenge.max_capacity) { + let new_cost = calculate_solution_cost(&solution, &challenge.distance_matrix); + if new_cost < best_cost { + best_solution = Some(solution); + } + } + } + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + + let max_distance = challenge.distance_matrix.iter().flat_map(|row| row.iter()).cloned().max().unwrap_or(0); + let threshold = max_distance / 2; + + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + if challenge.distance_matrix[i][j] <= threshold { + savings.push((0.0, i as u8, j as u8)); + } + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + + +#[inline] +fn try_inter_route_swap( + solution: &mut Solution, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> bool { + let mut improved = false; + let num_routes = solution.routes.len(); + + for i in 0..num_routes { + for j in i + 1..num_routes { + if let Some(better_routes) = find_best_swap( + &solution.routes[i], + &solution.routes[j], + distance_matrix, + demands, + max_capacity + ) { + solution.routes[i] = better_routes.0; + solution.routes[j] = better_routes.1; + improved = true; + } + } + } + + improved +} + +#[inline] +fn find_best_swap( + route1: &Vec, + route2: &Vec, + distance_matrix: &Vec>, + demands: &Vec, + max_capacity: i32 +) -> Option<(Vec, Vec)> { + let mut best_improvement = 0; + let mut best_swap = None; + + for i in 1..route1.len() - 1 { + for j in 1..route2.len() - 1 { + let route1_demand: i32 = route1.iter().map(|&n| demands[n]).sum(); + let route2_demand: i32 = route2.iter().map(|&n| demands[n]).sum(); + let demand_delta = demands[route2[j]] - demands[route1[i]]; + + if route1_demand + demand_delta > max_capacity || + route2_demand - demand_delta > max_capacity { + continue; + } + + let old_cost = distance_matrix[route1[i-1]][route1[i]] + + distance_matrix[route1[i]][route1[i+1]] + + distance_matrix[route2[j-1]][route2[j]] + + distance_matrix[route2[j]][route2[j+1]]; + + let new_cost = distance_matrix[route1[i-1]][route2[j]] + + distance_matrix[route2[j]][route1[i+1]] + + distance_matrix[route2[j-1]][route1[i]] + + distance_matrix[route1[i]][route2[j+1]]; + + let improvement = old_cost - new_cost; + if improvement > best_improvement { + best_improvement = improvement; + let mut new_route1 = route1.clone(); + let mut new_route2 = route2.clone(); + new_route1[i] = route2[j]; + new_route2[j] = route1[i]; + best_swap = Some((new_route1, new_route2)); + } + } + } + + best_swap +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/mod.rs b/tig-algorithms/src/vehicle_routing/mod.rs index 930b4fb..bc5ff2e 100644 --- a/tig-algorithms/src/vehicle_routing/mod.rs +++ b/tig-algorithms/src/vehicle_routing/mod.rs @@ -102,7 +102,8 @@ // c002_a052 -// c002_a053 +pub mod enhanced_routing; +pub use enhanced_routing as c002_a053; // c002_a054 diff --git a/tig-algorithms/src/vehicle_routing/template.rs b/tig-algorithms/src/vehicle_routing/template.rs index 02a7cab..b5c872b 100644 --- a/tig-algorithms/src/vehicle_routing/template.rs +++ b/tig-algorithms/src/vehicle_routing/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vehicle_routing::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vehicle_routing/enhanced_routing.wasm b/tig-algorithms/wasm/vehicle_routing/enhanced_routing.wasm new file mode 100644 index 0000000000000000000000000000000000000000..a8b736e6ad6696c934711ae6647d77c88c6c3a19 GIT binary patch literal 176643 zcmeFadz@X@Rp)sg_f_{%btRQ;StUjL9Lct1NwJa`NwJ-{b|ro&vSTOlctU6z(*pzC zGU=48Q35GR$UfZzW1IVj-x1w|4Dq&UFor7$Kqpm zCH6Ny7U?On)ab5QITZ?!Qq>Q?HT&hk(#em?JhgIEb&pzGML|+WLY1ROk4AUZ!?QJf zG!%@F#7Ah2H}y-Q^z-qPaayUg(YlT8i1(HN>UrC)GKS$MLkIxBeWV<0WyvFSqn;|>bmi2T&c$Ii|@Lt3KVN%$;E#OI`~Kk%z4uV`>+#5s#*vR( z-}9mPc4L9BV*0CtMORMSe-a`Cn#%JoN2jXaw6 z>8+JX7rV$aAC{P@nBIGPl7><$F6#L||I^QYt+pqM+M}V7j3T?D=mu3DZmv=`R~>FH zG?FL&FHSE++oFhu0It&W|CM)|xrTT3Syi897cT>10oXU5|JVP!0JHiAbGuW3SPe)L zQkc!@(IifymLC`B+tWtgoO?G(|GjiRdJlN4 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zt5?Vx@JPgnH$`vv4laO}37ElIa2}L_a!|pCq5>UXvZ*YHsTiqkz(E$oLsDbCcB7@S^*#(YyqvH9YnYj#ezfNC};!i zU==K(1>-=9cK`mDIz+!3s`ULx21bGHDa^3n6#bJZ}P~T$Y ztpRr60g&w^2*RzN*jh>RdOYk?VQQu<8rLJ1qsYyZ!FRgImgBpkbS==06dQQ1ls&gv2HeHB0dq=CpWUptqFUygc z-0vi?3&<~~RggQl`1%`zHlioB(jam8(k8GOYyt8?%j9R*lN}fDpC>;iR$_vjIIXHf zWiwkz`z}q`jKa)A!B47JI9WeAIwhynr1Y3lT5D27k2y}M+a^|XTiv4`7nIieYQbgK z^rEA9v9B=R9H7|>UwbtwVd(!UdNPllkhws<0p<9e;\method\` +`\breakthrough\` ## Making a Submission From 101ca13ca3d695145157acffade4cca50365196c Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Sat, 18 Jan 2025 14:18:43 +0000 Subject: [PATCH 45/49] Remove attribution section from evidence template. --- tig-breakthroughs/README.md | 2 +- tig-breakthroughs/{knapsack => }/evidence.md | 20 +-- tig-breakthroughs/satisfiability/evidence.md | 157 ------------------ tig-breakthroughs/vector_search/evidence.md | 157 ------------------ tig-breakthroughs/vehicle_routing/evidence.md | 157 ------------------ 5 files changed, 5 insertions(+), 488 deletions(-) rename tig-breakthroughs/{knapsack => }/evidence.md (92%) delete mode 100644 tig-breakthroughs/satisfiability/evidence.md delete mode 100644 tig-breakthroughs/vector_search/evidence.md delete mode 100644 tig-breakthroughs/vehicle_routing/evidence.md diff --git a/tig-breakthroughs/README.md b/tig-breakthroughs/README.md index b641eeb..369b16b 100644 --- a/tig-breakthroughs/README.md +++ b/tig-breakthroughs/README.md @@ -14,7 +14,7 @@ Each submissions is committed to their own branch with the naming pattern: * [Voting Guidelines for Token Holders](../docs/guides/voting.md) 2. Email the following to `breakthroughs@tig.foundation` with subject "Breakthrough Submission (``)": - * **Evidence form**: copy & fill in `evidence.md` from the relevant challenges folder (e.g. [`knapsack/evidence.md`](./knapsack/evidence.md)). Of particular importance is Section 1 which describes your breakthrough + * **Evidence form**: copy & fill in [`evidence.md`](./evidence.md). Of particular importance is Section 1 which describes your breakthrough * **Invention assignment**: copy & replace [invention_assignment.doc](../docs/agreements/invention_assignment.doc) the highlighted parts. Inventor and witness must sign. diff --git a/tig-breakthroughs/knapsack/evidence.md b/tig-breakthroughs/evidence.md similarity index 92% rename from tig-breakthroughs/knapsack/evidence.md rename to tig-breakthroughs/evidence.md index 033a260..3218dd9 100644 --- a/tig-breakthroughs/knapsack/evidence.md +++ b/tig-breakthroughs/evidence.md @@ -33,7 +33,7 @@ IT IS IMPORTANT THAT THIS SECTION IS COMPLETED IN SUFFICIENT DETAIL TO FULLY DES PLEASE IDENTIFY WHICH TIG CHALLENGE THE ALGORITHMIC METHOD ADDRESSES. -> knapsack +> YOUR RESPONSE HERE (options are satisfiability, vehicle_routing, knapsack, or vector_search) PLEASE DESCRIBE THE ALGORITHMIC METHOD AND THE PROBLEM THAT IT SOLVES. @@ -51,18 +51,6 @@ TO THE EXTENT THAT YOU HAVE IMPLEMENTED THE ALGORITHMIC METHOD IN CODE YOU SHOUL ## SECTION 3 -### ATTRIBUTION - -PLEASE PROVIDE THE IDENTITY OF THE CREATOR OF THE ALGORITHMIC METHOD (this should be a natural person or legal entity. If an artificial intelligence has been used to assist in the creation of the algorithmic method then the creator is the operator of the artificial intelligence) - -> YOUR RESPONSE HERE - -PLEASE PROVIDE EVIDENCE OF AUTHORSHIP WHERE IT IS AVAILABLE. - -> YOUR RESPONSE HERE - -## SECTION 4 - ### NOVELTY AND INVENTIVENESS To support your claim that an algorithmic method is novel and inventive, you should provide evidence that demonstrates both its uniqueness (novelty) and its non-obviousness (inventiveness) in relation to the existing state of the art. @@ -132,7 +120,7 @@ To support your claim that an algorithmic method is novel and inventive, you sho > YOUR RESPONSE HERE -## SECTION 5 +## SECTION 4 ### EVIDENCE TO SUPPORT PATENTABILITY @@ -140,7 +128,7 @@ To support your claim that an algorithmic method is novel and inventive, you sho > YOUR RESPONSE HERE -## SECTION 6 +## SECTION 5 ### SUGGESTED APPLICATIONS @@ -148,7 +136,7 @@ To support your claim that an algorithmic method is novel and inventive, you sho > YOUR RESPONSE HERE -## SECTION 7 +## SECTION 6 ### ANY OTHER INFORMATION diff --git a/tig-breakthroughs/satisfiability/evidence.md b/tig-breakthroughs/satisfiability/evidence.md deleted file mode 100644 index adc25ce..0000000 --- a/tig-breakthroughs/satisfiability/evidence.md +++ /dev/null @@ -1,157 +0,0 @@ -# EVIDENCE - -THIS IS A TEMPLATE FOR EVIDENCE TO BE SUBMITTED IN SUPPORT OF A REQUEST FOR ELIGIBILITY FOR BREAKTHROUGH REWARDS - -## OVERVIEW - -- TIG TOKEN HOLDERS WILL VOTE ON WHETHER YOUR ALGORITHMIC METHOD IS ELIGIBLE FOR BREAKTHROUGH REWARDS - -- TIG TOKEN HOLDERS ARE FREE TO VOTE AS THEY LIKE BUT HAVE BEEN ADVISED THAT IF THEY WANT TO MAXIMISE THE VALUE OF THE TOKENS THAT THEY HOLD, THEN THEY SHOULD BE SATISFYING THEMSELVES THAT ALGORITHIC METHODS THAT THEY VOTE AS ELIGIBLE WILL BE BOTH NOVEL AND INVENTIVE - -- THE REASON WHY NOVELTY AND INVENTIVENESS ARE IMPORTANT ATTRIBUTES IS BECAUSE THEY ARE PREREQUISITES OF PATENTATBILITY. - -- **THE PURPOSE OF THIS DOCUMENT IS TO:** - - CAPTURE A DESCRIPTION OF THE ALGORITHMIC METHOD THAT YOU WANT TO BE CONSIDERED FOR ELIGIBILTY. - - - TO IDENTIFY THE CREATOR OF THE ALGORITHMIC METHOD. - - - TO PROMPT YOU TO PROVIDE THE BEST EVIDNCE TO SUPPORT THE CASE THAT THE ALGORITHMIC METHOD IS NOVEL AND INVENTIVE. - - - TO PROMPT YOU TO PROVIDE SUGGESTIONS FOR REAL WORLD APPLICATIONS OF YOUR ALGORITHMIC METHOD WHERE YIOU CAN. - -WHEN PROVIDING EVIDENCE, YOU MAY CITE LINKS TO EXTERNAL DATA SOURCES. - -## UNIQUE ALGORITHM IDENTIFIER (UAI) - -> UAI PLACEHOLDER - ASSIGNED BY TIG PROTOCOL - -## SECTION 1 - -IT IS IMPORTANT THAT THIS SECTION IS COMPLETED IN SUFFICIENT DETAIL TO FULLY DESCRIBE THE METHOD BECAUSE THIS WILL DEFINE THE METHOD THAT IS THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. - -### DESCRIPTION OF ALGORITHMIC METHOD - -PLEASE IDENTIFY WHICH TIG CHALLENGE THE ALGORITHMIC METHOD ADDRESSES. - -> satisfiability - -PLEASE DESCRIBE THE ALGORITHMIC METHOD AND THE PROBLEM THAT IT SOLVES. - -> YOUR RESPONSE HERE - -## SECTION 2 - -THE COPYRIGHT IN THE IMPLEMENTATION WILL BE THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. - -### IMPLEMENTATION OF ALGORITHMIC METHOD - -TO THE EXTENT THAT YOU HAVE IMPLEMENTED THE ALGORITHMIC METHOD IN CODE YOU SHOULD IDENTIFY THE CODE AND SUBMIT IT WITH THIS - -> YOUR RESPONSE HERE - -## SECTION 3 - -### ATTRIBUTION - -PLEASE PROVIDE THE IDENTITY OF THE CREATOR OF THE ALGORITHMIC METHOD (this should be a natural person or legal entity. If an artificial intelligence has been used to assist in the creation of the algorithmic method then the creator is the operator of the artificial intelligence) - -> YOUR RESPONSE HERE - -PLEASE PROVIDE EVIDENCE OF AUTHORSHIP WHERE IT IS AVAILABLE. - -> YOUR RESPONSE HERE - -## SECTION 4 - -### NOVELTY AND INVENTIVENESS - -To support your claim that an algorithmic method is novel and inventive, you should provide evidence that demonstrates both its uniqueness (novelty) and its non-obviousness (inventiveness) in relation to the existing state of the art. - -### Establish the State of the Art - -- **Prior Art Search:** Conduct a comprehensive review of existing methods, algorithms, patents, academic papers, and industry practices to identify prior art in the domain. - - Highlight documents and technologies most closely related to your method. - - > YOUR RESPONSE HERE - - - Show where these existing methods fall short or lack the features your algorithmic method provides. - - > YOUR RESPONSE HERE - -- **Technical Context:** Describe the common approaches and challenges in the field prior to your innovation. - - > YOUR RESPONSE HERE - -### Evidence of Novelty - -- **Unique Features:** List the features, mechanisms, or aspects of your algorithmic method that are absent in prior art. - - > YOUR RESPONSE HERE - -- **New Problem Solved:** Describe how your algorithmic method provides a novel solution to an existing problem. - - > YOUR RESPONSE HERE - -- **Comparative Analysis:** Use a side-by-side comparison table to highlight the differences between your method and similar existing methods, clearly showing what’s new. - - > YOUR RESPONSE HERE - -### Evidence Inventiveness of Inventiveness - -- **Non-Obviousness:** Argue why a skilled person in the field would not have arrived at your method by simply combining existing ideas or extending known techniques. - - Demonstrate that the development involved an inventive step beyond straightforward application of existing principles. - - Unexpected Results: Highlight results or benefits that could not have been predicted based on prior art. - - > YOUR RESPONSE HERE - -- **Advantages:** Provide evidence of how your algorithm outperforms or offers significant advantages over existing methods, such as: - - Increased efficiency. - - Greater accuracy. - - Reduced computational complexity. - - Novel applications. - - > YOUR RESPONSE HERE - -### Supporting Data - -- **Experimental Results:** Include performance benchmarks, simulations, or empirical data that substantiate your claims of novelty and inventiveness. - - > YOUR RESPONSE HERE - -- **Proof of Concept:** If possible, show a working prototype or implementation that validates the method. - - > YOUR RESPONSE HERE - -### Citations and Expert Opinions - -- **Literature Gaps:** Affirm, to the best of your knowledge, the absence of similar solutions in published literature to reinforce your novelty claim. - - > YOUR RESPONSE HERE - -- **Endorsements:** Include reviews or opinions from industry experts, researchers, or peer-reviewed publications that evidences the novelty and impact of your algorithm. - - > YOUR RESPONSE HERE - -## SECTION 5 - -### EVIDENCE TO SUPPORT PATENTABILITY - -- **Development Records:** Please provide documentation of the invention process, including notes, sketches, and software versions, to establish a timeline of your innovation. - - > YOUR RESPONSE HERE - -## SECTION 6 - -### SUGGESTED APPLICATIONS - -- Please provide suggestions for any real world applications of your abstract algorithmic method that occur to you. - - > YOUR RESPONSE HERE - -## SECTION 7 - -### ANY OTHER INFORMATION - -- Please provide any other evidence or argument that you think might help support you request for eligibility for Breakthrough Rewards. - - > YOUR RESPONSE HERE \ No newline at end of file diff --git a/tig-breakthroughs/vector_search/evidence.md b/tig-breakthroughs/vector_search/evidence.md deleted file mode 100644 index 16965b1..0000000 --- a/tig-breakthroughs/vector_search/evidence.md +++ /dev/null @@ -1,157 +0,0 @@ -# EVIDENCE - -THIS IS A TEMPLATE FOR EVIDENCE TO BE SUBMITTED IN SUPPORT OF A REQUEST FOR ELIGIBILITY FOR BREAKTHROUGH REWARDS - -## OVERVIEW - -- TIG TOKEN HOLDERS WILL VOTE ON WHETHER YOUR ALGORITHMIC METHOD IS ELIGIBLE FOR BREAKTHROUGH REWARDS - -- TIG TOKEN HOLDERS ARE FREE TO VOTE AS THEY LIKE BUT HAVE BEEN ADVISED THAT IF THEY WANT TO MAXIMISE THE VALUE OF THE TOKENS THAT THEY HOLD, THEN THEY SHOULD BE SATISFYING THEMSELVES THAT ALGORITHIC METHODS THAT THEY VOTE AS ELIGIBLE WILL BE BOTH NOVEL AND INVENTIVE - -- THE REASON WHY NOVELTY AND INVENTIVENESS ARE IMPORTANT ATTRIBUTES IS BECAUSE THEY ARE PREREQUISITES OF PATENTATBILITY. - -- **THE PURPOSE OF THIS DOCUMENT IS TO:** - - CAPTURE A DESCRIPTION OF THE ALGORITHMIC METHOD THAT YOU WANT TO BE CONSIDERED FOR ELIGIBILTY. - - - TO IDENTIFY THE CREATOR OF THE ALGORITHMIC METHOD. - - - TO PROMPT YOU TO PROVIDE THE BEST EVIDNCE TO SUPPORT THE CASE THAT THE ALGORITHMIC METHOD IS NOVEL AND INVENTIVE. - - - TO PROMPT YOU TO PROVIDE SUGGESTIONS FOR REAL WORLD APPLICATIONS OF YOUR ALGORITHMIC METHOD WHERE YIOU CAN. - -WHEN PROVIDING EVIDENCE, YOU MAY CITE LINKS TO EXTERNAL DATA SOURCES. - -## UNIQUE ALGORITHM IDENTIFIER (UAI) - -> UAI PLACEHOLDER - ASSIGNED BY TIG PROTOCOL - -## SECTION 1 - -IT IS IMPORTANT THAT THIS SECTION IS COMPLETED IN SUFFICIENT DETAIL TO FULLY DESCRIBE THE METHOD BECAUSE THIS WILL DEFINE THE METHOD THAT IS THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. - -### DESCRIPTION OF ALGORITHMIC METHOD - -PLEASE IDENTIFY WHICH TIG CHALLENGE THE ALGORITHMIC METHOD ADDRESSES. - -> vector_search - -PLEASE DESCRIBE THE ALGORITHMIC METHOD AND THE PROBLEM THAT IT SOLVES. - -> YOUR RESPONSE HERE - -## SECTION 2 - -THE COPYRIGHT IN THE IMPLEMENTATION WILL BE THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. - -### IMPLEMENTATION OF ALGORITHMIC METHOD - -TO THE EXTENT THAT YOU HAVE IMPLEMENTED THE ALGORITHMIC METHOD IN CODE YOU SHOULD IDENTIFY THE CODE AND SUBMIT IT WITH THIS - -> YOUR RESPONSE HERE - -## SECTION 3 - -### ATTRIBUTION - -PLEASE PROVIDE THE IDENTITY OF THE CREATOR OF THE ALGORITHMIC METHOD (this should be a natural person or legal entity. If an artificial intelligence has been used to assist in the creation of the algorithmic method then the creator is the operator of the artificial intelligence) - -> YOUR RESPONSE HERE - -PLEASE PROVIDE EVIDENCE OF AUTHORSHIP WHERE IT IS AVAILABLE. - -> YOUR RESPONSE HERE - -## SECTION 4 - -### NOVELTY AND INVENTIVENESS - -To support your claim that an algorithmic method is novel and inventive, you should provide evidence that demonstrates both its uniqueness (novelty) and its non-obviousness (inventiveness) in relation to the existing state of the art. - -### Establish the State of the Art - -- **Prior Art Search:** Conduct a comprehensive review of existing methods, algorithms, patents, academic papers, and industry practices to identify prior art in the domain. - - Highlight documents and technologies most closely related to your method. - - > YOUR RESPONSE HERE - - - Show where these existing methods fall short or lack the features your algorithmic method provides. - - > YOUR RESPONSE HERE - -- **Technical Context:** Describe the common approaches and challenges in the field prior to your innovation. - - > YOUR RESPONSE HERE - -### Evidence of Novelty - -- **Unique Features:** List the features, mechanisms, or aspects of your algorithmic method that are absent in prior art. - - > YOUR RESPONSE HERE - -- **New Problem Solved:** Describe how your algorithmic method provides a novel solution to an existing problem. - - > YOUR RESPONSE HERE - -- **Comparative Analysis:** Use a side-by-side comparison table to highlight the differences between your method and similar existing methods, clearly showing what’s new. - - > YOUR RESPONSE HERE - -### Evidence Inventiveness of Inventiveness - -- **Non-Obviousness:** Argue why a skilled person in the field would not have arrived at your method by simply combining existing ideas or extending known techniques. - - Demonstrate that the development involved an inventive step beyond straightforward application of existing principles. - - Unexpected Results: Highlight results or benefits that could not have been predicted based on prior art. - - > YOUR RESPONSE HERE - -- **Advantages:** Provide evidence of how your algorithm outperforms or offers significant advantages over existing methods, such as: - - Increased efficiency. - - Greater accuracy. - - Reduced computational complexity. - - Novel applications. - - > YOUR RESPONSE HERE - -### Supporting Data - -- **Experimental Results:** Include performance benchmarks, simulations, or empirical data that substantiate your claims of novelty and inventiveness. - - > YOUR RESPONSE HERE - -- **Proof of Concept:** If possible, show a working prototype or implementation that validates the method. - - > YOUR RESPONSE HERE - -### Citations and Expert Opinions - -- **Literature Gaps:** Affirm, to the best of your knowledge, the absence of similar solutions in published literature to reinforce your novelty claim. - - > YOUR RESPONSE HERE - -- **Endorsements:** Include reviews or opinions from industry experts, researchers, or peer-reviewed publications that evidences the novelty and impact of your algorithm. - - > YOUR RESPONSE HERE - -## SECTION 5 - -### EVIDENCE TO SUPPORT PATENTABILITY - -- **Development Records:** Please provide documentation of the invention process, including notes, sketches, and software versions, to establish a timeline of your innovation. - - > YOUR RESPONSE HERE - -## SECTION 6 - -### SUGGESTED APPLICATIONS - -- Please provide suggestions for any real world applications of your abstract algorithmic method that occur to you. - - > YOUR RESPONSE HERE - -## SECTION 7 - -### ANY OTHER INFORMATION - -- Please provide any other evidence or argument that you think might help support you request for eligibility for Breakthrough Rewards. - - > YOUR RESPONSE HERE \ No newline at end of file diff --git a/tig-breakthroughs/vehicle_routing/evidence.md b/tig-breakthroughs/vehicle_routing/evidence.md deleted file mode 100644 index 6577f8a..0000000 --- a/tig-breakthroughs/vehicle_routing/evidence.md +++ /dev/null @@ -1,157 +0,0 @@ -# EVIDENCE - -THIS IS A TEMPLATE FOR EVIDENCE TO BE SUBMITTED IN SUPPORT OF A REQUEST FOR ELIGIBILITY FOR BREAKTHROUGH REWARDS - -## OVERVIEW - -- TIG TOKEN HOLDERS WILL VOTE ON WHETHER YOUR ALGORITHMIC METHOD IS ELIGIBLE FOR BREAKTHROUGH REWARDS - -- TIG TOKEN HOLDERS ARE FREE TO VOTE AS THEY LIKE BUT HAVE BEEN ADVISED THAT IF THEY WANT TO MAXIMISE THE VALUE OF THE TOKENS THAT THEY HOLD, THEN THEY SHOULD BE SATISFYING THEMSELVES THAT ALGORITHIC METHODS THAT THEY VOTE AS ELIGIBLE WILL BE BOTH NOVEL AND INVENTIVE - -- THE REASON WHY NOVELTY AND INVENTIVENESS ARE IMPORTANT ATTRIBUTES IS BECAUSE THEY ARE PREREQUISITES OF PATENTATBILITY. - -- **THE PURPOSE OF THIS DOCUMENT IS TO:** - - CAPTURE A DESCRIPTION OF THE ALGORITHMIC METHOD THAT YOU WANT TO BE CONSIDERED FOR ELIGIBILTY. - - - TO IDENTIFY THE CREATOR OF THE ALGORITHMIC METHOD. - - - TO PROMPT YOU TO PROVIDE THE BEST EVIDNCE TO SUPPORT THE CASE THAT THE ALGORITHMIC METHOD IS NOVEL AND INVENTIVE. - - - TO PROMPT YOU TO PROVIDE SUGGESTIONS FOR REAL WORLD APPLICATIONS OF YOUR ALGORITHMIC METHOD WHERE YIOU CAN. - -WHEN PROVIDING EVIDENCE, YOU MAY CITE LINKS TO EXTERNAL DATA SOURCES. - -## UNIQUE ALGORITHM IDENTIFIER (UAI) - -> UAI PLACEHOLDER - ASSIGNED BY TIG PROTOCOL - -## SECTION 1 - -IT IS IMPORTANT THAT THIS SECTION IS COMPLETED IN SUFFICIENT DETAIL TO FULLY DESCRIBE THE METHOD BECAUSE THIS WILL DEFINE THE METHOD THAT IS THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. - -### DESCRIPTION OF ALGORITHMIC METHOD - -PLEASE IDENTIFY WHICH TIG CHALLENGE THE ALGORITHMIC METHOD ADDRESSES. - -> vehicle_routing - -PLEASE DESCRIBE THE ALGORITHMIC METHOD AND THE PROBLEM THAT IT SOLVES. - -> YOUR RESPONSE HERE - -## SECTION 2 - -THE COPYRIGHT IN THE IMPLEMENTATION WILL BE THE SUBJECT OF THE ASSIGNMENT THAT YOU EXECUTE. - -### IMPLEMENTATION OF ALGORITHMIC METHOD - -TO THE EXTENT THAT YOU HAVE IMPLEMENTED THE ALGORITHMIC METHOD IN CODE YOU SHOULD IDENTIFY THE CODE AND SUBMIT IT WITH THIS - -> YOUR RESPONSE HERE - -## SECTION 3 - -### ATTRIBUTION - -PLEASE PROVIDE THE IDENTITY OF THE CREATOR OF THE ALGORITHMIC METHOD (this should be a natural person or legal entity. If an artificial intelligence has been used to assist in the creation of the algorithmic method then the creator is the operator of the artificial intelligence) - -> YOUR RESPONSE HERE - -PLEASE PROVIDE EVIDENCE OF AUTHORSHIP WHERE IT IS AVAILABLE. - -> YOUR RESPONSE HERE - -## SECTION 4 - -### NOVELTY AND INVENTIVENESS - -To support your claim that an algorithmic method is novel and inventive, you should provide evidence that demonstrates both its uniqueness (novelty) and its non-obviousness (inventiveness) in relation to the existing state of the art. - -### Establish the State of the Art - -- **Prior Art Search:** Conduct a comprehensive review of existing methods, algorithms, patents, academic papers, and industry practices to identify prior art in the domain. - - Highlight documents and technologies most closely related to your method. - - > YOUR RESPONSE HERE - - - Show where these existing methods fall short or lack the features your algorithmic method provides. - - > YOUR RESPONSE HERE - -- **Technical Context:** Describe the common approaches and challenges in the field prior to your innovation. - - > YOUR RESPONSE HERE - -### Evidence of Novelty - -- **Unique Features:** List the features, mechanisms, or aspects of your algorithmic method that are absent in prior art. - - > YOUR RESPONSE HERE - -- **New Problem Solved:** Describe how your algorithmic method provides a novel solution to an existing problem. - - > YOUR RESPONSE HERE - -- **Comparative Analysis:** Use a side-by-side comparison table to highlight the differences between your method and similar existing methods, clearly showing what’s new. - - > YOUR RESPONSE HERE - -### Evidence Inventiveness of Inventiveness - -- **Non-Obviousness:** Argue why a skilled person in the field would not have arrived at your method by simply combining existing ideas or extending known techniques. - - Demonstrate that the development involved an inventive step beyond straightforward application of existing principles. - - Unexpected Results: Highlight results or benefits that could not have been predicted based on prior art. - - > YOUR RESPONSE HERE - -- **Advantages:** Provide evidence of how your algorithm outperforms or offers significant advantages over existing methods, such as: - - Increased efficiency. - - Greater accuracy. - - Reduced computational complexity. - - Novel applications. - - > YOUR RESPONSE HERE - -### Supporting Data - -- **Experimental Results:** Include performance benchmarks, simulations, or empirical data that substantiate your claims of novelty and inventiveness. - - > YOUR RESPONSE HERE - -- **Proof of Concept:** If possible, show a working prototype or implementation that validates the method. - - > YOUR RESPONSE HERE - -### Citations and Expert Opinions - -- **Literature Gaps:** Affirm, to the best of your knowledge, the absence of similar solutions in published literature to reinforce your novelty claim. - - > YOUR RESPONSE HERE - -- **Endorsements:** Include reviews or opinions from industry experts, researchers, or peer-reviewed publications that evidences the novelty and impact of your algorithm. - - > YOUR RESPONSE HERE - -## SECTION 5 - -### EVIDENCE TO SUPPORT PATENTABILITY - -- **Development Records:** Please provide documentation of the invention process, including notes, sketches, and software versions, to establish a timeline of your innovation. - - > YOUR RESPONSE HERE - -## SECTION 6 - -### SUGGESTED APPLICATIONS - -- Please provide suggestions for any real world applications of your abstract algorithmic method that occur to you. - - > YOUR RESPONSE HERE - -## SECTION 7 - -### ANY OTHER INFORMATION - -- Please provide any other evidence or argument that you think might help support you request for eligibility for Breakthrough Rewards. - - > YOUR RESPONSE HERE \ No newline at end of file From ec5e09d1c0b1fa6b8c920b0b78e5d7e3f9da4155 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Sat, 18 Jan 2025 14:19:26 +0000 Subject: [PATCH 46/49] Add get-batch-data endpoint to master. --- .../master/master/client_manager.py | 39 +++++++++++++++++++ tig-benchmarker/nginx/nginx.conf | 2 +- 2 files changed, 40 insertions(+), 1 deletion(-) diff --git a/tig-benchmarker/master/master/client_manager.py b/tig-benchmarker/master/master/client_manager.py index d6faf07..8483061 100644 --- a/tig-benchmarker/master/master/client_manager.py +++ b/tig-benchmarker/master/master/client_manager.py @@ -199,6 +199,45 @@ class ClientManager: headers = {"Accept-Encoding": "gzip"} ) + @self.app.get("/get-batch-data/{batch_id}") + async def get_batch_data(batch_id: str): + benchmark_id, batch_idx = batch_id.split("_") + result = get_db_conn().fetch_one( + f""" + SELECT + JSONB_BUILD_OBJECT( + 'id', A.benchmark_id || '_' || A.batch_idx, + 'benchmark_id', A.benchmark_id, + 'start_nonce', A.batch_idx * B.batch_size, + 'num_nonces', LEAST(B.batch_size, B.num_nonces - A.batch_idx * B.batch_size), + 'settings', B.settings, + 'sampled_nonces', NULL, + 'runtime_config', B.runtime_config, + 'download_url', B.download_url, + 'rand_hash', B.rand_hash, + 'batch_size', B.batch_size, + 'batch_idx', A.batch_idx + ) AS batch, + C.merkle_root, + C.solution_nonces, + C.merkle_proofs + FROM root_batch A + INNER JOIN job B + ON A.benchmark_id = '{benchmark_id}' + AND A.batch_idx = {batch_idx} + AND A.benchmark_id = B.benchmark_id + INNER JOIN batch_data C + ON A.benchmark_id = C.benchmark_id + AND A.batch_idx = C.batch_idx + """ + ) + + return JSONResponse( + content=dict(result), + status_code=200, + headers = {"Accept-Encoding": "gzip"} + ) + def start(self): def run(): self.app = FastAPI() diff --git a/tig-benchmarker/nginx/nginx.conf b/tig-benchmarker/nginx/nginx.conf index 26ceb36..e42d919 100644 --- a/tig-benchmarker/nginx/nginx.conf +++ b/tig-benchmarker/nginx/nginx.conf @@ -18,7 +18,7 @@ http { server_name localhost; # Master Service - specific endpoints - location ~ ^/(get-config|stop|update-config|get-jobs|get-latest-data) { + location ~ ^/(get-config|stop|update-config|get-batch-data|get-jobs|get-latest-data) { proxy_pass http://master:3336; proxy_http_version 1.1; proxy_set_header Upgrade $http_upgrade; From 1bb3a70e68f0682180958dba9ca3d87ac060841d Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Sat, 18 Jan 2025 16:51:58 +0000 Subject: [PATCH 47/49] Add example batch verification script. --- .../master/master/client_manager.py | 5 +- tig-benchmarker/verify_batch.py | 134 ++++++++++++++++++ 2 files changed, 138 insertions(+), 1 deletion(-) create mode 100644 tig-benchmarker/verify_batch.py diff --git a/tig-benchmarker/master/master/client_manager.py b/tig-benchmarker/master/master/client_manager.py index 8483061..08bb10c 100644 --- a/tig-benchmarker/master/master/client_manager.py +++ b/tig-benchmarker/master/master/client_manager.py @@ -211,7 +211,7 @@ class ClientManager: 'start_nonce', A.batch_idx * B.batch_size, 'num_nonces', LEAST(B.batch_size, B.num_nonces - A.batch_idx * B.batch_size), 'settings', B.settings, - 'sampled_nonces', NULL, + 'sampled_nonces', D.sampled_nonces, 'runtime_config', B.runtime_config, 'download_url', B.download_url, 'rand_hash', B.rand_hash, @@ -229,6 +229,9 @@ class ClientManager: INNER JOIN batch_data C ON A.benchmark_id = C.benchmark_id AND A.batch_idx = C.batch_idx + LEFT JOIN proofs_batch D + ON A.benchmark_id = D.benchmark_id + AND A.batch_idx = D.batch_idx """ ) diff --git a/tig-benchmarker/verify_batch.py b/tig-benchmarker/verify_batch.py new file mode 100644 index 0000000..64a4f78 --- /dev/null +++ b/tig-benchmarker/verify_batch.py @@ -0,0 +1,134 @@ +from common.structs import * +import requests +import json +import random +import os +import subprocess + +print("THIS IS AN EXAMPLE SCRIPT TO VERIFY A BATCH") +TIG_WORKER_PATH = input("Enter path of tig-worker executable: ") +NUM_WORKERS = int(input("Enter number of workers: ")) +if not os.path.exists(TIG_WORKER_PATH): + raise FileNotFound[ERROR](f"tig-worker not found at path: {TIG_WORKER_PATH}") +MASTER_IP = input("Enter Master IP: ") +MASTER_PORT = input("Enter Master Port: ") + +jobs = requests.get(f"http://{MASTER_IP}:{MASTER_PORT}/get-jobs").json() +for i, j in enumerate(jobs): + print(f"{i + 1}) benchmark_id: {j['benchmark_id']}, challenge: {j['challenge']}, algorithm: {j['algorithm']}, status: {j['status']}") +job_idx = int(input("Enter the index of the batch you want to verify: ")) - 1 + +for i, b in enumerate(jobs[job_idx]["batches"]): + print(f"{i + 1}) slave: {b['slave']}, num_solutions: {b['num_solutions']}, status: {b['status']}") +batch_idx = int(input("Enter the index of the batch you want to verify: ")) - 1 + +benchmark_id = jobs[job_idx]["benchmark_id"] +url = f"http://{MASTER_IP}:{MASTER_PORT}/get-batch-data/{benchmark_id}_{batch_idx}" +print(f"Fetching batch data from {url}") +data = requests.get(url).json() + +batch = data['batch'] +merkle_root = data['merkle_root'] +solution_nonces = data['solution_nonces'] +merkle_proofs = data['merkle_proofs'] +if merkle_proofs is not None: + merkle_proofs = {x['leaf']['nonce']: x for x in merkle_proofs} + +if ( + merkle_proofs is None or + len(solution_nonces := set(merkle_proofs) & set(solution_nonces)) == 0 +): + print("No solution data to verify for this batch") +else: + for nonce in solution_nonces: + print(f"Verifying solution for nonce: {nonce}") + cmd = [ + TIG_WORKER_PATH, "verify_solution", + json.dumps(batch['settings'], separators=(',', ':')), + batch["rand_hash"], + str(nonce), + json.dumps(merkle_proofs[nonce]['leaf']['solution'], separators=(',', ':')), + ] + print(f"Running cmd: {' '.join(cmd)}") + ret = subprocess.run( + cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + if ret.returncode == 0: + print(f"[SUCCESS]: {ret.stdout.decode()}") + else: + print(f"[ERROR]: {ret.stderr.decode()}") + +if merkle_root is not None: + download_url = batch["download_url"] + print(f"Downloading WASM from {download_url}") + resp = requests.get(download_url) + if resp.status_code != 200: + raise Exception(f"status {resp.status_code} when downloading WASM: {resp.text}") + wasm_path = f'{batch["settings"]["algorithm_id"]}.wasm' + with open(wasm_path, 'wb') as f: + f.write(resp.content) + print(f"WASM Path: {wasm_path}") + print("") + +if merkle_proofs is None: + print("No merkle proofs to verify for this batch") +else: + for nonce in merkle_proofs: + print(f"Verifying output data for nonce: {nonce}") + cmd = [ + TIG_WORKER_PATH, "compute_solution", + json.dumps(batch['settings'], separators=(',', ':')), + batch["rand_hash"], + str(nonce), + wasm_path, + "--mem", str(batch["runtime_config"]["max_memory"]), + "--fuel", str(batch["runtime_config"]["max_fuel"]), + ] + print(f"Running cmd: {' '.join(cmd)}") + ret = subprocess.run( + cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + out = json.loads(ret.stdout.decode()) + expected = json.dumps(merkle_proofs[nonce]['leaf'], separators=(',', ':'), sort_keys=True) + actual = json.dumps(out, separators=(',', ':'), sort_keys=True) + if expected == actual: + print(f"[SUCCESS]: output data match") + else: + print(f"[ERROR]: output data mismatch") + print(f"Batch data: {expected}") + print(f"Recomputed: {actual}") + print(f"") + +if merkle_root is None: + print("No merkle root to verify for this batch") +else: + print("Verifying merkle root") + cmd = [ + TIG_WORKER_PATH, "compute_batch", + json.dumps(batch["settings"]), + batch["rand_hash"], + str(batch["start_nonce"]), + str(batch["num_nonces"]), + str(batch["batch_size"]), + wasm_path, + "--mem", str(batch["runtime_config"]["max_memory"]), + "--fuel", str(batch["runtime_config"]["max_fuel"]), + "--workers", str(NUM_WORKERS), + ] + print(f"Running cmd: {' '.join(cmd)}") + ret = subprocess.run( + cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + if ret.returncode == 0: + out = json.loads(ret.stdout.decode()) + if out["merkle_root"] == merkle_root: + print(f"[SUCCESS]: merkle root match") + else: + print(f"[ERROR]: merkle root mismatch") + print(f"Batch data: {expected}") + print(f"Recomputed: {actual}") + else: + print(f"[ERROR]: {ret.stderr.decode()}") + +print("") +print("FINISHED") \ No newline at end of file From cb4d8779883716dc21b9118819f47ec4ffd4d097 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 23 Jan 2025 20:14:16 +0800 Subject: [PATCH 48/49] Address deposit edge case. --- tig-protocol/src/contracts/players.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tig-protocol/src/contracts/players.rs b/tig-protocol/src/contracts/players.rs index 7991a8c..407bd28 100644 --- a/tig-protocol/src/contracts/players.rs +++ b/tig-protocol/src/contracts/players.rs @@ -273,7 +273,7 @@ pub(crate) async fn update(cache: &mut AddBlockCache) { for deposit in active_deposit_details.values() { let total_time = PreciseNumber::from(deposit.end_timestamp - deposit.start_timestamp); for i in 0..lock_period_cap { - if round_timestamps[i + 1] <= deposit.start_timestamp { + if i + 1 < lock_period_cap && round_timestamps[i + 1] <= deposit.start_timestamp { continue; } if round_timestamps[i] >= deposit.end_timestamp { From 424d096a858fd5ee55ae9805385921b3026d0c70 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Sun, 26 Jan 2025 10:20:21 +0800 Subject: [PATCH 49/49] Only update adoption for active breakthroughs. --- tig-protocol/src/contracts/algorithms.rs | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/tig-protocol/src/contracts/algorithms.rs b/tig-protocol/src/contracts/algorithms.rs index 3cb824f..4a48514 100644 --- a/tig-protocol/src/contracts/algorithms.rs +++ b/tig-protocol/src/contracts/algorithms.rs @@ -216,10 +216,9 @@ pub(crate) async fn update(cache: &mut AddBlockCache) { if let Some(breakthrough_id) = &active_algorithms_details[algorithm_id].breakthrough_id { - active_breakthroughs_block_data - .get_mut(breakthrough_id) - .unwrap() - .adoption += adoption; + if let Some(block_data) = active_breakthroughs_block_data.get_mut(breakthrough_id) { + block_data.adoption += adoption; + } } } }

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z2tE#;1kZwh0&jr+4R*S~A)q|Qxt<31fhHJnDn7gI?8sU<$OGZB!GfSH(P2Y)j|;FNqF zhrdguYD5e-T99zJGoUdTwQn|~P&!wHUFWtWhL~tTM1NaVl*rjeRfxX>`EqvP8k`t2 zuQu(9onv#o9c#Lfnr#kj-Ux7Z;GLb#;*b-g)=tRrh7G-qXV~%t=O*sR*~}I@n<=a# zZZOzT^;y8MsjT4OWUK_SX$#t^Y+|^yS8qCB$^)MnG>FgktDaI!8$^;6;6o2f6+ zTO00AyA2g1#y!V5XJ@+%QLfyeusM${VwGdvF~Mo`WD90y zAZW#(1!9=`VP{Fosf1!jC~{aol`8U#R!jRV-y%6)hBzuy0^&e!^Xci<)GGyNNb2l`rxwf>t1K0ATJyQ?P_uYW)HDCF;M0u6K8KgNZiKo!baa<8^8Juqv_+kmzzxS{_io~th@b=B(D)4 z8$No+@Yv|#LG~kBOLL>g)1&FJWNNwFUGJP28Q~nN<=#RXRn6x4Aa literal 0 HcmV?d00001 From cf3a14e974ef269377a90099064fc0ca14b40ed9 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:08:15 +0000 Subject: [PATCH 39/49] Compiled vehicle_routing/clarke_wright_super --- .../benchmarker_outbound.rs | 130 ++++++++++++++++++ .../clarke_wright_super/commercial.rs | 130 ++++++++++++++++++ .../clarke_wright_super/inbound.rs | 130 ++++++++++++++++++ .../clarke_wright_super/innovator_outbound.rs | 130 ++++++++++++++++++ .../clarke_wright_super/mod.rs | 4 + .../clarke_wright_super/open_data.rs | 130 ++++++++++++++++++ tig-algorithms/src/vehicle_routing/mod.rs | 3 +- .../src/vehicle_routing/template.rs | 26 +--- .../vehicle_routing/clarke_wright_super.wasm | Bin 0 -> 158220 bytes 9 files changed, 658 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright_super/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright_super/commercial.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright_super/inbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright_super/innovator_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright_super/mod.rs create mode 100644 tig-algorithms/src/vehicle_routing/clarke_wright_super/open_data.rs create mode 100644 tig-algorithms/wasm/vehicle_routing/clarke_wright_super.wasm diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright_super/benchmarker_outbound.rs b/tig-algorithms/src/vehicle_routing/clarke_wright_super/benchmarker_outbound.rs new file mode 100644 index 0000000..cb49161 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright_super/benchmarker_outbound.rs @@ -0,0 +1,130 @@ +/*! +Copyright 2024 OvErLoDe + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n * (n - 1)) / 2); +for i in 1..n { + let d_i0 = d[i][0]; // Cache this value to avoid repeated lookups + for j in (i + 1)..n { + let score = d_i0 + d[0][j] - d[i][j]; + scores.push((score, i, j)); + } +} + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + // Directly get the routes + let (left_route, right_route) = (routes[i].as_ref().unwrap(), routes[j].as_ref().unwrap()); + + // Cache indices and demands + let (left_startnode, left_endnode) = (left_route[0], *left_route.last().unwrap()); + let (right_startnode, right_endnode) = (right_route[0], *right_route.last().unwrap()); + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + // Check constraints + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + // Merge routes + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // Reverse if needed + if left_startnode == i { + left_route.reverse(); + } + if right_endnode == j { + right_route.reverse(); + } + + // Create new route + let mut new_route = left_route; + new_route.extend(right_route); + + // Update routes and demands + let (start, end) = (*new_route.first().unwrap(), *new_route.last().unwrap()); + routes[start] = Some(new_route.clone()); + routes[end] = Some(new_route); + route_demands[start] = merged_demand; + route_demands[end] = merged_demand; + } + + let mut final_routes = Vec::new(); + + for (i, opt_route) in routes.into_iter().enumerate() { + if let Some(mut route) = opt_route { + if route[0] == i { + let mut full_route = Vec::with_capacity(route.len() + 2); + full_route.push(0); + full_route.append(&mut route); + full_route.push(0); + final_routes.push(full_route); + } + } + } + + Ok(Some(Solution { routes: final_routes })) + +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright_super/commercial.rs b/tig-algorithms/src/vehicle_routing/clarke_wright_super/commercial.rs new file mode 100644 index 0000000..257991d --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright_super/commercial.rs @@ -0,0 +1,130 @@ +/*! +Copyright 2024 OvErLoDe + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n * (n - 1)) / 2); +for i in 1..n { + let d_i0 = d[i][0]; // Cache this value to avoid repeated lookups + for j in (i + 1)..n { + let score = d_i0 + d[0][j] - d[i][j]; + scores.push((score, i, j)); + } +} + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + // Directly get the routes + let (left_route, right_route) = (routes[i].as_ref().unwrap(), routes[j].as_ref().unwrap()); + + // Cache indices and demands + let (left_startnode, left_endnode) = (left_route[0], *left_route.last().unwrap()); + let (right_startnode, right_endnode) = (right_route[0], *right_route.last().unwrap()); + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + // Check constraints + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + // Merge routes + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // Reverse if needed + if left_startnode == i { + left_route.reverse(); + } + if right_endnode == j { + right_route.reverse(); + } + + // Create new route + let mut new_route = left_route; + new_route.extend(right_route); + + // Update routes and demands + let (start, end) = (*new_route.first().unwrap(), *new_route.last().unwrap()); + routes[start] = Some(new_route.clone()); + routes[end] = Some(new_route); + route_demands[start] = merged_demand; + route_demands[end] = merged_demand; + } + + let mut final_routes = Vec::new(); + + for (i, opt_route) in routes.into_iter().enumerate() { + if let Some(mut route) = opt_route { + if route[0] == i { + let mut full_route = Vec::with_capacity(route.len() + 2); + full_route.push(0); + full_route.append(&mut route); + full_route.push(0); + final_routes.push(full_route); + } + } + } + + Ok(Some(Solution { routes: final_routes })) + +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright_super/inbound.rs b/tig-algorithms/src/vehicle_routing/clarke_wright_super/inbound.rs new file mode 100644 index 0000000..0523dd0 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright_super/inbound.rs @@ -0,0 +1,130 @@ +/*! +Copyright 2024 OvErLoDe + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n * (n - 1)) / 2); +for i in 1..n { + let d_i0 = d[i][0]; // Cache this value to avoid repeated lookups + for j in (i + 1)..n { + let score = d_i0 + d[0][j] - d[i][j]; + scores.push((score, i, j)); + } +} + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + // Directly get the routes + let (left_route, right_route) = (routes[i].as_ref().unwrap(), routes[j].as_ref().unwrap()); + + // Cache indices and demands + let (left_startnode, left_endnode) = (left_route[0], *left_route.last().unwrap()); + let (right_startnode, right_endnode) = (right_route[0], *right_route.last().unwrap()); + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + // Check constraints + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + // Merge routes + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // Reverse if needed + if left_startnode == i { + left_route.reverse(); + } + if right_endnode == j { + right_route.reverse(); + } + + // Create new route + let mut new_route = left_route; + new_route.extend(right_route); + + // Update routes and demands + let (start, end) = (*new_route.first().unwrap(), *new_route.last().unwrap()); + routes[start] = Some(new_route.clone()); + routes[end] = Some(new_route); + route_demands[start] = merged_demand; + route_demands[end] = merged_demand; + } + + let mut final_routes = Vec::new(); + + for (i, opt_route) in routes.into_iter().enumerate() { + if let Some(mut route) = opt_route { + if route[0] == i { + let mut full_route = Vec::with_capacity(route.len() + 2); + full_route.push(0); + full_route.append(&mut route); + full_route.push(0); + final_routes.push(full_route); + } + } + } + + Ok(Some(Solution { routes: final_routes })) + +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright_super/innovator_outbound.rs b/tig-algorithms/src/vehicle_routing/clarke_wright_super/innovator_outbound.rs new file mode 100644 index 0000000..c87bd18 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright_super/innovator_outbound.rs @@ -0,0 +1,130 @@ +/*! +Copyright 2024 OvErLoDe + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n * (n - 1)) / 2); +for i in 1..n { + let d_i0 = d[i][0]; // Cache this value to avoid repeated lookups + for j in (i + 1)..n { + let score = d_i0 + d[0][j] - d[i][j]; + scores.push((score, i, j)); + } +} + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + // Directly get the routes + let (left_route, right_route) = (routes[i].as_ref().unwrap(), routes[j].as_ref().unwrap()); + + // Cache indices and demands + let (left_startnode, left_endnode) = (left_route[0], *left_route.last().unwrap()); + let (right_startnode, right_endnode) = (right_route[0], *right_route.last().unwrap()); + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + // Check constraints + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + // Merge routes + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // Reverse if needed + if left_startnode == i { + left_route.reverse(); + } + if right_endnode == j { + right_route.reverse(); + } + + // Create new route + let mut new_route = left_route; + new_route.extend(right_route); + + // Update routes and demands + let (start, end) = (*new_route.first().unwrap(), *new_route.last().unwrap()); + routes[start] = Some(new_route.clone()); + routes[end] = Some(new_route); + route_demands[start] = merged_demand; + route_demands[end] = merged_demand; + } + + let mut final_routes = Vec::new(); + + for (i, opt_route) in routes.into_iter().enumerate() { + if let Some(mut route) = opt_route { + if route[0] == i { + let mut full_route = Vec::with_capacity(route.len() + 2); + full_route.push(0); + full_route.append(&mut route); + full_route.push(0); + final_routes.push(full_route); + } + } + } + + Ok(Some(Solution { routes: final_routes })) + +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright_super/mod.rs b/tig-algorithms/src/vehicle_routing/clarke_wright_super/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright_super/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/clarke_wright_super/open_data.rs b/tig-algorithms/src/vehicle_routing/clarke_wright_super/open_data.rs new file mode 100644 index 0000000..5d71dbb --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/clarke_wright_super/open_data.rs @@ -0,0 +1,130 @@ +/*! +Copyright 2024 OvErLoDe + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n * (n - 1)) / 2); +for i in 1..n { + let d_i0 = d[i][0]; // Cache this value to avoid repeated lookups + for j in (i + 1)..n { + let score = d_i0 + d[0][j] - d[i][j]; + scores.push((score, i, j)); + } +} + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); // Sort in descending order by score + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + // Directly get the routes + let (left_route, right_route) = (routes[i].as_ref().unwrap(), routes[j].as_ref().unwrap()); + + // Cache indices and demands + let (left_startnode, left_endnode) = (left_route[0], *left_route.last().unwrap()); + let (right_startnode, right_endnode) = (right_route[0], *right_route.last().unwrap()); + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + // Check constraints + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + // Merge routes + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // Reverse if needed + if left_startnode == i { + left_route.reverse(); + } + if right_endnode == j { + right_route.reverse(); + } + + // Create new route + let mut new_route = left_route; + new_route.extend(right_route); + + // Update routes and demands + let (start, end) = (*new_route.first().unwrap(), *new_route.last().unwrap()); + routes[start] = Some(new_route.clone()); + routes[end] = Some(new_route); + route_demands[start] = merged_demand; + route_demands[end] = merged_demand; + } + + let mut final_routes = Vec::new(); + + for (i, opt_route) in routes.into_iter().enumerate() { + if let Some(mut route) = opt_route { + if route[0] == i { + let mut full_route = Vec::with_capacity(route.len() + 2); + full_route.push(0); + full_route.append(&mut route); + full_route.push(0); + final_routes.push(full_route); + } + } + } + + Ok(Some(Solution { routes: final_routes })) + +} +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/mod.rs b/tig-algorithms/src/vehicle_routing/mod.rs index 930b4fb..6103311 100644 --- a/tig-algorithms/src/vehicle_routing/mod.rs +++ b/tig-algorithms/src/vehicle_routing/mod.rs @@ -68,7 +68,8 @@ // c002_a035 -// c002_a036 +pub mod clarke_wright_super; +pub use clarke_wright_super as c002_a036; // c002_a037 diff --git a/tig-algorithms/src/vehicle_routing/template.rs b/tig-algorithms/src/vehicle_routing/template.rs index 02a7cab..b5c872b 100644 --- a/tig-algorithms/src/vehicle_routing/template.rs +++ b/tig-algorithms/src/vehicle_routing/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vehicle_routing::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vehicle_routing/clarke_wright_super.wasm b/tig-algorithms/wasm/vehicle_routing/clarke_wright_super.wasm new file mode 100644 index 0000000000000000000000000000000000000000..929b53de3bcdb6430840dd5a65dc50604d8141b4 GIT binary patch literal 158220 zcmeFa3z%LFmZ<1j>Gi25~ zA<_Q+Z=Lg9`eIoGmVKTX>v4bQ)TvYT)?07A_o@?p|D8V^M^O~tA5Yzu9yxL(K5|=P zf8!&Ot|BXqZp(PaO{@xaUF)tk^2-ye#c#yBhxP7ZYpZ;L2Q7Q!$l=3>quUzcS}kUW zL&cOQ>WkYb{f+q7<21{8s71!%=s(+0n%Q{W|PwLHD zmZm8$X0?dFaVu-^a+<}-L__zqmh~vnwTk(Z#&NAy%i7IQ7^e}%G?P$;XW_pFt)_{Z z&eANMg~?AG+f}^vSq&B#wOCc~kSUiN=q|KM{ZL+wPCgyY#=` z{)2D-$8Vb)Z?8Y^H8iTvZqLXply$?pEYBmJvE z*=`rjCOw9_wDk5ni^SI)|L>Xh@xpj5PHJfubzD4~M1}t7s#!z}KD{y9?P3>s=EDke zRnz_auB54w%tiD5Yk%?4uhw@(QFlBvl2c_z6kVg2hnvf)=4!*ug+_qseeXXN%|;Oo 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08f39b762cea1117f4107ad4945393026aef00d0 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:08:48 +0000 Subject: [PATCH 40/49] Compiled vehicle_routing/cw_heuristic --- .../cw_heuristic/benchmarker_outbound.rs | 133 ++++++++++++++++++ .../cw_heuristic/commercial.rs | 133 ++++++++++++++++++ .../vehicle_routing/cw_heuristic/inbound.rs | 133 ++++++++++++++++++ .../cw_heuristic/innovator_outbound.rs | 133 ++++++++++++++++++ .../src/vehicle_routing/cw_heuristic/mod.rs | 4 + .../vehicle_routing/cw_heuristic/open_data.rs | 133 ++++++++++++++++++ tig-algorithms/src/vehicle_routing/mod.rs | 3 +- .../src/vehicle_routing/template.rs | 26 +--- .../wasm/vehicle_routing/cw_heuristic.wasm | Bin 0 -> 158227 bytes 9 files changed, 673 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vehicle_routing/cw_heuristic/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/cw_heuristic/commercial.rs create mode 100644 tig-algorithms/src/vehicle_routing/cw_heuristic/inbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/cw_heuristic/innovator_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/cw_heuristic/mod.rs create mode 100644 tig-algorithms/src/vehicle_routing/cw_heuristic/open_data.rs create mode 100644 tig-algorithms/wasm/vehicle_routing/cw_heuristic.wasm diff --git a/tig-algorithms/src/vehicle_routing/cw_heuristic/benchmarker_outbound.rs b/tig-algorithms/src/vehicle_routing/cw_heuristic/benchmarker_outbound.rs new file mode 100644 index 0000000..5ddaecf --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/cw_heuristic/benchmarker_outbound.rs @@ -0,0 +1,133 @@ +/*! +Copyright 2024 Just + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n-1)*(n-2)/2); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + let routes = routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(); + + Ok(Some(Solution { + routes + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/cw_heuristic/commercial.rs b/tig-algorithms/src/vehicle_routing/cw_heuristic/commercial.rs new file mode 100644 index 0000000..334851d --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/cw_heuristic/commercial.rs @@ -0,0 +1,133 @@ +/*! +Copyright 2024 Just + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n-1)*(n-2)/2); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + let routes = routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(); + + Ok(Some(Solution { + routes + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/cw_heuristic/inbound.rs b/tig-algorithms/src/vehicle_routing/cw_heuristic/inbound.rs new file mode 100644 index 0000000..7dc1630 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/cw_heuristic/inbound.rs @@ -0,0 +1,133 @@ +/*! +Copyright 2024 Just + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n-1)*(n-2)/2); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + let routes = routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(); + + Ok(Some(Solution { + routes + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/cw_heuristic/innovator_outbound.rs b/tig-algorithms/src/vehicle_routing/cw_heuristic/innovator_outbound.rs new file mode 100644 index 0000000..3087299 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/cw_heuristic/innovator_outbound.rs @@ -0,0 +1,133 @@ +/*! +Copyright 2024 Just + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n-1)*(n-2)/2); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + let routes = routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(); + + Ok(Some(Solution { + routes + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/cw_heuristic/mod.rs b/tig-algorithms/src/vehicle_routing/cw_heuristic/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/cw_heuristic/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/cw_heuristic/open_data.rs b/tig-algorithms/src/vehicle_routing/cw_heuristic/open_data.rs new file mode 100644 index 0000000..d2545b8 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/cw_heuristic/open_data.rs @@ -0,0 +1,133 @@ +/*! +Copyright 2024 Just + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use tig_challenges::vehicle_routing::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let d = &challenge.distance_matrix; + let c = challenge.max_capacity; + let n = challenge.difficulty.num_nodes; + + let max_dist: f32 = challenge.distance_matrix[0].iter().sum::() as f32; + let p = challenge.max_total_distance as f32 / max_dist; + if p < 0.57 { + return Ok(None) + } + + // Clarke-Wright heuristic for node pairs based on their distances to depot + // vs distance between each other + let mut scores: Vec<(i32, usize, usize)> = Vec::with_capacity((n-1)*(n-2)/2); + for i in 1..n { + for j in (i + 1)..n { + scores.push((d[i][0] + d[0][j] - d[i][j], i, j)); + } + } + + scores.sort_unstable_by(|a, b| b.0.cmp(&a.0)); + + // Create a route for every node + let mut routes: Vec>> = (0..n).map(|i| Some(vec![i])).collect(); + routes[0] = None; + let mut route_demands: Vec = challenge.demands.clone(); + + // Iterate through node pairs, starting from greatest score + for (s, i, j) in scores { + // Stop if score is negative + if s < 0 { + break; + } + + // Skip if joining the nodes is not possible + if routes[i].is_none() || routes[j].is_none() { + continue; + } + + let left_route = routes[i].as_ref().unwrap(); + let right_route = routes[j].as_ref().unwrap(); + let mut left_startnode = left_route[0]; + let right_startnode = right_route[0]; + let left_endnode = left_route[left_route.len() - 1]; + let mut right_endnode = right_route[right_route.len() - 1]; + let merged_demand = route_demands[left_startnode] + route_demands[right_startnode]; + + if left_startnode == right_startnode || merged_demand > c { + continue; + } + + let mut left_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + routes[left_startnode] = None; + routes[right_startnode] = None; + routes[left_endnode] = None; + routes[right_endnode] = None; + + // reverse it + if left_startnode == i { + left_route.reverse(); + left_startnode = left_endnode; + } + if right_endnode == j { + right_route.reverse(); + right_endnode = right_startnode; + } + + let mut new_route = left_route; + new_route.extend(right_route); + + // Only the start and end nodes of routes are kept + routes[left_startnode] = Some(new_route.clone()); + routes[right_endnode] = Some(new_route); + route_demands[left_startnode] = merged_demand; + route_demands[right_endnode] = merged_demand; + } + + let routes = routes + .into_iter() + .enumerate() + .filter(|(i, x)| x.as_ref().is_some_and(|x| x[0] == *i)) + .map(|(_, mut x)| { + let mut route = vec![0]; + route.append(x.as_mut().unwrap()); + route.push(0); + route + }) + .collect(); + + Ok(Some(Solution { + routes + })) +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/vehicle_routing/mod.rs b/tig-algorithms/src/vehicle_routing/mod.rs index 930b4fb..bbe2d90 100644 --- a/tig-algorithms/src/vehicle_routing/mod.rs +++ b/tig-algorithms/src/vehicle_routing/mod.rs @@ -66,7 +66,8 @@ // c002_a034 -// c002_a035 +pub mod cw_heuristic; +pub use cw_heuristic as c002_a035; // c002_a036 diff --git a/tig-algorithms/src/vehicle_routing/template.rs b/tig-algorithms/src/vehicle_routing/template.rs index 02a7cab..b5c872b 100644 --- a/tig-algorithms/src/vehicle_routing/template.rs +++ b/tig-algorithms/src/vehicle_routing/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vehicle_routing::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vehicle_routing/cw_heuristic.wasm b/tig-algorithms/wasm/vehicle_routing/cw_heuristic.wasm new file mode 100644 index 0000000000000000000000000000000000000000..0294ce07379416211247dc59b94230b0e961639e GIT binary patch literal 158227 zcmeFa3z%NVS?76f-~H?EucfvvtL3Q9QQK}=QY=G|9VaBJW_^(r$4=t)gzW6Z%!Va& z8xzS|5hg9E9m_^Z40yoI7%)T$Nn{|JaY(|1;1DU8nE)nuH(}!eFPOy3dV$G$X2Ps@ zFlc}Ox6b)4eX*>NEc-k&*5m%psZ*!wt+(EK?^P%Io;!aaj-n{OKc2cRJ#yqoeB`#o z{>DclT}4(J-Ino;n^+aiof4_fxdk;8`%N4GV?wOY&$ zhl(jr)EBo=`s?wp#c7sx(|Tn;<&|SJ5JN2`Y)~NDz4Y#RR0=DmUWu^pVXVR zEKO5h%xV#T<5t$-p$?lKk$S5e>jP< z@4w^w4@Cbe?tJeZ2loH)Ti@}6-+#w}=u>g)NF4dN^Va5N6OA8>e?0!KAG$w2@6!K% z`}e)`AOFzgczgYMufOTG?^*c4*MIN-=)Y^?N8{!{{E7IV#vh668~t~^J>UKPi__~i 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zCW*?J>5LiexJsXSn`4Th*pIwyXv88Trb{;Q@h0=43+Yc}^o5aWjG9&{(ruYIjqGfC z&(tSc)_eLijP)@7OiGCw(+*?GA+Ir4=0$U~lwSc^rnHtuVZAC|g=(!{`|L&$uP=@7 zVU!Li*8P3>phw@-3Dx~~6Z&)&XI1`LxuF6Z z236f%c3D}tqONkj@@&;D%Jho)6_1turedOUXO&WxEPu01t#X&|#ddz$_6VIRo+qQS3WHJFJ)In zxAH3XA^LLVw#sG7rn2U;FDZ{z-Bi|E-d6Tb6}Ef75*a)R#zy6|&sGa-t8$=h5~M}4 zn81s~@`-8e*jdE1x~wvSk7AUQbU~sfYkNxNRXJQsobGgZ@g1?vj_;M-_!6+O$%fCK z-L>^iwJv8(Rf17$aC#k1hs$Q~D-?$EE9&a7Z&qKi#{pAST{^u(`>RFdpT<{MHkT*v zOnN-^&LqBTjyHQ!?gpIKC%yPGx~DPjb~SnG<7$1PMrl3;HfX#Z-JVk8_yAYUS1daP jAD>W?*7l~eJ=mCZ*_57QdZ1PlSj#5nvGZ3grZN8q!OyXi literal 0 HcmV?d00001 From 72e4cb5ba1044ce5e45160d87f072997847c294e Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:21:56 +0000 Subject: [PATCH 41/49] Compiled satisfiability/sat_adaptive --- tig-algorithms/src/satisfiability/mod.rs | 3 +- .../sat_adaptive/benchmarker_outbound.rs | 315 ++++++++++++++++++ .../satisfiability/sat_adaptive/commercial.rs | 315 ++++++++++++++++++ .../satisfiability/sat_adaptive/inbound.rs | 315 ++++++++++++++++++ .../sat_adaptive/innovator_outbound.rs | 315 ++++++++++++++++++ .../src/satisfiability/sat_adaptive/mod.rs | 4 + .../satisfiability/sat_adaptive/open_data.rs | 315 ++++++++++++++++++ tig-algorithms/src/satisfiability/template.rs | 26 +- .../wasm/satisfiability/sat_adaptive.wasm | Bin 0 -> 157768 bytes 9 files changed, 1583 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/satisfiability/sat_adaptive/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_adaptive/commercial.rs create mode 100644 tig-algorithms/src/satisfiability/sat_adaptive/inbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_adaptive/innovator_outbound.rs create mode 100644 tig-algorithms/src/satisfiability/sat_adaptive/mod.rs create mode 100644 tig-algorithms/src/satisfiability/sat_adaptive/open_data.rs create mode 100644 tig-algorithms/wasm/satisfiability/sat_adaptive.wasm diff --git a/tig-algorithms/src/satisfiability/mod.rs b/tig-algorithms/src/satisfiability/mod.rs index 22d6805..f295f6c 100644 --- a/tig-algorithms/src/satisfiability/mod.rs +++ b/tig-algorithms/src/satisfiability/mod.rs @@ -80,7 +80,8 @@ // c001_a041 -// c001_a042 +pub mod sat_adaptive; +pub use sat_adaptive as c001_a042; // c001_a043 diff --git a/tig-algorithms/src/satisfiability/sat_adaptive/benchmarker_outbound.rs b/tig-algorithms/src/satisfiability/sat_adaptive/benchmarker_outbound.rs new file mode 100644 index 0000000..f3dc67c --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_adaptive/benchmarker_outbound.rs @@ -0,0 +1,315 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let base_prob = 0.52; + let mut current_prob = base_prob; + let check_interval = 50; + let mut last_check_residual = residual_.len(); + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + if rounds % check_interval == 0 { + let progress = last_check_residual as i64 - residual_.len() as i64; + let progress_ratio = progress as f64 / last_check_residual as f64; + + let progress_threshold = 0.2 + 0.1 * f64::min(1.0, (clauses_ratio - 410.0) / 15.0); + + if progress <= 0 { + let prob_adjustment = 0.025 * (-progress as f64 / last_check_residual as f64).min(1.0); + current_prob = (current_prob + prob_adjustment).min(0.9); + } else if progress_ratio > progress_threshold { + current_prob = base_prob; + } else { + current_prob = current_prob * 0.8 + base_prob * 0.2; + } + + last_check_residual = residual_.len(); + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(current_prob) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_adaptive/commercial.rs b/tig-algorithms/src/satisfiability/sat_adaptive/commercial.rs new file mode 100644 index 0000000..324e53f --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_adaptive/commercial.rs @@ -0,0 +1,315 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let base_prob = 0.52; + let mut current_prob = base_prob; + let check_interval = 50; + let mut last_check_residual = residual_.len(); + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + if rounds % check_interval == 0 { + let progress = last_check_residual as i64 - residual_.len() as i64; + let progress_ratio = progress as f64 / last_check_residual as f64; + + let progress_threshold = 0.2 + 0.1 * f64::min(1.0, (clauses_ratio - 410.0) / 15.0); + + if progress <= 0 { + let prob_adjustment = 0.025 * (-progress as f64 / last_check_residual as f64).min(1.0); + current_prob = (current_prob + prob_adjustment).min(0.9); + } else if progress_ratio > progress_threshold { + current_prob = base_prob; + } else { + current_prob = current_prob * 0.8 + base_prob * 0.2; + } + + last_check_residual = residual_.len(); + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(current_prob) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_adaptive/inbound.rs b/tig-algorithms/src/satisfiability/sat_adaptive/inbound.rs new file mode 100644 index 0000000..885501f --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_adaptive/inbound.rs @@ -0,0 +1,315 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let base_prob = 0.52; + let mut current_prob = base_prob; + let check_interval = 50; + let mut last_check_residual = residual_.len(); + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + if rounds % check_interval == 0 { + let progress = last_check_residual as i64 - residual_.len() as i64; + let progress_ratio = progress as f64 / last_check_residual as f64; + + let progress_threshold = 0.2 + 0.1 * f64::min(1.0, (clauses_ratio - 410.0) / 15.0); + + if progress <= 0 { + let prob_adjustment = 0.025 * (-progress as f64 / last_check_residual as f64).min(1.0); + current_prob = (current_prob + prob_adjustment).min(0.9); + } else if progress_ratio > progress_threshold { + current_prob = base_prob; + } else { + current_prob = current_prob * 0.8 + base_prob * 0.2; + } + + last_check_residual = residual_.len(); + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(current_prob) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_adaptive/innovator_outbound.rs b/tig-algorithms/src/satisfiability/sat_adaptive/innovator_outbound.rs new file mode 100644 index 0000000..38b12c6 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_adaptive/innovator_outbound.rs @@ -0,0 +1,315 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let base_prob = 0.52; + let mut current_prob = base_prob; + let check_interval = 50; + let mut last_check_residual = residual_.len(); + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + if rounds % check_interval == 0 { + let progress = last_check_residual as i64 - residual_.len() as i64; + let progress_ratio = progress as f64 / last_check_residual as f64; + + let progress_threshold = 0.2 + 0.1 * f64::min(1.0, (clauses_ratio - 410.0) / 15.0); + + if progress <= 0 { + let prob_adjustment = 0.025 * (-progress as f64 / last_check_residual as f64).min(1.0); + current_prob = (current_prob + prob_adjustment).min(0.9); + } else if progress_ratio > progress_threshold { + current_prob = base_prob; + } else { + current_prob = current_prob * 0.8 + base_prob * 0.2; + } + + last_check_residual = residual_.len(); + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(current_prob) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/sat_adaptive/mod.rs b/tig-algorithms/src/satisfiability/sat_adaptive/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_adaptive/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/satisfiability/sat_adaptive/open_data.rs b/tig-algorithms/src/satisfiability/sat_adaptive/open_data.rs new file mode 100644 index 0000000..4a4711b --- /dev/null +++ b/tig-algorithms/src/satisfiability/sat_adaptive/open_data.rs @@ -0,0 +1,315 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use std::collections::HashMap; +use tig_challenges::satisfiability::*; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut rng = SmallRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap()) as u64); + + let mut p_single = vec![false; challenge.difficulty.num_variables]; + let mut n_single = vec![false; challenge.difficulty.num_variables]; + + let mut clauses_ = challenge.clauses.clone(); + let mut clauses: Vec> = Vec::with_capacity(clauses_.len()); + + let mut rounds = 0; + + let mut dead = false; + + while !(dead) { + let mut done = true; + for c in &clauses_ { + let mut c_: Vec = Vec::with_capacity(c.len()); // Preallocate with capacity + let mut skip = false; + for (i, l) in c.iter().enumerate() { + if (p_single[(l.abs() - 1) as usize] && *l > 0) + || (n_single[(l.abs() - 1) as usize] && *l < 0) + || c[(i + 1)..].contains(&-l) + { + skip = true; + break; + } else if p_single[(l.abs() - 1) as usize] + || n_single[(l.abs() - 1) as usize] + || c[(i + 1)..].contains(&l) + { + done = false; + continue; + } else { + c_.push(*l); + } + } + if skip { + done = false; + continue; + }; + match c_[..] { + [l] => { + done = false; + if l > 0 { + if n_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + p_single[(l.abs() - 1) as usize] = true; + } + } else { + if p_single[(l.abs() - 1) as usize] { + dead = true; + break; + } else { + n_single[(l.abs() - 1) as usize] = true; + } + } + } + [] => { + dead = true; + break; + } + _ => { + clauses.push(c_); + } + } + } + if done { + break; + } else { + clauses_ = clauses; + clauses = Vec::with_capacity(clauses_.len()); + } + } + + if dead { + return Ok(None); + } + + let num_variables = challenge.difficulty.num_variables; + let num_clauses = clauses.len(); + + let mut p_clauses: Vec> = vec![Vec::new(); num_variables]; + let mut n_clauses: Vec> = vec![Vec::new(); num_variables]; + + // Preallocate capacity for p_clauses and n_clauses + for c in &clauses { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + if p_clauses[var].capacity() == 0 { + p_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } else { + if n_clauses[var].capacity() == 0 { + n_clauses[var] = Vec::with_capacity(clauses.len() / num_variables + 1); + } + } + } + } + + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 { + p_clauses[var].push(i); + } else { + n_clauses[var].push(i); + } + } + } + + let mut variables = vec![false; num_variables]; + for v in 0..num_variables { + let num_p = p_clauses[v].len(); + let num_n = n_clauses[v].len(); + + let nad = 1.28; + let mut vad = nad + 1.0; + if num_n > 0 { + vad = num_p as f32 / num_n as f32; + } + + if vad <= nad { + variables[v] = false; + } else { + let prob = num_p as f64 / (num_p + num_n).max(1) as f64; + variables[v] = rng.gen_bool(prob) + } + } + + let mut num_good_so_far: Vec = vec![0; num_clauses]; + for (i, &ref c) in clauses.iter().enumerate() { + for &l in c { + let var = (l.abs() - 1) as usize; + if l > 0 && variables[var] { + num_good_so_far[i] += 1 + } else if l < 0 && !variables[var] { + num_good_so_far[i] += 1 + } + } + } + + + let mut residual_ = Vec::with_capacity(num_clauses); + let mut residual_indices = vec![None; num_clauses]; + + for (i, &num_good) in num_good_so_far.iter().enumerate() { + if num_good == 0 { + residual_.push(i); + residual_indices[i] = Some(residual_.len() - 1); + } + } + + let base_prob = 0.52; + let mut current_prob = base_prob; + let check_interval = 50; + let mut last_check_residual = residual_.len(); + + let clauses_ratio = challenge.difficulty.clauses_to_variables_percent as f64; + let num_vars = challenge.difficulty.num_variables as f64; + let max_fuel = 2000000000.0; + let base_fuel = (2000.0 + 40.0 * clauses_ratio) * num_vars; + let flip_fuel = 350.0 + 0.9 * clauses_ratio; + let max_num_rounds = ((max_fuel - base_fuel) / flip_fuel) as usize; + loop { + if !residual_.is_empty() { + + let rand_val = rng.gen::(); + + let i = residual_[rand_val % residual_.len()]; + let mut min_sad = clauses.len(); + let mut v_min_sad = usize::MAX; + let c = &mut clauses[i]; + + if c.len() > 1 { + let random_index = rand_val % c.len(); + c.swap(0, random_index); + } + for &l in c.iter() { + let abs_l = l.abs() as usize - 1; + let clauses_to_check = if variables[abs_l] { &p_clauses[abs_l] } else { &n_clauses[abs_l] }; + + let mut sad = 0; + for &c in clauses_to_check { + if num_good_so_far[c] == 1 { + sad += 1; + } + } + + if sad < min_sad { + min_sad = sad; + v_min_sad = abs_l; + } + } + + if rounds % check_interval == 0 { + let progress = last_check_residual as i64 - residual_.len() as i64; + let progress_ratio = progress as f64 / last_check_residual as f64; + + let progress_threshold = 0.2 + 0.1 * f64::min(1.0, (clauses_ratio - 410.0) / 15.0); + + if progress <= 0 { + let prob_adjustment = 0.025 * (-progress as f64 / last_check_residual as f64).min(1.0); + current_prob = (current_prob + prob_adjustment).min(0.9); + } else if progress_ratio > progress_threshold { + current_prob = base_prob; + } else { + current_prob = current_prob * 0.8 + base_prob * 0.2; + } + + last_check_residual = residual_.len(); + } + + let v = if min_sad == 0 { + v_min_sad + } else if rng.gen_bool(current_prob) { + c[0].abs() as usize - 1 + } else { + v_min_sad + }; + + if variables[v] { + for &c in &n_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + for &c in &p_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + } else { + for &c in &n_clauses[v] { + if num_good_so_far[c] == 1 { + residual_.push(c); + residual_indices[c] = Some(residual_.len() - 1); + } + num_good_so_far[c] -= 1; + } + + for &c in &p_clauses[v] { + num_good_so_far[c] += 1; + if num_good_so_far[c] == 1 { + let i = residual_indices[c].take().unwrap(); + let last = residual_.pop().unwrap(); + if i < residual_.len() { + residual_[i] = last; + residual_indices[last] = Some(i); + } + } + } + } + + variables[v] = !variables[v]; + } else { + break; + } + rounds += 1; + if rounds >= max_num_rounds { + return Ok(None); + } + } + return Ok(Some(Solution { variables })); +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; diff --git a/tig-algorithms/src/satisfiability/template.rs b/tig-algorithms/src/satisfiability/template.rs index c2d7153..f11c4cd 100644 --- a/tig-algorithms/src/satisfiability/template.rs +++ b/tig-algorithms/src/satisfiability/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::satisfiability::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/satisfiability/sat_adaptive.wasm b/tig-algorithms/wasm/satisfiability/sat_adaptive.wasm new file mode 100644 index 0000000000000000000000000000000000000000..98eae0e0a370dba84b67609054d0f590560e9801 GIT binary patch literal 157768 zcmeFa3%Fj@dEdEj-{pLlb3RE25FEi-dn4coNk&eHz?c}V)8Qfu0(KaNcsdi>X(r7% z#PJcT;>1VN!4gDv?3PKHmZZd%6KvrGB{*?9JPC=99MZZCamb~y+mZ&yOv+t5K%AST(Y6UAJdEw0oY1N?b&|dsy!twzkTO6pV#e4j(=o-PZ`$ z*5Kh#Fx?Xo;*IqLKt}aVll3^O#qWymyRXIwHq^&}nO|C3j@pep zt$+XbeE$#Z|DhzxzVDsicOd%txcxovJh1r&$Pb!>BcAG7q=$OBaOqCNB)ImzDT!5$)5hdBmJwv zvpv4%qM4+}^p=+1{Xmhp#Mkbe^6~t5Elz4_7PVbGlSGC7=&D&n^FF;J+v8#vc^1JQ z=BlRqx!p-qPcj$H`9J&8C%#zU9Yvk-&`3^|ol$hNULI~PtD36~Hy0Wy693T?%h5~} z(GY{n=KROGr{^Q_8UNX{>4JYb 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zp~HKvG!w%heLZk%(~aTH*Z>2XzOIp>HjSYjsDd#O13NT4E%Xaw(BP$128&Nk3|_`S hl2ju&oEZQgj#Lv4hz$?Z;0P==Ay#N`qy{Ww{y(LgDrNux literal 0 HcmV?d00001 From 4c06bc5ea936207b1b0faac03a742a157c6d2cb0 Mon Sep 17 00:00:00 2001 From: FiveMovesAhead Date: Thu, 16 Jan 2025 21:07:37 +0000 Subject: [PATCH 37/49] Compiled vehicle_routing/advanced_routing --- .../advanced_routing/benchmarker_outbound.rs | 234 ++++++++++++++++++ .../advanced_routing/commercial.rs | 234 ++++++++++++++++++ .../advanced_routing/inbound.rs | 234 ++++++++++++++++++ .../advanced_routing/innovator_outbound.rs | 234 ++++++++++++++++++ .../vehicle_routing/advanced_routing/mod.rs | 4 + .../advanced_routing/open_data.rs | 234 ++++++++++++++++++ tig-algorithms/src/vehicle_routing/mod.rs | 3 +- .../src/vehicle_routing/template.rs | 26 +- .../vehicle_routing/advanced_routing.wasm | Bin 0 -> 171363 bytes 9 files changed, 1178 insertions(+), 25 deletions(-) create mode 100644 tig-algorithms/src/vehicle_routing/advanced_routing/benchmarker_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_routing/commercial.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_routing/inbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_routing/innovator_outbound.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_routing/mod.rs create mode 100644 tig-algorithms/src/vehicle_routing/advanced_routing/open_data.rs create mode 100644 tig-algorithms/wasm/vehicle_routing/advanced_routing.wasm diff --git a/tig-algorithms/src/vehicle_routing/advanced_routing/benchmarker_outbound.rs b/tig-algorithms/src/vehicle_routing/advanced_routing/benchmarker_outbound.rs new file mode 100644 index 0000000..8d7001f --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_routing/benchmarker_outbound.rs @@ -0,0 +1,234 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Benchmarker Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_routing/commercial.rs b/tig-algorithms/src/vehicle_routing/advanced_routing/commercial.rs new file mode 100644 index 0000000..bb1110f --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_routing/commercial.rs @@ -0,0 +1,234 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Commercial License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_routing/inbound.rs b/tig-algorithms/src/vehicle_routing/advanced_routing/inbound.rs new file mode 100644 index 0000000..2b8f8e1 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_routing/inbound.rs @@ -0,0 +1,234 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later +version (the "License"); you may not use this file except in compliance with the +License. You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_routing/innovator_outbound.rs b/tig-algorithms/src/vehicle_routing/advanced_routing/innovator_outbound.rs new file mode 100644 index 0000000..e768ec0 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_routing/innovator_outbound.rs @@ -0,0 +1,234 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Innovator Outbound Game License v1.0 (the "License"); you +may not use this file except in compliance with the License. You may obtain a copy +of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_routing/mod.rs b/tig-algorithms/src/vehicle_routing/advanced_routing/mod.rs new file mode 100644 index 0000000..fcec967 --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_routing/mod.rs @@ -0,0 +1,4 @@ +mod benchmarker_outbound; +pub use benchmarker_outbound::solve_challenge; +#[cfg(feature = "cuda")] +pub use benchmarker_outbound::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/advanced_routing/open_data.rs b/tig-algorithms/src/vehicle_routing/advanced_routing/open_data.rs new file mode 100644 index 0000000..41bdafe --- /dev/null +++ b/tig-algorithms/src/vehicle_routing/advanced_routing/open_data.rs @@ -0,0 +1,234 @@ +/*! +Copyright 2024 syebastian + +Licensed under the TIG Open Data License v1.0 or (at your option) any later version +(the "License"); you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +https://github.com/tig-foundation/tig-monorepo/tree/main/docs/licenses + +Unless required by applicable law or agreed to in writing, software distributed +under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR +CONDITIONS OF ANY KIND, either express or implied. See the License for the specific +language governing permissions and limitations under the License. +*/ + +use rand::{rngs::{SmallRng, StdRng}, Rng, SeedableRng}; +use tig_challenges::vehicle_routing::{Challenge, Solution}; + +pub fn solve_challenge(challenge: &Challenge) -> anyhow::Result> { + let mut best_solution: Option = None; + let mut best_cost = std::i32::MAX; + + const INITIAL_TEMPERATURE: f32 = 2.0; + const COOLING_RATE: f32 = 0.995; + const ITERATIONS_PER_TEMPERATURE: usize = 2; + + let num_nodes = challenge.difficulty.num_nodes; + + let mut current_params = vec![1.0; num_nodes]; + let mut savings_list = create_initial_savings_list(challenge); + recompute_and_sort_savings(&mut savings_list, ¤t_params, challenge); + + let mut current_solution = create_solution(challenge, ¤t_params, &savings_list); + let mut current_cost = calculate_solution_cost(¤t_solution, &challenge.distance_matrix); + + if current_cost <= challenge.max_total_distance { + return Ok(Some(current_solution)); + } + + if (current_cost as f32 * 0.96) > challenge.max_total_distance as f32 { + return Ok(None); + } + + let mut temperature = INITIAL_TEMPERATURE; + let mut rng = StdRng::seed_from_u64(u64::from_le_bytes(challenge.seed[..8].try_into().unwrap())); + + while temperature > 1.0 { + for _ in 0..ITERATIONS_PER_TEMPERATURE { + let neighbor_params = generate_neighbor(¤t_params, &mut rng); + recompute_and_sort_savings(&mut savings_list, &neighbor_params, challenge); + let mut neighbor_solution = create_solution(challenge, &neighbor_params, &savings_list); + apply_local_search_until_no_improvement(&mut neighbor_solution, &challenge.distance_matrix); + let neighbor_cost = calculate_solution_cost(&neighbor_solution, &challenge.distance_matrix); + + let delta = neighbor_cost as f32 - current_cost as f32; + if delta < 0.0 || rng.gen::() < (-delta / temperature).exp() { + current_params = neighbor_params; + current_cost = neighbor_cost; + current_solution = neighbor_solution; + + if current_cost < best_cost { + best_cost = current_cost; + best_solution = Some(Solution { + routes: current_solution.routes.clone(), + }); + } + } + if best_cost <= challenge.max_total_distance { + return Ok(best_solution); + } + } + + temperature *= COOLING_RATE; + } + + Ok(best_solution) +} + +#[inline] +fn create_initial_savings_list(challenge: &Challenge) -> Vec<(f32, u8, u8)> { + let num_nodes = challenge.difficulty.num_nodes; + let capacity = ((num_nodes - 1) * (num_nodes - 2)) / 2; + let mut savings = Vec::with_capacity(capacity); + for i in 1..num_nodes { + for j in (i + 1)..num_nodes { + savings.push((0.0, i as u8, j as u8)); + } + } + savings +} + +#[inline] +fn recompute_and_sort_savings(savings_list: &mut [(f32, u8, u8)], params: &[f32], challenge: &Challenge) { + let distance_matrix = &challenge.distance_matrix; + + let mut zero_len = 0; + for (score, i, j) in savings_list.iter_mut() { + let i = *i as usize; + let j = *j as usize; + *score = params[i] * distance_matrix[0][i] as f32 + + params[j] * distance_matrix[j][0] as f32 - + params[i] * params[j] * distance_matrix[i][j] as f32; + } + + savings_list.sort_unstable_by(|a, b| b.0.partial_cmp(&a.0).unwrap()); +} + +#[inline] +fn generate_neighbor(current: &[f32], rng: &mut R) -> Vec { + current.iter().map(|¶m| { + let delta = rng.gen_range(-0.1..=0.1); + (param + delta).clamp(0.0, 2.0) + }).collect() +} + +#[inline] +fn apply_local_search_until_no_improvement(solution: &mut Solution, distance_matrix: &Vec>) { + let mut improved = true; + while improved { + improved = false; + for route in &mut solution.routes { + if two_opt(route, distance_matrix) { + improved = true; + } + } + } +} +#[inline] +fn two_opt(route: &mut Vec, distance_matrix: &Vec>) -> bool { + let n = route.len(); + let mut improved = false; + + for i in 1..n - 2 { + for j in i + 1..n - 1 { + let current_distance = distance_matrix[route[i - 1]][route[i]] + + distance_matrix[route[j]][route[j + 1]]; + let new_distance = distance_matrix[route[i - 1]][route[j]] + + distance_matrix[route[i]][route[j + 1]]; + + if new_distance < current_distance { + route[i..=j].reverse(); + improved = true; + } + } + } + + improved +} + +#[inline] +fn calculate_solution_cost(solution: &Solution, distance_matrix: &Vec>) -> i32 { + solution.routes.iter().map(|route| { + route.windows(2).map(|w| distance_matrix[w[0]][w[1]]).sum::() + }).sum() +} + +#[inline] +fn create_solution(challenge: &Challenge, params: &[f32], savings_list: &[(f32, u8, u8)]) -> Solution { + let distance_matrix = &challenge.distance_matrix; + let max_capacity = challenge.max_capacity; + let num_nodes = challenge.difficulty.num_nodes; + let demands = &challenge.demands; + + let mut routes = vec![None; num_nodes]; + for i in 1..num_nodes { + routes[i] = Some(vec![i]); + } + let mut route_demands = demands.clone(); + + for &(_, i, j) in savings_list { + let (i, j) = (i as usize, j as usize); + if let (Some(left_route), Some(right_route)) = (routes[i].as_ref(), routes[j].as_ref()) { + let (left_start, left_end) = (*left_route.first().unwrap(), *left_route.last().unwrap()); + let (right_start, right_end) = (*right_route.first().unwrap(), *right_route.last().unwrap()); + + if left_start == right_start || route_demands[left_start] + route_demands[right_start] > max_capacity { + continue; + } + + let mut new_route = routes[i].take().unwrap(); + let mut right_route = routes[j].take().unwrap(); + + if left_start == i { new_route.reverse(); } + if right_end == j { right_route.reverse(); } + + new_route.extend(right_route); + + let combined_demand = route_demands[left_start] + route_demands[right_start]; + let new_start = new_route[0]; + let new_end = *new_route.last().unwrap(); + + route_demands[new_start] = combined_demand; + route_demands[new_end] = combined_demand; + + routes[new_start] = Some(new_route.clone()); + routes[new_end] = Some(new_route); + } + } + + Solution { + routes: routes + .into_iter() + .enumerate() + .filter_map(|(i, route)| route.filter(|r| r[0] == i)) + .map(|mut route| { + route.insert(0, 0); + route.push(0); + route + }) + .collect(), + } +} + +#[cfg(feature = "cuda")] +mod gpu_optimisation { + use super::*; + use cudarc::driver::*; + use std::{collections::HashMap, sync::Arc}; + use tig_challenges::CudaKernel; + + // set KERNEL to None if algorithm only has a CPU implementation + pub const KERNEL: Option = None; + + // Important! your GPU and CPU version of the algorithm should return the same result + pub fn cuda_solve_challenge( + challenge: &Challenge, + dev: &Arc, + mut funcs: HashMap<&'static str, CudaFunction>, + ) -> anyhow::Result> { + solve_challenge(challenge) + } +} +#[cfg(feature = "cuda")] +pub use gpu_optimisation::{cuda_solve_challenge, KERNEL}; \ No newline at end of file diff --git a/tig-algorithms/src/vehicle_routing/mod.rs b/tig-algorithms/src/vehicle_routing/mod.rs index 930b4fb..d89fbd1 100644 --- a/tig-algorithms/src/vehicle_routing/mod.rs +++ b/tig-algorithms/src/vehicle_routing/mod.rs @@ -94,7 +94,8 @@ // c002_a048 -// c002_a049 +pub mod advanced_routing; +pub use advanced_routing as c002_a049; // c002_a050 diff --git a/tig-algorithms/src/vehicle_routing/template.rs b/tig-algorithms/src/vehicle_routing/template.rs index 02a7cab..b5c872b 100644 --- a/tig-algorithms/src/vehicle_routing/template.rs +++ b/tig-algorithms/src/vehicle_routing/template.rs @@ -1,11 +1,7 @@ /*! -Copyright [year copyright work created] [name of copyright owner] +Copyright [yyyy] [name of copyright owner] -Identity of Submitter [name of person or entity that submits the Work to TIG] - -UAI [UAI (if applicable)] - -Licensed under the TIG Inbound Game License v2.0 or (at your option) any later +Licensed under the TIG Inbound Game License v1.0 or (at your option) any later version (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at @@ -17,24 +13,6 @@ CONDITIONS OF ANY KIND, either express or implied. See the License for the speci language governing permissions and limitations under the License. */ -// REMOVE BELOW SECTION IF UNUSED -/* -REFERENCES AND ACKNOWLEDGMENTS - -This implementation is based on or inspired by existing work. Citations and -acknowledgments below: - -1. Academic Papers: - - [Author(s), "Paper Title", DOI (if available)] - -2. Code References: - - [Author(s), URL] - -3. Other: - - [Author(s), Details] - -*/ - // TIG's UI uses the pattern `tig_challenges::` to automatically detect your algorithm's challenge use anyhow::{anyhow, Result}; use tig_challenges::vehicle_routing::{Challenge, Solution}; diff --git a/tig-algorithms/wasm/vehicle_routing/advanced_routing.wasm b/tig-algorithms/wasm/vehicle_routing/advanced_routing.wasm new file mode 100644 index 0000000000000000000000000000000000000000..87cc27b45555bceff4158fde4218441b302442c4 GIT binary patch literal 171363 zcmeFa3z%KkRp)sg_gVK=)s<9|WtHsO=csH;mJ}3B zWlT!eD4|o5%BbX45)%xV?f?c9Fp(iCa9(tPgp|@|+7nDLGcZYm0VOewzmNuh-I;DP zB+>l-YoBxPEmg@6zHaF6n~CDO=j`X&Yp=cbdhLCp@4oj3<0y*apNiMsl^#8MG(LJ) zVt?bKk)9$;jqa+D$4jgNH9hOCTKrlj8Bv*}R*tCd5o@ccKx)%!96fU6NOV^{JgddZ zkx(!`93Q4D-qbIJ(oe^~9jBE_J5A!a84p)RV*VG!aVt*rzqFdfwOX9&Up=W*h8mSh zGE{5StF=m+rj)EyBP(C2Q!%Z?$!J~gaiznbA$ug1tTc|R)oP{Lu=YuaNUm1WM5T-W zHE0t^)N&=MHY@3>ilAuJn`)y5jA_KrYA_nr){NEYJ$`%qJ>OFWl2x@P*zU{m}60W_|6AH~mj<`Jcc4#_#;+d#+jYFXPU;;}h|zxVCQ3 zHFu3)_1yoy 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