File size: 4,140 Bytes
d5175d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
/**
* Copyright 2017-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the license found in the
* LICENSE file in the root directory of this source tree.
*/
/*
C++ code for solving the linear assignment problem.
Based on the Auction Algorithm from https://dspace.mit.edu/bitstream/handle/1721.1/3265/P-2108-26912652.pdf and the implementation from:
https://github.com/bkj/auction-lap
Adapted to be more efficient when each worker is looking for k jobs instead of 1.
*/
#include <torch/extension.h>
#include <iostream>
using namespace torch::indexing;
torch::Tensor balanced_assignment(torch::Tensor job_and_worker_to_score) {
int max_iterations = 100;
torch::Tensor epsilon = (job_and_worker_to_score.max() - job_and_worker_to_score.min()) / 50;
epsilon.clamp_min_(1e-04);
torch::Tensor worker_and_job_to_score = job_and_worker_to_score.detach().transpose(0,1).contiguous();
int num_workers = worker_and_job_to_score.size(0);
int num_jobs = worker_and_job_to_score.size(1);
auto device = worker_and_job_to_score.device();
int jobs_per_worker = num_jobs / num_workers;
torch::Tensor value = worker_and_job_to_score.clone();
int counter = 0;
torch::Tensor max_value = worker_and_job_to_score.max();
torch::Tensor bid_indices;
torch::Tensor cost = worker_and_job_to_score.new_zeros({1, num_jobs});
torch::Tensor bids = worker_and_job_to_score.new_empty({num_workers, num_jobs});
torch::Tensor bid_increments = worker_and_job_to_score.new_empty({num_workers, jobs_per_worker});
torch::Tensor top_values = worker_and_job_to_score.new_empty({num_workers, jobs_per_worker + 1});
torch::Tensor high_bids = worker_and_job_to_score.new_empty({num_jobs});
torch::Tensor top_index = top_values.to(torch::kLong);
torch::Tensor high_bidders = top_index.new_empty({num_jobs});
torch::Tensor have_bids = high_bidders.to(torch::kBool);
torch::Tensor jobs_indices = torch::arange({num_jobs}, torch::dtype(torch::kLong).device(device));
torch::Tensor true_tensor = torch::ones({1}, torch::dtype(torch::kBool).device(device));
while (true) {
bids.zero_();
torch::topk_out(top_values, top_index, value, jobs_per_worker + 1, 1);
// Each worker bids the difference in value between that job and the k+1th job
torch::sub_out(bid_increments,
top_values.index({Slice(None, None), Slice(0, jobs_per_worker)}),
top_values.index({Slice(None, None), jobs_per_worker}).unsqueeze(1));
bid_increments.add_(epsilon);
bids.scatter_(1,
top_index.index({Slice(None, None),Slice(0, jobs_per_worker)}),
bid_increments);
if (counter < max_iterations && counter > 0) {
// Put in a minimal bid to retain items from the last round if no-one else bids for them this round
bids.view(-1).index_put_({bid_indices}, epsilon);
}
// Find the highest bidding worker per job
torch::max_out(high_bids, high_bidders, bids, 0);
torch::gt_out(have_bids, high_bids, 0);
if (have_bids.all().item<bool>()) {
// All jobs were bid for
break;
}
// Make popular items more expensive
cost.add_(high_bids);
torch::sub_out(value, worker_and_job_to_score, cost);
bid_indices = ((high_bidders * num_jobs) + jobs_indices).index({have_bids});
if (counter < max_iterations) {
// Make sure that this item will be in the winning worker's top-k next time.
value.view(-1).index_put_({bid_indices}, max_value);
}
else {
// Suboptimal approximation that converges quickly from current solution
value.view(-1).index_put_({bid_indices}, worker_and_job_to_score.view(-1).index({bid_indices}));
}
counter += 1;
}
return top_index.index({Slice(None, None), Slice(0, jobs_per_worker)}).reshape(-1);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("balanced_assignment", &balanced_assignment, "Balanced Assignment");
}
|