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4f9b60dd53
* Remove "using namespace mlx::core" in benchmarks/examples * Fix building example extension * A missing one in comment * Fix building on M chips
55 lines
1.3 KiB
C++
55 lines
1.3 KiB
C++
// Copyright © 2023 Apple Inc.
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#include <chrono>
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#include <cmath>
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#include <iostream>
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#include "mlx/mlx.h"
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#include "timer.h"
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/**
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* An example of logistic regression with MLX.
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*/
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namespace mx = mlx::core;
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int main() {
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int num_features = 100;
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int num_examples = 1'000;
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int num_iters = 10'000;
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float learning_rate = 0.1;
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// True parameters
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auto w_star = mx::random::normal({num_features});
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// The input examples
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auto X = mx::random::normal({num_examples, num_features});
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// Labels
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auto y = mx::matmul(X, w_star) > 0;
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// Initialize random parameters
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mx::array w = 1e-2 * mx::random::normal({num_features});
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auto loss_fn = [&](mx::array w) {
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auto logits = mx::matmul(X, w);
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auto scale = (1.0f / num_examples);
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return scale * mx::sum(mx::logaddexp(mx::array(0.0f), logits) - y * logits);
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};
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auto grad_fn = mx::grad(loss_fn);
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auto tic = timer::time();
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for (int it = 0; it < num_iters; ++it) {
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auto grads = grad_fn(w);
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w = w - learning_rate * grads;
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mx::eval(w);
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}
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auto toc = timer::time();
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auto loss = loss_fn(w);
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auto acc = mx::sum((mx::matmul(X, w) > 0) == y) / num_examples;
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auto throughput = num_iters / timer::seconds(toc - tic);
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std::cout << "Loss " << loss << ", Accuracy, " << acc << ", Throughput "
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<< throughput << " (it/s)." << std::endl;
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}
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