Add telemetry_and_inference example and finalize previous DLL/ABI fixes

examples/telemetry_and_inference.cpp

@@ -0,0 +1,114 @@
+/**
+ * @file telemetry_and_inference.cpp
+ * @brief Example showcasing telemetry instrumentation and model inference.
+ */
+
+#include <iostream>
+#include <chrono>
+#include <torch/torch.h>
+#include "fces/optimizer.hpp"
+#include "fces/telemetry.hpp"
+
+// Define a simple neural network for nonlinear regression: y = x^2
+struct RegressionNet : torch::nn::Module {
+    torch::nn::Linear fc1{nullptr}, fc2{nullptr};
+
+    RegressionNet() {
+        fc1 = register_module("fc1", torch::nn::Linear(1, 16));
+        fc2 = register_module("fc2", torch::nn::Linear(16, 1));
+    }
+
+    torch::Tensor forward(torch::Tensor x) {
+        x = torch::tanh(fc1->forward(x));
+        return fc2->forward(x);
+    }
+};
+
+int main() {
+    fces::Telemetry::get().info("app_start", "Telemetry and Inference demo initialized.");
+
+    // 1. Create Model and Data
+    auto model = std::make_shared<RegressionNet>();
+    
+    // Generate training data: x in [-2, 2], y = x^2 + noise
+    auto x_train = torch::linspace(-2.0, 2.0, 100).unsqueeze(1);
+    auto y_train = x_train.pow(2) + 0.1 * torch::randn({100, 1});
+
+    // 2. Configure Optimizer
+    std::vector<torch::Tensor> params;
+    for (auto& p : model->parameters()) {
+        params.push_back(p);
+    }
+
+    fces::FCESOptimizer optimizer(
+        params,
+        fces::FCESConfig{}
+            .set_lr(2e-3f)
+            .set_population_size(150)
+            .set_total_steps(100)
+    );
+
+    fces::Telemetry::get().info("training_start", "Beginning neural net optimization with FCES.");
+
+    auto start_train = std::chrono::high_resolution_clock::now();
+
+    // 3. Optimization Loop
+    for (int epoch = 0; epoch <= 100; ++epoch) {
+        optimizer.zero_grad();
+        auto pred = model->forward(x_train);
+        auto loss = torch::mse_loss(pred, y_train);
+        loss.backward();
+        optimizer.step();
+        optimizer.update_fitness(loss.item<float>());
+
+        if (epoch % 20 == 0) {
+            fces::Telemetry::get().info("epoch_checkpoint", 
+                "Epoch " + std::to_string(epoch) + " | Loss: " + std::to_string(loss.item<float>()));
+        }
+    }
+
+    auto end_train = std::chrono::high_resolution_clock::now();
+    double train_duration = std::chrono::duration<double, std::milli>(end_train - start_train).count();
+
+    fces::Telemetry::get().info("training_complete", 
+        "Duration: " + std::to_string(train_duration) + " ms");
+
+    // 4. Inference Phase
+    fces::Telemetry::get().info("inference_phase_start", "Evaluating model on new test inputs.");
+
+    // Generate test inputs
+    auto x_test = torch::tensor({-1.5f, -0.5f, 0.0f, 0.5f, 1.5f}).unsqueeze(1);
+    auto y_expected = x_test.pow(2);
+
+    // Switch model to evaluation mode
+    model->eval();
+
+    // Run inference and measure latency
+    auto start_inf = std::chrono::high_resolution_clock::now();
+    torch::Tensor y_pred;
+    {
+        torch::NoGradGuard no_grad;
+        y_pred = model->forward(x_test);
+    }
+    auto end_inf = std::chrono::high_resolution_clock::now();
+    double inf_duration = std::chrono::duration<double, std::milli>(end_inf - start_inf).count();
+
+    // Log telemetry for inference performance
+    fces::Telemetry::get().info("inference_perf", 
+        "Inputs: " + std::to_string(x_test.size(0)) + " | Latency: " + std::to_string(inf_duration) + " ms");
+
+    // Print predictions and expected values side-by-side
+    std::cout << "\n================ INFERENCE RESULTS ================" << std::endl;
+    std::cout << "Input (x)  |  Predicted (y_pred)  |  Expected (y_expected)" << std::endl;
+    std::cout << "----------------------------------------------------" << std::endl;
+    for (int i = 0; i < x_test.size(0); ++i) {
+        float x_val = x_test[i][0].item<float>();
+        float pred_val = y_pred[i][0].item<float>();
+        float exp_val = y_expected[i][0].item<float>();
+        std::printf("  %7.2f  |        %7.4f       |        %7.4f\n", x_val, pred_val, exp_val);
+    }
+    std::cout << "====================================================\n" << std::endl;
+
+    fces::Telemetry::get().info("app_finish", "Exiting demo successfully.");
+    return 0;
+}