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26d5b81 | 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 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | #include <iostream>
#include <chrono>
#include "../include/neuroflow/model.hpp"
#include "../include/neuroflow/backprop.hpp"
using namespace neuroflow;
void test_forward_cache() {
std::cout << "\n=== Forward Cache Test ===\n";
NeuroFlowModel::Config cfg;
cfg.input_dim = 64;
cfg.hidden_dim = 32;
cfg.output_dim = 5;
cfg.memory_dim = 32;
cfg.num_layers = 1;
cfg.num_associations = 2;
NeuroFlowModel model(cfg);
FullBackpropEngine bp(model);
Tensor input({2, 64});
float* data = input.as_fp32();
for (size_t i = 0; i < input.numel(); ++i) {
data[i] = 0.1f * i;
}
auto output = bp.forward_with_cache(input);
std::cout << " Forward cache stored:\n";
std::cout << " input: [" << bp.cache.input.shape_[0] << ", " << bp.cache.input.shape_[1] << "]\n";
std::cout << " h: [" << bp.cache.h.shape_[0] << ", " << bp.cache.h.shape_[1] << "]\n";
std::cout << " ecn_hidden: " << bp.cache.ecn_hidden.size() << " layers\n";
std::cout << " combined: [" << bp.cache.combined.shape_[0] << ", " << bp.cache.combined.shape_[1] << "]\n";
std::cout << " Output shape: [" << output.output.shape_[0]
<< ", " << output.output.shape_[1] << "]\n";
std::cout << " [PASS] Forward cache works\n";
}
void test_backward_pass() {
std::cout << "\n=== Backward Pass Test ===\n";
NeuroFlowModel::Config cfg;
cfg.input_dim = 64;
cfg.hidden_dim = 32;
cfg.output_dim = 5;
cfg.memory_dim = 32;
cfg.num_layers = 1;
cfg.num_associations = 2;
NeuroFlowModel model(cfg);
FullBackpropEngine bp(model);
Tensor input({2, 64});
for (size_t i = 0; i < input.numel(); ++i) {
input.as_fp32()[i] = 0.1f * i;
}
auto output = bp.forward_with_cache(input);
Tensor output_grad({2, 5});
for (size_t i = 0; i < output_grad.numel(); ++i) {
output_grad.as_fp32()[i] = 0.01f;
}
auto grads = bp.backward(output_grad);
std::cout << " Backward gradients computed:\n";
std::cout << " output_fusion_down_weight_grad: [" << grads.output_fusion_down_weight_grad.shape_[0]
<< ", " << grads.output_fusion_down_weight_grad.shape_[1] << "]\n";
std::cout << " output_fusion_up_weight_grad: [" << grads.output_fusion_up_weight_grad.shape_[0]
<< ", " << grads.output_fusion_up_weight_grad.shape_[1] << "]\n";
std::cout << " input_grad: [" << grads.input_grad.shape_[0]
<< ", " << grads.input_grad.shape_[1] << "]\n";
float grad_sum = 0;
const float* wg = grads.output_fusion_down_weight_grad.as_fp32();
for (size_t i = 0; i < grads.output_fusion_down_weight_grad.numel(); ++i) {
grad_sum += std::abs(wg[i]);
}
std::cout << " Total weight gradient magnitude: " << grad_sum << "\n";
std::cout << " [PASS] Backward pass works\n";
}
void test_trainer() {
std::cout << "\n=== FullTrainer Test ===\n";
NeuroFlowModel::Config cfg;
cfg.input_dim = 64;
cfg.hidden_dim = 32;
cfg.output_dim = 5;
cfg.memory_dim = 32;
cfg.num_layers = 1;
cfg.num_associations = 2;
NeuroFlowModel model(cfg);
FullTrainer trainer(model, 0.01f);
Tensor input({2, 64});
Tensor target({2, 5});
std::mt19937 rng(42);
std::uniform_real_distribution<float> dist(-0.05f, 0.05f);
for (size_t i = 0; i < input.numel(); ++i) {
input.as_fp32()[i] = dist(rng);
}
for (size_t i = 0; i < target.numel(); ++i) {
target.as_fp32()[i] = (i % 5 == 2) ? 1.0f : 0.0f;
}
auto output1 = model.forward(input);
float loss1 = LossFunctions::mse(output1.output, target);
std::cout << " Training for 10 steps...\n";
for (int i = 0; i < 10; ++i) {
auto step = trainer.train_step(input, target);
std::cout << " Step " << i << ": loss=" << step.loss
<< ", grad_norm=" << step.grad_norm << "\n";
}
auto output2 = model.forward(input);
float loss2 = LossFunctions::mse(output2.output, target);
std::cout << " Initial loss: " << loss1 << "\n";
std::cout << " Final loss: " << loss2 << "\n";
std::cout << " Loss reduction: " << (loss1 - loss2) << "\n";
if (loss2 <= loss1) {
std::cout << " [PASS] Training reduces loss\n";
} else {
std::cout << " [WARN] Loss increased\n";
}
}
void test_memory_consolidation_training() {
std::cout << "\n=== Memory Consolidation Training Test ===\n";
NeuroFlowModel::Config cfg;
cfg.input_dim = 64;
cfg.hidden_dim = 32;
cfg.output_dim = 5;
cfg.memory_slots = 16;
cfg.memory_dim = 16;
NeuroFlowModel model(cfg);
FullTrainer trainer(model, 0.01f);
std::cout << " Initial memory bank sample: " << model.memory->memory_bank.as_fp32()[0] << "\n";
std::mt19937 rng(123);
std::uniform_real_distribution<float> dist(-0.05f, 0.05f);
std::uniform_real_distribution<float> dist01(0.0f, 1.0f);
for (int batch = 0; batch < 5; ++batch) {
Tensor input({4, 64});
Tensor target({4, 5});
for (size_t i = 0; i < input.numel(); ++i) {
input.as_fp32()[i] = dist(rng);
}
for (size_t i = 0; i < target.numel(); ++i) {
target.as_fp32()[i] = dist01(rng);
}
auto step = trainer.train_step(input, target);
std::cout << " Batch " << batch << ": loss=" << step.loss << "\n";
}
float mem_after = model.memory->memory_bank.as_fp32()[0];
std::cout << " Memory bank after training: " << mem_after << "\n";
std::cout << " Memory slots: " << model.memory->memory_slots << "\n";
std::cout << " [PASS] Memory consolidation during training\n";
}
void test_gradient_flow() {
std::cout << "\n=== Gradient Flow Test ===\n";
NeuroFlowModel::Config cfg;
cfg.input_dim = 32;
cfg.hidden_dim = 16;
cfg.output_dim = 3;
cfg.num_layers = 2;
NeuroFlowModel model(cfg);
FullBackpropEngine bp(model);
Tensor input({1, 32});
for (size_t i = 0; i < 32; ++i) input.as_fp32()[i] = 0.1f;
auto output = bp.forward_with_cache(input);
Tensor output_grad({1, 3});
output_grad.as_fp32()[0] = 1.0f;
output_grad.as_fp32()[1] = 0.0f;
output_grad.as_fp32()[2] = -1.0f;
auto grads = bp.backward(output_grad);
std::cout << " Input gradient samples:\n";
const float* ig = grads.input_grad.as_fp32();
for (size_t i = 0; i < 5; ++i) {
std::cout << " grad[" << i << "] = " << ig[i] << "\n";
}
std::cout << " [PASS] Gradient flows to input\n";
}
int main() {
std::cout << "========================================\n";
std::cout << "NeuroFlow Backpropagation Tests\n";
std::cout << "========================================\n";
test_forward_cache();
test_backward_pass();
test_trainer();
test_memory_consolidation_training();
test_gradient_flow();
std::cout << "\n========================================\n";
std::cout << "All Tests Complete!\n";
std::cout << "========================================\n";
return 0;
}
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