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Implement C-AdamW #146

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2 changes: 1 addition & 1 deletion src/evaltune_main.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -75,7 +75,7 @@
std::cout << "Loaded " << positions.size() << " FENs from " << fenFiles.size() << " files."
<< std::endl;

Clockwork::Autograd::AdamW optim(10, 0.9, 0.999, 1e-8, 0.0);
Clockwork::Autograd::CAdamW optim(10, 0.9, 0.999, 1e-8, 0.0);

i32 epochs = 1000;
const f64 K = 1.0 / 400;
Expand Down Expand Up @@ -106,7 +106,7 @@
size_t idx = indices[j];
f64 y = results[idx];
Position pos = positions[idx];
auto result = (evaluate_white_pov(pos) * K)->sigmoid();

Check failure on line 109 in src/evaltune_main.cpp

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src/evaltune_main.cpp:109:66 [clang-diagnostic-error] 

member reference type 'f64' (aka 'double') is not a pointer
batch_outputs.push_back(result);
batch_targets.push_back(y);
}
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161 changes: 161 additions & 0 deletions src/tuning/optim.hpp
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Expand Up @@ -177,5 +177,166 @@ class AdamW {
}
};

class CAdamW {
private:
std::vector<ValuePtr> m_value_params;
std::vector<PairPtr> m_pair_params;

f64 m_lr;
f64 m_beta1;
f64 m_beta2;
f64 m_eps;
f64 m_weight_decay;
long long m_t;

std::vector<f64> m_m;
std::vector<f64> m_v;
std::vector<f128> m_pair_m;
std::vector<f128> m_pair_v;

public:
explicit CAdamW(
f64 lr = 1e-3, f64 beta1 = 0.9, f64 beta2 = 0.999, f64 eps = 1e-8, f64 weight_decay = 0.01) :
m_lr(lr),
m_beta1(beta1),
m_beta2(beta2),
m_eps(eps),
m_weight_decay(weight_decay),
m_t(0) {
auto graph = Graph::get();
m_value_params = graph->get_parameters();
m_pair_params = graph->get_pair_parameters();

m_m.resize(m_value_params.size(), 0.0);
m_v.resize(m_value_params.size(), 0.0);

m_pair_m.resize(m_pair_params.size(), f128::zero());
m_pair_v.resize(m_pair_params.size(), f128::zero());
}

void step() {
m_t += 1;

const f64 b1t = std::pow(m_beta1, static_cast<f64>(m_t));
const f64 b2t = std::pow(m_beta2, static_cast<f64>(m_t));
const f64 inv1mb1t = 1.0 / (1.0 - b1t);
const f64 inv1mb2t = 1.0 / (1.0 - b2t);

// ---------------- Value parameters ----------------
{
std::vector<f64> adam_updates;
std::vector<bool> alignment_mask;
i64 alignment_mask_nnz = 0;

const size_t N = m_value_params.size();

adam_updates.resize(N);
alignment_mask.resize(N);

for (size_t i = 0; i < N; ++i) {
auto& p = m_value_params[i];
const f64 g = p->get_gradient();

m_m[i] = m_beta1 * m_m[i] + (1.0 - m_beta1) * g;

m_v[i] = m_beta2 * m_v[i] + (1.0 - m_beta2) * g * g;

const f64 m_hat = m_m[i] * inv1mb1t;
const f64 v_hat = m_v[i] * inv1mb2t;

adam_updates[i] = m_lr * m_hat / (std::sqrt(v_hat) + m_eps);

bool alignment = adam_updates[i] * g > 0;
alignment_mask[i] = alignment;
alignment_mask_nnz += alignment;
}

f64 cautiousness_rescale = static_cast<f64>(N) / (alignment_mask_nnz + 1);

for (size_t i = 0; i < N; ++i) {
auto& p = m_value_params[i];

const f64 adam_update = alignment_mask[i] * cautiousness_rescale * adam_updates[i];
const f64 weight_decay_update = m_lr * m_weight_decay * p->get_value();

const f64 total_update = -(adam_update + weight_decay_update);

p->change_value(total_update);
}
}

// ---------------- Pair parameters ----------------
{
std::vector<std::array<f64, 2>> adam_updates;
std::vector<std::array<bool, 2>> alignment_mask;
i64 alignment_mask_nnz = 0;

const size_t N = m_pair_params.size();

adam_updates.resize(N);
alignment_mask.resize(N);

for (size_t i = 0; i < N; ++i) {
auto& p = m_pair_params[i];
auto& m = m_pair_m[i];
auto& v = m_pair_v[i];

const f128 g_vec = f128::make(p->grad_first(), p->grad_second());
const f128 g2_vec = f128::make(p->grad_first() * p->grad_first(),
p->grad_second() * p->grad_second());

const f128 m_scaled = f128::mul_scalar(m, m_beta1);
const f128 g_scaled = f128::mul_scalar(g_vec, (1.0 - m_beta1));
m = f128::add(m_scaled, g_scaled);

const f128 v_scaled = f128::mul_scalar(v, m_beta2);
const f128 g2_scaled = f128::mul_scalar(g2_vec, (1.0 - m_beta2));
v = f128::add(v_scaled, g2_scaled);

const f128 m_hat = f128::mul_scalar(m, inv1mb1t);
const f128 v_hat = f128::mul_scalar(v, inv1mb2t);

const f64 adam_upd_f = m_lr * m_hat.first() / (std::sqrt(v_hat.first()) + m_eps);
const f64 adam_upd_s = m_lr * m_hat.second() / (std::sqrt(v_hat.second()) + m_eps);

adam_updates[i] = {adam_upd_f, adam_upd_s};

bool align_f = adam_upd_f * p->grad_first() > 0;
bool align_s = adam_upd_s * p->grad_second() > 0;

alignment_mask[i] = {align_f, align_s};
alignment_mask_nnz += align_f + align_s;
}

f64 cautiousness_rescale = static_cast<f64>(N * 2) / (alignment_mask_nnz + 1);

for (size_t i = 0; i < N; ++i) {
auto& p = m_pair_params[i];

bool align_f = alignment_mask[i][0];
bool align_s = alignment_mask[i][1];

const f64 adam_upd_f = align_f * cautiousness_rescale * adam_updates[i][0];
const f64 adam_upd_s = align_s * cautiousness_rescale * adam_updates[i][1];

const f64 decay_upd_f = m_lr * m_weight_decay * p->first();
const f64 decay_upd_s = m_lr * m_weight_decay * p->second();

const f64 total_upd_f = -(adam_upd_f + decay_upd_f);
const f64 total_upd_s = -(adam_upd_s + decay_upd_s);

p->m_values = f128::add(p->m_values, f128::make(total_upd_f, total_upd_s));
}
}
}

void set_lr(f64 lr) {
m_lr = lr;
}
f64 get_lr() const {
return m_lr;
}
};

} // namespace Autograd
} // namespace Clockwork
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