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Simplify passing constants that may vary between calls.

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This commit is contained in:
Tomasz Sobczyk
2020-11-30 20:26:02 +01:00
committed by nodchip
parent cbd973fdaa
commit 01ae7b1e2c
2 changed files with 47 additions and 8 deletions
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32
src/learn/autograd.h
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@@ -209,7 +209,7 @@ namespace Learner::Autograd::UnivariateStatic
struct VariableParameter : Evaluable<T, VariableParameter<T, I>>
{
using ValueType = T;
static constexpr bool is_constant = false;
constexpr VariableParameter()
@@ -281,6 +281,36 @@ namespace Learner::Autograd::UnivariateStatic
T m_x;
};
// The "constant" may change between executions, but is assumed to be
// constant during a single evaluation.
template <typename T>
struct ConstantRef : Evaluable<T, ConstantRef<T>>
{
using ValueType = T;
static constexpr bool is_constant = true;
constexpr ConstantRef(const T& x) :
m_x(x)
{
}
template <typename... ArgsTs>
[[nodiscard]] T calculate_value(const std::tuple<ArgsTs...>&) const
{
return m_x;
}
template <typename... ArgsTs>
[[nodiscard]] T calculate_grad(const std::tuple<ArgsTs...>&) const
{
return T(0.0);
}
private:
const T& m_x;
};
template <typename LhsT, typename RhsT, typename T = typename std::remove_reference_t<LhsT>::ValueType>
struct Sum : Evaluable<T, Sum<LhsT, RhsT, T>>
{

23
src/learn/learn.cpp
View File

@@ -220,6 +220,16 @@ namespace Learner
return loss_;
}
template <typename ValueT>
static auto& expected_perf_(ValueT&& v_)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto perf_ = sigmoid(std::forward<ValueT>(v_) * ConstantRef<double>(winning_probability_coefficient));
return perf_;
}
static ValueWithGrad<double> get_loss(Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
@@ -238,18 +248,17 @@ namespace Learner
auto loss_ = pow(q_ - p_, 2.0) * (1.0 / (2400.0 * 2.0 * 600.0));
*/
static thread_local auto q_ = sigmoid(VariableParameter<double, 0>{} * ConstantParameter<double, 4>{});
static thread_local auto p_ = sigmoid(ConstantParameter<double, 1>{} * ConstantParameter<double, 4>{});
static thread_local auto q_ = expected_perf_(VariableParameter<double, 0>{});
static thread_local auto p_ = expected_perf_(ConstantParameter<double, 1>{});
static thread_local auto t_ = (ConstantParameter<double, 2>{} + 1.0) * 0.5;
static thread_local auto lambda_ = ConstantParameter<double, 3>{};
static thread_local auto loss_ = cross_entropy_(q_, p_, t_, lambda_);
auto args = std::tuple(
(double)shallow,
(double)teacher_signal,
(double)result,
calculate_lambda(teacher_signal),
winning_probability_coefficient
(double)shallow,
(double)teacher_signal,
(double)result,
calculate_lambda(teacher_signal)
);
return loss_.eval(args).clamp_grad(max_grad);