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Avoid computing gradient for validation loss.

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This commit is contained in:
Tomasz Sobczyk
2020-11-30 22:25:05 +01:00
committed by nodchip
parent fafb9557a8
commit 28d6d7cb03
2 changed files with 95 additions and 11 deletions
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98
src/learn/learn.cpp
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@@ -310,7 +310,8 @@ namespace Learner
return perf;
}
[[maybe_unused]] static ValueWithGrad<double> get_loss_noob(Value shallow, Value teacher_signal, int result, int /* ply */)
[[maybe_unused]] static ValueWithGrad<double> get_loss_noob(
Value shallow, Value teacher_signal, int result, int /* ply */)
{
using namespace Learner::Autograd::UnivariateStatic;
@@ -328,7 +329,7 @@ namespace Learner
return loss_.eval(args);
}
static ValueWithGrad<double> get_loss_cross_entropy(Value shallow, Value teacher_signal, int result, int /* ply */)
static auto& get_loss_cross_entropy_()
{
using namespace Learner::Autograd::UnivariateStatic;
@@ -338,18 +339,45 @@ namespace Learner
static thread_local auto lambda_ = ConstantParameter<double, 3>{};
static thread_local auto& loss_ = cross_entropy_(q_, p_, t_, lambda_);
auto args = std::tuple(
return loss_;
}
static auto get_loss_cross_entropy_args(
Value shallow, Value teacher_signal, int result)
{
return std::tuple(
(double)shallow,
(double)teacher_signal,
(double)result,
calculate_lambda(teacher_signal)
);
}
static ValueWithGrad<double> get_loss_cross_entropy(
Value shallow, Value teacher_signal, int result, int /* ply */)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto& loss_ = get_loss_cross_entropy_();
auto args = get_loss_cross_entropy_args(shallow, teacher_signal, result);
return loss_.eval(args);
}
static ValueWithGrad<double> get_loss_cross_entropy_use_wdl(
Value shallow, Value teacher_signal, int result, int ply)
static ValueWithGrad<double> get_loss_cross_entropy_no_grad(
Value shallow, Value teacher_signal, int result, int /* ply */)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto& loss_ = get_loss_cross_entropy_();
auto args = get_loss_cross_entropy_args(shallow, teacher_signal, result);
return { loss_.value(args), 0.0 };
}
static auto& get_loss_cross_entropy_use_wdl_()
{
using namespace Learner::Autograd::UnivariateStatic;
@@ -364,7 +392,13 @@ namespace Learner
static thread_local auto lambda_ = ConstantParameter<double, 3>{};
static thread_local auto& loss_ = cross_entropy_(q_, p_, t_, lambda_);
auto args = std::tuple(
return loss_;
}
static auto get_loss_cross_entropy_use_wdl_args(
Value shallow, Value teacher_signal, int result, int ply)
{
return std::tuple(
(double)shallow,
// This is required because otherwise MSVC crashes :(
expected_perf_use_wdl(scale_score(teacher_signal), ply),
@@ -372,10 +406,32 @@ namespace Learner
calculate_lambda(teacher_signal),
(double)std::min(240, ply)
);
}
static ValueWithGrad<double> get_loss_cross_entropy_use_wdl(
Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto& loss_ = get_loss_cross_entropy_use_wdl_();
auto args = get_loss_cross_entropy_use_wdl_args(shallow, teacher_signal, result, ply);
return loss_.eval(args);
}
static ValueWithGrad<double> get_loss_cross_entropy_use_wdl_no_grad(
Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto& loss_ = get_loss_cross_entropy_use_wdl_();
auto args = get_loss_cross_entropy_use_wdl_args(shallow, teacher_signal, result, ply);
return { loss_.value(args), 0.0 };
}
static auto get_loss(Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
@@ -390,7 +446,21 @@ namespace Learner
}
}
static auto get_loss(
static auto get_loss_no_grad(Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
if (use_wdl)
{
return get_loss_cross_entropy_use_wdl_no_grad(shallow, teacher_signal, result, ply);
}
else
{
return get_loss_cross_entropy_no_grad(shallow, teacher_signal, result, ply);
}
}
[[maybe_unused]] static auto get_loss(
Value teacher_signal,
Value shallow,
const PackedSfenValue& psv)
@@ -398,6 +468,14 @@ namespace Learner
return get_loss(shallow, teacher_signal, psv.game_result, psv.gamePly);
}
static auto get_loss_no_grad(
Value teacher_signal,
Value shallow,
const PackedSfenValue& psv)
{
return get_loss_no_grad(shallow, teacher_signal, psv.game_result, psv.gamePly);
}
// Class to generate sfen with multiple threads
struct LearnerThink
{
@@ -828,11 +906,11 @@ namespace Learner
if (psv.size() && test_loss_sum.count() > 0)
{
test_loss_sum.print("val", out);
test_loss_sum.print_only_loss("val", out);
if (learn_loss_sum.count() > 0)
{
learn_loss_sum.print("train", out);
learn_loss_sum.print_with_grad("train", out);
}
out << " - norm = " << sum_norm << endl;
@@ -880,7 +958,7 @@ namespace Learner
// Evaluation value of deep search
const auto deep_value = (Value)ps.score;
const auto loss = get_loss(
const auto loss = get_loss_no_grad(
deep_value,
shallow_value,
ps);