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Pass pointer to model parameters. (#5101)

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* Pass pointer to model parameters.

This PR de-duplicates most of the model parameters except the one in
`tree_model.h`.  One difficulty is `base_score` is a model property but can be
changed at runtime by objective function.  Hence when performing model IO, we
need to save the one provided by users, instead of the one transformed by
objective.  Here we created an immutable version of `LearnerModelParam` that
represents the value of model parameter after configuration.
This commit is contained in:
Jiaming Yuan
2019-12-10 12:11:22 +08:00
committed by GitHub
parent 979f74d51a
commit e089e16e3d
33 changed files with 623 additions and 404 deletions
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25
src/linear/updater_gpu_coordinate.cu
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@@ -41,7 +41,7 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
monitor_.Init("GPUCoordinateUpdater");
}
void LazyInitDevice(DMatrix *p_fmat, const gbm::GBLinearModelParam &model_param) {
void LazyInitDevice(DMatrix *p_fmat, const LearnerModelParam &model_param) {
if (learner_param_->gpu_id < 0) return;
num_row_ = static_cast<size_t>(p_fmat->Info().num_row_);
@@ -88,14 +88,14 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
gbm::GBLinearModel *model, double sum_instance_weight) override {
tparam_.DenormalizePenalties(sum_instance_weight);
monitor_.Start("LazyInitDevice");
this->LazyInitDevice(p_fmat, model->param);
this->LazyInitDevice(p_fmat, *(model->learner_model_param_));
monitor_.Stop("LazyInitDevice");
monitor_.Start("UpdateGpair");
auto &in_gpair_host = in_gpair->ConstHostVector();
// Update gpair
if (learner_param_->gpu_id >= 0) {
this->UpdateGpair(in_gpair_host, model->param);
this->UpdateGpair(in_gpair_host);
}
monitor_.Stop("UpdateGpair");
@@ -107,8 +107,9 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm,
coord_param_.top_k);
monitor_.Start("UpdateFeature");
for (auto group_idx = 0; group_idx < model->param.num_output_group; ++group_idx) {
for (auto i = 0U; i < model->param.num_feature; i++) {
for (auto group_idx = 0; group_idx < model->learner_model_param_->num_output_group;
++group_idx) {
for (auto i = 0U; i < model->learner_model_param_->num_feature; i++) {
auto fidx = selector_->NextFeature(
i, *model, group_idx, in_gpair->ConstHostVector(), p_fmat,
tparam_.reg_alpha_denorm, tparam_.reg_lambda_denorm);
@@ -120,11 +121,12 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
}
void UpdateBias(DMatrix *p_fmat, gbm::GBLinearModel *model) {
for (int group_idx = 0; group_idx < model->param.num_output_group; ++group_idx) {
for (int group_idx = 0; group_idx < model->learner_model_param_->num_output_group;
++group_idx) {
// Get gradient
auto grad = GradientPair(0, 0);
if (learner_param_->gpu_id >= 0) {
grad = GetBiasGradient(group_idx, model->param.num_output_group);
grad = GetBiasGradient(group_idx, model->learner_model_param_->num_output_group);
}
auto dbias = static_cast<float>(
tparam_.learning_rate *
@@ -133,7 +135,7 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
// Update residual
if (learner_param_->gpu_id >= 0) {
UpdateBiasResidual(dbias, group_idx, model->param.num_output_group);
UpdateBiasResidual(dbias, group_idx, model->learner_model_param_->num_output_group);
}
}
}
@@ -145,7 +147,7 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
// Get gradient
auto grad = GradientPair(0, 0);
if (learner_param_->gpu_id >= 0) {
grad = GetGradient(group_idx, model->param.num_output_group, fidx);
grad = GetGradient(group_idx, model->learner_model_param_->num_output_group, fidx);
}
auto dw = static_cast<float>(tparam_.learning_rate *
CoordinateDelta(grad.GetGrad(), grad.GetHess(),
@@ -154,7 +156,7 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
w += dw;
if (learner_param_->gpu_id >= 0) {
UpdateResidual(dw, group_idx, model->param.num_output_group, fidx);
UpdateResidual(dw, group_idx, model->learner_model_param_->num_output_group, fidx);
}
}
@@ -217,8 +219,7 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
return num_row_ == 0;
}
void UpdateGpair(const std::vector<GradientPair> &host_gpair,
const gbm::GBLinearModelParam &model_param) {
void UpdateGpair(const std::vector<GradientPair> &host_gpair) {
dh::safe_cuda(cudaMemcpyAsync(
gpair_.data(),
host_gpair.data(),