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Refactor configuration [Part II]. (#4577)

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* Refactor configuration [Part II].

* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
   parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.

* Learner changes:
** Make `LearnerImpl` the only source of configuration.

    All configurations are stored and carried out by `LearnerImpl::Configure()`.

** Remove booster in C API.

    Originally kept for "compatibility reason", but did not state why.  So here
    we just remove it.

** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`.  Configuration will always be lazy.
** Run `Configure` before every iteration.

* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.

    `GBTree` is now used to dispatch the predictor.

** Remove some GPU Predictor tests.

* IO

No IO changes.  The binary model format stability is tested by comparing
hashing value of save models between two commits
This commit is contained in:
Jiaming Yuan
2019-07-20 08:34:56 -04:00
committed by GitHub
parent ad1192e8a3
commit f0064c07ab
69 changed files with 669 additions and 761 deletions
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24
src/tree/updater_gpu_hist.cu
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@@ -1290,13 +1290,12 @@ template <typename GradientSumT>
class GPUHistMakerSpecialised {
public:
GPUHistMakerSpecialised() : initialised_{false}, p_last_fmat_{nullptr} {}
void Init(const std::vector<std::pair<std::string, std::string>>& args,
LearnerTrainParam const* lparam) {
void Configure(const Args& args, GenericParameter const* generic_param) {
param_.InitAllowUnknown(args);
learner_param_ = lparam;
generic_param_ = generic_param;
hist_maker_param_.InitAllowUnknown(args);
auto devices = GPUSet::All(learner_param_->gpu_id,
learner_param_->n_gpus);
auto devices = GPUSet::All(generic_param_->gpu_id,
generic_param_->n_gpus);
n_devices_ = devices.Size();
CHECK(n_devices_ != 0) << "Must have at least one device";
dist_ = GPUDistribution::Block(devices);
@@ -1362,7 +1361,7 @@ class GPUHistMakerSpecialised {
monitor_.StartCuda("Quantiles");
// Create the quantile sketches for the dmatrix and initialize HistogramCuts
size_t row_stride = common::DeviceSketch(param_, *learner_param_,
size_t row_stride = common::DeviceSketch(param_, *generic_param_,
hist_maker_param_.gpu_batch_nrows,
dmat, &hmat_);
monitor_.StopCuda("Quantiles");
@@ -1488,7 +1487,7 @@ class GPUHistMakerSpecialised {
int n_bins_;
GPUHistMakerTrainParam hist_maker_param_;
LearnerTrainParam const* learner_param_;
GenericParameter const* generic_param_;
dh::AllReducer reducer_;
@@ -1502,17 +1501,16 @@ class GPUHistMakerSpecialised {
class GPUHistMaker : public TreeUpdater {
public:
void Init(
const std::vector<std::pair<std::string, std::string>>& args) override {
void Configure(const Args& args) override {
hist_maker_param_.InitAllowUnknown(args);
float_maker_.reset();
double_maker_.reset();
if (hist_maker_param_.single_precision_histogram) {
float_maker_.reset(new GPUHistMakerSpecialised<GradientPair>());
float_maker_->Init(args, tparam_);
float_maker_->Configure(args, tparam_);
} else {
double_maker_.reset(new GPUHistMakerSpecialised<GradientPairPrecise>());
double_maker_->Init(args, tparam_);
double_maker_->Configure(args, tparam_);
}
}
@@ -1534,6 +1532,10 @@ class GPUHistMaker : public TreeUpdater {
}
}
char const* Name() const override {
return "gpu_hist";
}
private:
GPUHistMakerTrainParam hist_maker_param_;
std::unique_ptr<GPUHistMakerSpecialised<GradientPair>> float_maker_;