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[MT-TREE] Support prediction cache and model slicing. (#8968)

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- Fix prediction range.
- Support prediction cache in mt-hist.
- Support model slicing.
- Make the booster a Python iterable by defining `__iter__`.
- Cleanup removed/deprecated parameters.
- A new field in the output model `iteration_indptr` for pointing to the ranges of trees for each iteration.
This commit is contained in:
Jiaming Yuan
2023-03-27 23:10:54 +08:00
committed by GitHub
parent c2b3a13e70
commit acc110c251
30 changed files with 502 additions and 343 deletions
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12
include/xgboost/base.h
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@@ -113,8 +113,18 @@ using bst_row_t = std::size_t; // NOLINT
using bst_node_t = std::int32_t; // NOLINT
/*! \brief Type for ranking group index. */
using bst_group_t = std::uint32_t; // NOLINT
/*! \brief Type for indexing into output targets. */
/**
* \brief Type for indexing into output targets.
*/
using bst_target_t = std::uint32_t; // NOLINT
/**
* brief Type for indexing boosted layers.
*/
using bst_layer_t = std::int32_t; // NOLINT
/**
* \brief Type for indexing trees.
*/
using bst_tree_t = std::int32_t; // NOLINT
namespace detail {
/*! \brief Implementation of gradient statistics pair. Template specialisation

49
include/xgboost/gbm.h
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@@ -59,16 +59,16 @@ class GradientBooster : public Model, public Configurable {
* \param fo output stream
*/
virtual void Save(dmlc::Stream* fo) const = 0;
/*!
/**
* \brief Slice a model using boosting index. The slice m:n indicates taking all trees
* that were fit during the boosting rounds m, (m+1), (m+2), ..., (n-1).
* \param layer_begin Beginning of boosted tree layer used for prediction.
* \param layer_end End of booster layer. 0 means do not limit trees.
* \param out Output gradient booster
* \param begin Beginning of boosted tree layer used for prediction.
* \param end End of booster layer. 0 means do not limit trees.
* \param out Output gradient booster
*/
virtual void Slice(int32_t /*layer_begin*/, int32_t /*layer_end*/, int32_t /*step*/,
virtual void Slice(bst_layer_t /*begin*/, bst_layer_t /*end*/, bst_layer_t /*step*/,
GradientBooster* /*out*/, bool* /*out_of_bound*/) const {
LOG(FATAL) << "Slice is not supported by current booster.";
LOG(FATAL) << "Slice is not supported by the current booster.";
}
/*! \brief Return number of boosted rounds.
*/
@@ -88,34 +88,31 @@ class GradientBooster : public Model, public Configurable {
virtual void DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
PredictionCacheEntry*, ObjFunction const* obj) = 0;
/*!
* \brief generate predictions for given feature matrix
* \param dmat feature matrix
/**
* \brief Generate predictions for given feature matrix
*
* \param dmat The feature matrix.
* \param out_preds output vector to hold the predictions
* \param training Whether the prediction value is used for training. For dart booster
* drop out is performed during training.
* \param layer_begin Beginning of boosted tree layer used for prediction.
* \param layer_end End of booster layer. 0 means do not limit trees.
* \param begin Beginning of boosted tree layer used for prediction.
* \param end End of booster layer. 0 means do not limit trees.
*/
virtual void PredictBatch(DMatrix* dmat,
PredictionCacheEntry* out_preds,
bool training,
unsigned layer_begin,
unsigned layer_end) = 0;
virtual void PredictBatch(DMatrix* dmat, PredictionCacheEntry* out_preds, bool training,
bst_layer_t begin, bst_layer_t end) = 0;
/*!
/**
* \brief Inplace prediction.
*
* \param p_fmat A proxy DMatrix that contains the data and related
* meta info.
* \param missing Missing value in the data.
* \param [in,out] out_preds The output preds.
* \param layer_begin (Optional) Beginning of boosted tree layer used for prediction.
* \param layer_end (Optional) End of booster layer. 0 means do not limit trees.
* \param p_fmat A proxy DMatrix that contains the data and related.
* \param missing Missing value in the data.
* \param [in,out] out_preds The output preds.
* \param begin (Optional) Beginning of boosted tree layer used for prediction.
* \param end (Optional) End of booster layer. 0 means do not limit trees.
*/
virtual void InplacePredict(std::shared_ptr<DMatrix>, float, PredictionCacheEntry*, uint32_t,
uint32_t) const {
LOG(FATAL) << "Inplace predict is not supported by current booster.";
virtual void InplacePredict(std::shared_ptr<DMatrix>, float, PredictionCacheEntry*, bst_layer_t,
bst_layer_t) const {
LOG(FATAL) << "Inplace predict is not supported by the current booster.";
}
/*!
* \brief online prediction function, predict score for one instance at a time

8
include/xgboost/learner.h
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@@ -9,7 +9,7 @@
#define XGBOOST_LEARNER_H_
#include <dmlc/io.h> // for Serializable
#include <xgboost/base.h> // for bst_feature_t, bst_target_t, bst_float, Args, GradientPair
#include <xgboost/base.h> // for bst_feature_t, bst_target_t, bst_float, Args, GradientPair, ..
#include <xgboost/context.h> // for Context
#include <xgboost/linalg.h> // for Tensor, TensorView
#include <xgboost/metric.h> // for Metric
@@ -229,7 +229,7 @@ class Learner : public Model, public Configurable, public dmlc::Serializable {
*/
virtual void GetFeatureTypes(std::vector<std::string>* ft) const = 0;
/*!
/**
* \brief Slice the model.
*
* See InplacePredict for layer parameters.
@@ -239,8 +239,8 @@ class Learner : public Model, public Configurable, public dmlc::Serializable {
*
* \return a sliced model.
*/
virtual Learner *Slice(int32_t begin_layer, int32_t end_layer, int32_t step,
bool *out_of_bound) = 0;
virtual Learner* Slice(bst_layer_t begin, bst_layer_t end, bst_layer_t step,
bool* out_of_bound) = 0;
/*!
* \brief dump the model in the requested format
* \param fmap feature map that may help give interpretations of feature

4
include/xgboost/tree_updater.h
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@@ -85,8 +85,8 @@ class TreeUpdater : public Configurable {
* the prediction cache. If true, the prediction cache will have been
* updated by the time this function returns.
*/
virtual bool UpdatePredictionCache(const DMatrix * /*data*/,
linalg::VectorView<float> /*out_preds*/) {
virtual bool UpdatePredictionCache(const DMatrix* /*data*/,
linalg::MatrixView<float> /*out_preds*/) {
return false;
}