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Update GPUTreeShap, add docs (#6281)

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* Update GPUTreeShap, add docs

* Fix test

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Rory Mitchell 2020-10-27 18:22:12 +13:00 committed by GitHub
parent b180223d18
commit f0c3ff313f
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5 changed files with 254 additions and 28 deletions
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2
demo/gpu_acceleration/README.md
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@ -1,3 +1,5 @@
# GPU Acceleration Demo
`cover_type.py` shows how to train a model on the [forest cover type](https://archive.ics.uci.edu/ml/datasets/covertype) dataset using GPU acceleration. The forest cover type dataset has 581,012 rows and 54 features, making it time consuming to process. We compare the run-time and accuracy of the GPU and CPU histogram algorithms.
`shap.ipynb` demonstrates using GPU acceleration to compute SHAP values for feature importance.

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demo/gpu_acceleration/shap.ipynb Normal file
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doc/gpu/index.rst
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@ -85,6 +85,19 @@ The GPU algorithms currently work with CLI, Python and R packages. See :doc:`/bu
XGBRegressor(tree_method='gpu_hist', gpu_id=0)
GPU-Accelerated SHAP values
=============================
XGBoost makes use of `GPUTreeShap <https://github.com/rapidsai/gputreeshap>`_ as a backend for computing shap values when the GPU predictor is selected.
.. code-block:: python
model.set_param({"predictor": "gpu_predictor"})
shap_values = model.predict(dtrain, pred_contribs=True)
shap_interaction_values = model.predict(dtrain, pred_interactions=True)
See examples `here
<https://github.com/dmlc/xgboost/tree/master/demo/gpu_acceleration>`_.
Multi-node Multi-GPU Training
=============================
XGBoost supports fully distributed GPU training using `Dask <https://dask.org/>`_. For

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gputreeshap

@ -1 +1 @@
Subproject commit 5f33132d75482338f78cfba562791d8445e157f6
Subproject commit 3310a30bb123a49ab12c58e03edc2479512d2f64

52
src/predictor/gpu_predictor.cu
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@ -671,17 +671,6 @@ class GPUPredictor : public xgboost::Predictor {
model.learner_model_param->num_output_group);
out_contribs->Fill(0.0f);
auto phis = out_contribs->DeviceSpan();
p_fmat->Info().base_margin_.SetDevice(generic_param_->gpu_id);
const auto margin = p_fmat->Info().base_margin_.ConstDeviceSpan();
float base_score = model.learner_model_param->base_score;
// Add the base margin term to last column
dh::LaunchN(
generic_param_->gpu_id,
p_fmat->Info().num_row_ * model.learner_model_param->num_output_group,
[=] __device__(size_t idx) {
phis[(idx + 1) * contributions_columns - 1] =
margin.empty() ? base_score : margin[idx];
});
dh::device_vector<gpu_treeshap::PathElement> device_paths;
ExtractPaths(&device_paths, model, real_ntree_limit,
@ -695,6 +684,17 @@ class GPUPredictor : public xgboost::Predictor {
X, device_paths.begin(), device_paths.end(), ngroup,
phis.data() + batch.base_rowid * contributions_columns, phis.size());
}
// Add the base margin term to last column
p_fmat->Info().base_margin_.SetDevice(generic_param_->gpu_id);
const auto margin = p_fmat->Info().base_margin_.ConstDeviceSpan();
float base_score = model.learner_model_param->base_score;
dh::LaunchN(
generic_param_->gpu_id,
p_fmat->Info().num_row_ * model.learner_model_param->num_output_group,
[=] __device__(size_t idx) {
phis[(idx + 1) * contributions_columns - 1] +=
margin.empty() ? base_score : margin[idx];
});
}
void PredictInteractionContributions(DMatrix* p_fmat,
@ -726,21 +726,6 @@ class GPUPredictor : public xgboost::Predictor {
model.learner_model_param->num_output_group);
out_contribs->Fill(0.0f);
auto phis = out_contribs->DeviceSpan();
p_fmat->Info().base_margin_.SetDevice(generic_param_->gpu_id);
const auto margin = p_fmat->Info().base_margin_.ConstDeviceSpan();
float base_score = model.learner_model_param->base_score;
// Add the base margin term to last column
size_t n_features = model.learner_model_param->num_feature;
dh::LaunchN(
generic_param_->gpu_id,
p_fmat->Info().num_row_ * model.learner_model_param->num_output_group,
[=] __device__(size_t idx) {
size_t group = idx % ngroup;
size_t row_idx = idx / ngroup;
phis[gpu_treeshap::IndexPhiInteractions(
row_idx, ngroup, group, n_features, n_features, n_features)] =
margin.empty() ? base_score : margin[idx];
});
dh::device_vector<gpu_treeshap::PathElement> device_paths;
ExtractPaths(&device_paths, model, real_ntree_limit,
@ -754,6 +739,21 @@ class GPUPredictor : public xgboost::Predictor {
X, device_paths.begin(), device_paths.end(), ngroup,
phis.data() + batch.base_rowid * contributions_columns, phis.size());
}
// Add the base margin term to last column
p_fmat->Info().base_margin_.SetDevice(generic_param_->gpu_id);
const auto margin = p_fmat->Info().base_margin_.ConstDeviceSpan();
float base_score = model.learner_model_param->base_score;
size_t n_features = model.learner_model_param->num_feature;
dh::LaunchN(
generic_param_->gpu_id,
p_fmat->Info().num_row_ * model.learner_model_param->num_output_group,
[=] __device__(size_t idx) {
size_t group = idx % ngroup;
size_t row_idx = idx / ngroup;
phis[gpu_treeshap::IndexPhiInteractions(
row_idx, ngroup, group, n_features, n_features, n_features)] +=
margin.empty() ? base_score : margin[idx];
});
}
protected: