On GPU we use rouding factor to truncate the gradient for deterministic results. This PR changes the gradient representation to fixed point number with exponent aligned with rounding factor.
[breaking] Drop non-deterministic histogram.
Use fixed point for shared memory.
This PR is to improve the performance of GPU Hist.
Co-authored-by: Andy Adinets <aadinets@nvidia.com>
* Work around a segfault observed in SparsePage::Push()
* Revert "Work around a segfault observed in SparsePage::Push()"
This reverts commit 30934844d00908750a5442082eb4769b1489f6a9.
* Don't call vector::resize() inside OpenMP block
* Set GITHUB_PAT env var to fix R tests
* Use built-in GITHUB_TOKEN
* Use type aliases for discard iterators
* update to include host_vector as thrust 1.12 doesn't bring it in as a side-effect
* cub::DispatchRadixSort requires signed offset types
- Reduce dependency on dmlc parsers and provide an interface for users to load data by themselves.
- Remove use of threaded iterator and IO queue.
- Remove `page_size`.
- Make sure the number of pages in memory is bounded.
- Make sure the cache can not be violated.
- Provide an interface for internal algorithms to process data asynchronously.
The role of ProxyDMatrix is going beyond what it was designed. Now it's used by both
QuantileDeviceDMatrix and inplace prediction. After the refactoring of sparse DMatrix it
will also be used for external memory. Renaming the C API to extract it from
QuantileDeviceDMatrix.
Other than modularizing the split evaluation function, this PR also removes some more functions including `InitNewNodes` and `BuildNodeStats` among some other unused variables. Also, scattered code like setting leaf weights is grouped into the split evaluator and `NodeEntry` is simplified and made private. Another subtle difference with the original implementation is that the modified code doesn't call `tree[nidx].Parent()` to traversal upward.
* Add feature score support for linear model.
* Port R interface to the new implementation.
* Add linear model support in Python.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Support categorical data for dask functional interface and DQM.
* Implement categorical data support for GPU GK-merge.
* Add support for dask functional interface.
* Add support for DQM.
* Get newer cupy.
* Categorical prediction with CPU predictor and GPU predict leaf.
* Implement categorical prediction for CPU prediction.
* Implement categorical prediction for GPU predict leaf.
* Refactor the prediction functions to have a unified get next node function.
Co-authored-by: Shvets Kirill <kirill.shvets@intel.com>
* Change C API name.
* Test for all primitive types from array.
* Add native support for CPU 128 float.
* Convert boolean and float16 in Python.
* Fix dask version for now.
Jiaming Yuan
Philip Hyunsu Cho
Robert Maynard
Taewoo KimandTaewoo Kim
ShvetsKSandSHVETS, KIRILL
farfarawayzyt
TP Boudreau