This PR prepares the GHistIndexMatrix to host the column matrix which is used by the hist tree method by accepting sparse_threshold parameter.
Some cleanups are made to ensure the correct batch param is being passed into DMatrix along with some additional tests for correctness of SimpleDMatrix.
This PR rewrites the approx tree method to use codebase from hist for better performance and code sharing.
The rewrite has many benefits:
- Support for both `max_leaves` and `max_depth`.
- Support for `grow_policy`.
- Support for mono constraint.
- Support for feature weights.
- Support for easier bin configuration (`max_bin`).
- Support for categorical data.
- Faster performance for most of the datasets. (many times faster)
- Support for prediction cache.
- Significantly better performance for external memory.
- Unites the code base between approx and hist.
* Support more input types for categorical data.
* Shorten the type name from "categorical" to "c".
* Tests for np/cp array and scipy csr/csc/coo.
* Specify the type for feature info.
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>
- 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.
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.
* Removed some warnings
* Rebase with master
* Solved C++ Google Tests errors made by refactoring in order to remove warnings
* Undo renaming path -> path_
* Fix style check
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* fixed some endian issues
* Use dmlc::ByteSwap() to simplify code
* Fix lint check
* [CI] Add test for s390x
* Download latest CMake on s390x
* Fix a bug in my code
* Save magic number in dmatrix with byteswap on big-endian machine
* Save version in binary with byteswap on big-endian machine
* Load scalar with byteswap in MetaInfo
* Add a debugging message
* Handle arrays correctly when byteswapping
* EOF can also be 255
* Handle magic number in MetaInfo carefully
* Skip Tree.Load test for big-endian, since the test manually builds little-endian binary model
* Handle missing packages in Python tests
* Don't use boto3 in model compatibility tests
* Add s390 Docker file for local testing
* Add model compatibility tests
* Add R compatibility test
* Revert "Add R compatibility test"
This reverts commit c2d2bdcb7dbae133cbb927fcd20f7e83ee2b18a8.
Co-authored-by: Qi Zhang <q.zhang@ibm.com>
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* [CI] Add RMM as an optional dependency
* Replace caching allocator with pool allocator from RMM
* Revert "Replace caching allocator with pool allocator from RMM"
This reverts commit e15845d4e72e890c2babe31a988b26503a7d9038.
* Use rmm::mr::get_default_resource()
* Try setting default resource (doesn't work yet)
* Allocate pool_mr in the heap
* Prevent leaking pool_mr handle
* Separate EXPECT_DEATH() in separate test suite suffixed DeathTest
* Turn off death tests for RMM
* Address reviewer's feedback
* Prevent leaking of cuda_mr
* Fix Jenkinsfile syntax
* Remove unnecessary function in Jenkinsfile
* [CI] Install NCCL into RMM container
* Run Python tests
* Try building with RMM, CUDA 10.0
* Do not use RMM for CUDA 10.0 target
* Actually test for test_rmm flag
* Fix TestPythonGPU
* Use CNMeM allocator, since pool allocator doesn't yet support multiGPU
* Use 10.0 container to build RMM-enabled XGBoost
* Revert "Use 10.0 container to build RMM-enabled XGBoost"
This reverts commit 789021fa31112e25b683aef39fff375403060141.
* Fix Jenkinsfile
* [CI] Assign larger /dev/shm to NCCL
* Use 10.2 artifact to run multi-GPU Python tests
* Add CUDA 10.0 -> 11.0 cross-version test; remove CUDA 10.0 target
* Rename Conda env rmm_test -> gpu_test
* Use env var to opt into CNMeM pool for C++ tests
* Use identical CUDA version for RMM builds and tests
* Use Pytest fixtures to enable RMM pool in Python tests
* Move RMM to plugin/CMakeLists.txt; use PLUGIN_RMM
* Use per-device MR; use command arg in gtest
* Set CMake prefix path to use Conda env
* Use 0.15 nightly version of RMM
* Remove unnecessary header
* Fix a unit test when cudf is missing
* Add RMM demos
* Remove print()
* Use HostDeviceVector in GPU predictor
* Simplify pytest setup; use LocalCUDACluster fixture
* Address reviewers' commments
Co-authored-by: Hyunsu Cho <chohyu01@cs.wasshington.edu>
