* add set_stream
* add .record_stream for NDArray and HeteroGraph
* refactor dgl stream Python APIs
* test record_stream
* add unit test for record stream
* use pytorch's stream
* fix lint
* fix cpu build
* address comments
* address comments
* add record stream tests for dgl.graph
* record frames and update dataloder
* add docstring
* update frame
* add backend check for record_stream
* remove CUDAThreadEntry::stream
* record stream for newly created formats
* fix bug
* fix cpp test
* fix None c_void_p to c_handle
* * Added "exclude_self" and "output_batch" options to knn_graph and segmented_knn_graph
* Updated out-of-date comments on remove_edges and remove_self_loop, since they now preserve batch information
* * Changed defaults on new knn_graph and segmented_knn_graph function parameters, for compatibility; pytorch/test_geometry.py was failing
* * Added test to ensure dgl.remove_self_loop function correctly updates batch information
* * Added new knn_graph and segmented_knn_graph parameters to dgl.nn.KNNGraph and dgl.nn.SegmentedKNNGraph
* * Formatting
* * Oops, I missed the one in segmented_knn_graph when I fixed the similar thing in knn_graph
* * Fixed edge case handling when invalid k specified, since it still needs to be handled consistently for tests to pass
* Fixed context of batch info, since it must match the context of the input position data for remove_self_loop to succeed
* * Fixed batch info resulting from knn_graph when output_batch is true, for case of 3D input tensor, representing multiple segments
* * Added testing of new exclude_self and output_batch parameters on knn_graph and segmented_knn_graph, and their wrappers, KNNGraph and SegmentedKNNGraph, into the test_knn_cuda test
* * Added doc comments for new parameters
* * Added correct handling for uncommon case of k or more coincident points when excluding self edges in knn_graph and segmented_knn_graph
* Added test cases for more than k coincident points
* * Updated doc comments for output_batch parameters for clarity
* * Linter formatting fixes
* * Extracted out common function for test_knn_cpu and test_knn_cuda, to add the new test cases to test_knn_cpu
* * Rewording in doc comments
* * Removed output_batch parameter from knn_graph and segmented_knn_graph, in favour of always setting the batch information, except in knn_graph if x is a 2D tensor
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* Use an internal cuda stream for CopyDataFromTo
* small fix white space
* Fix to compile
* Make stream optional in copydata for compile
* fix lint issue
* Update cub functions to use internal stream
* Lint check
* Update CopyTo/CopyFrom/CopyFromTo to use internal stream
* Address comments
* Fix backward CUDA stream
* Avoid overloading CopyFromTo()
* Minor comment update
* Overload copydatafromto in cuda device api
Co-authored-by: xiny <xiny@nvidia.com>
* Allocate tensors in DGL's current stream
* make tensoradaptor stream-aware
* replace TAemtpy with cpu allocator
* fix typo
* try fix cpu allocation
* clean header
* redirect AllocDataSpace as well
* resolve comments
* Add helper method for temporary affinitization of compute threads
* Rework DL affinitization as single helper
* Add example usage in benchmarks
* Fix python linter warnings
* Fix affinity helper params
* Use NUMA node 0 cores only by default
* Fix benchmarks
* Fix lint errors
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
* Distributed Lookup service which is for retrieving global_nids to shuffle-global-nids/partition-id mappings
1. Implemented a class to provide distributed lookup service
2. This class can be used to retrieve global-nids mappings
* Code changes to address CI comments.
1. Removed some unneeded type_casts to numpy.int64
2. Added additional comments when iterating over the partition-ids list.
3.Added docstring to the class and adjusted comments where it is relevant.
* Updated code comments and variable names...
1. Changed the variable names to appropriately represent the values stored in these variables.
2. Updated the docstring correctly.
* Corrected docstring as per the suggestion... and removed all the capital letters for Global nids and Shuffle Global nids...
* Addressing CI review comments.
* NGNN for ogbl
* modify doc organization.
* merge similar parts
* 1st approving review.
* minor changes
* Remove the "Usage" section.
Co-authored-by: Mufei Li <mufeili1996@gmail.com>
* code changes for bug fixes identified during mag_lsc dataset (#4187)
* code changes for bug fixes identified during mag_lsc dataset
1. Changed from call torch.Tensor() to torch.from_numpy() to address memory corruption issues when creating large tensors. Tricky thing is this works correctly for small tensors.
2. Changed dgl.graph() function call to include 'num_nodes" argument to specifically mention all the nodes in a graph partition.
* Update convert_partition.py
Moving the changes to the function "create_metadata_json" function to the "multiple-file-format" support, where this change is more appropriate. Since multiple machine testing was done with these code changes.
* Addressing review comments.
Removed space as suggested at the end of the line
* Revert "Revert "[Distributed Training Pipeline] Initial implementation of Distributed data processing step in the Dis… (#3926)" (#4037)"
This reverts commit 7c598aac6c.
* Added code to support multiple-file-support feature and removed singl… (#4188)
* Added code to support multiple-file-support feature and removed single-file-support code
1. Added code to read dataset in multiple-file-format
2. Removed code for single-file format
* added files missing in the previous commit
This commit includes dataset_utils.py, which reads the dataset in multiple-file-format, gloo_wrapper function calls to support exchanging dictionaries as objects and helper functions in utils.py
* Update convert_partition.py
Updated function call "create_metadata_json" file to include partition_id so that each rank only creates its own metadata object and later on these are accumulated on rank-0 to create graph-level metadata json file.
* addressing code review comments during the CI process
code changes resulting from the code review comments received during the CI process.
* Code reorganization
Addressing CI comments and code reorganization for easier understanding.
* Removed commented out line
removed commented out line.
* Support new format for multi-file support in distributed partitioning. (#4217)
* Code changes for the following
1. Generating node data at each process
2. Reading csv files using pyarrow
3. feature complete code.
* Removed some typo's because of which unit tests were failing
1. Change the file name to correct file name when loading edges from file
2. When storing node-features after shuffling, use the correct key to store the global-nids of node features which are received after transmitted.
* Code changes to address CI comments by reviewers
1. Removed some redundant code and added text in the doc-strings to describe the functionality of some functions.
2 function signatures and invocations now match w.r.t argument list
3. Added detailed description of the metadata json structure so that the users understand the the type of information present in this file and how it is used through out the code.
* Addressing code review comments
1. Addressed all the CI comments and some of the changes include simplifying the code related to the concatenation of lists and enhancing the docstrings of functions which are changed in this process.
* Update docstring's of two functions appropriately in response to code review comments
Removed "todo" from the docstring of the gen_nodedata function.
Added "todo" to the gen_dist_partitions function when node-id to partition-id's are read for the first time.
Removed 'num-node-weights' from the docstring for the get_dataset function and added schema_map docstring to the argument list.
* [Distributed] Change for the new input format for distributed partitioning (#4273)
* Code changes to address the updated file format support for massively large graphs.
1. Updated the docstring for the starting function 'gen_dist_partitions" to describe the newly proposed file format for input dataset.
2. Code which was dependent on the structure of the old-metadata json object has been updated to read from the newly proposed metadata file.
3. Fixed some errors when appropriate functions were invoked and the calling function expects return values from the invoked furnction.
4. This modified code has been tested on "mag" dataset using 4-way partitions and verified the results
* Code changes to address the CI review comments
1. Improved docstrings for some functions.
2. Added a new function in the utils.py to compute the id ranges and this is used in multiple places.
* Added TODO to indicate the redundant data structure.
Because of the new file format changes, one of the dictionaries (node_feature_tids, node_tids) will be redundant. Added TODO text so that this will be removed in the next iteration of code changes.
* [Distributed] use alltoall fix to bypass gloo - alltoallv bug in distributed partitioning (#4311)
* Alltoall Fix to bypass gloo - alltoallv bug which is preventing further testing
1. Replaced alltoallv gloo wrapper call with alltoall message.
2. All the messages are padded to be of same length
3. Receiving side unpads the messages and continues processing.
* Code changes to address CI comments
1. Removed unused functions from gloo_wrapper.py
2. Changed the function signature of alltoallv_cpu_data as suggested.
3. Added docstring to include more description of the functionality inside alltoallv_cpu_data. Included more asserts to validate the assumptions.
* Changed the function name appropriately
Changed the function name from "alltoallv_cpu_data" to alltoallv_cpu which I believe is appropriate because underlying functionality is providing alltoallv which is basically alltoall_cpu + padding
* Added code and text to address the review comments.
1. Changed the function name to indicate the local use of this function.
2. Changed docstring to indicate the assumptions made by alltoallv_cpu function.
* Removed unused function from import statement
Removed unused/removed function from import statement.
* [Distributed] reduce memory consumption in distributed graph partitioning. (#4338)
* Fix for node_subgraph function, which seems to generate segmentation fault for very large partitions
1. Removed three graph dgl objects and we create the final dgl object directly by maintaining the following constraints
a) nodes are reordered so that local nodes are placed in the beginning of the nodes list compared to non-local nodes.
b)Edges order are maintained as passed into this function.
c) src/dst end points are mapped to target values based on the reshuffle'd nodes order.
* Code changes addressing CI comments for this PR
1. Used Da's suggested map to map nodes from old to new order.
This is much simpler and mem. efficient.
* Addressing CI Comments.
1. Reduced the amount of documentation to reflect the actual implementation.
2. named the mapping object appropriately.
* [Distributed] Graph chunking UX (#4365)
* first commit
* update
* huh
* fix
* update
* revert core
* fix
* update
* rewrite
* oops
* address comments
* add graph name
* address comments
* remove sample metadata file
* address comments
* fix
* remove
* add docs
* Adding launch script and wrapper script to trigger distributed graph … (#4276)
* Adding launch script and wrapper script to trigger distributed graph partitioning pipeline as defined in the UX document
1. dispatch_data.py is a wrapper script which builds the command and triggers the distributed partitioning pipeline
2. distgraphlaunch.py is the main python script which triggers the pipeline and to simplify its usage dispatch_data.py is included as a wrapper script around it.
* Added code to auto-detect python version and retrieve some parameters from the input metadata json file
1. Auto detect python version
2. Read the metadata json file and extract some parameters to pass to the user defined command which is used to trigger the pipeline.
* Updated the json file name to metadata.json file per UX documentation
1. Renamed json file name per UX documentation.
* address comments
* fix
* fix doc
* use unbuffered logging to cure anxiety
* cure more anxiety
* Update tools/dispatch_data.py
Co-authored-by: Minjie Wang <minjie.wang@nyu.edu>
* oops
Co-authored-by: Quan Gan <coin2028@hotmail.com>
Co-authored-by: Minjie Wang <minjie.wang@nyu.edu>
Co-authored-by: kylasa <kylasa@gmail.com>
Co-authored-by: Da Zheng <zhengda1936@gmail.com>
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
Quan Gan