Add Configuration for ParameterConstraints in SparseNN Benchmark #3075
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…ugh GPUs (pytorch#3068) Summary: 1) Add comprehensive docstrings to RunOptions, EmbeddingTablesConfig, and PipelineConfig. 2) Replace direct return with hypothesis.assume(torch.cuda.is_available() and torch.cuda.device_count() >= world_size) Reviewed By: TroyGarden, aliafzal Differential Revision: D76160331
Summary: Created an EmbeddingShardingPlanner in the runner function after generating the unsharded model and modified _generate_sharded_model_and_optimizer to accept and use this planner. This change enables optimized sharding of embedding tables based on the topology. Differential Revision: D76188112
…rch#3070) Summary: This change adds the ability to select different sharding planners when running sparse neural network benchmarks. Previously, the benchmark code only used EmbeddingShardingPlanner, but now users can choose between EmbeddingShardingPlanner and LinearProgrammingPlanner. The changes include: 1. Adding a new `planner_type` parameter to the `RunOptions` class 2. Creating a new `_generate_planner` function that returns the appropriate planner based on the selected type 3. Updating the runner function to use this new function 4. Updating type annotations to support both planner types Differential Revision: D76231527
Summary: This commit introduces configurable `ParameterConstraints` for embedding tables in the SparseNN benchmark. Users can now specify sharding strategies (e.g., table-wise, row-wise, column-wise, data_parallel) and select compute kernel types (e.g., dense, fused, quant) for embedding tables. Differential Revision: D76310319
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Summary: This commit introduces configurable
ParameterConstraintsfor embedding tables in the SparseNN benchmark. Users can now specify sharding strategies (e.g., table-wise, row-wise, column-wise, data_parallel) and select compute kernel types (e.g., dense, fused, quant) for embedding tables.Differential Revision: D76310319