# PyTorch Data Benchmarks

This directory contains benchmarks for the `torch.utils.data` module components, focusing on the performance of samplers.

## Dependencies

The benchmarks require the following dependencies:
```
numpy
tabulate
```

You can install them using pip:
```bash
pip install numpy tabulate
```

## Running the benchmarks

To run the BatchSampler benchmark:
```bash
python samplers_benchmark.py
```

## Sampler Benchmark

The `samplers_benchmark.py` script benchmarks the performance of PyTorch's BatchSampler against an alternative implementation as an example. It tests with the following parameters:

- Batch sizes: 4, 8, 64, 640, 6400, 64000
- Drop last options: True, False
- Each configuration is run 10 times and averaged
- Results include speedup percentage calculations

### Output

The benchmark outputs a table with the following columns:
- Batch Size
- Drop Last
- Original (s): Time taken by the original implementation
- New (s): Time taken by the alternative implementation
- Speedup: Percentage improvement of the new implementation over the original

Example output:
```
+------------+-----------+---------------+----------+---------+
| Batch Size | Drop Last | Original (s)  | New (s)  | Speedup |
+============+===========+===============+==========+=========+
|          4 | True      | 0.1234        | 0.1000   | 18.96%  |
+------------+-----------+---------------+----------+---------+
|          4 | False     | 0.1345        | 0.1100   | 18.22%  |
+------------+-----------+---------------+----------+---------+
...
```

### Extending the Benchmark

To benchmark a different implementation:

On local:
1. Modify the `NewBatchSampler` class in `samplers_benchmark.py` with your implementation. Similarly replace `BatchSampler` with the corresponding PyTorch implementation.
    * Ensure to include all inputs like `replacement` for `RandomSampler` and its variations
2. Run the benchmark to compare its performance against the original