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Divyansh Khanna e6d8ed02cb PyTorch Data Sampler benchmark (#156974 )

## Motivation
Many PRs optimizing samplers (for eg https://github.com/pytorch/pytorch/pull/147706, https://github.com/pytorch/pytorch/pull/137423) are leveraging an adhoc script for benchmarking samplers. The script and outputs are often copied over in PRs. We want to begin centralizing benchmarks for torch.utils.data components.

## What ?
* This PR adds a new sub-folder in `benchmarks`  for `data`. This is aimed to cover benchmarking scripts for torch.utils.data components like dataloader and sampler.
* Specifically, this PR includes a simple script to time samplers. This is often "copy-pasted" in PRs optimizing samplers. Having it in a centralized location should prevent that, and allow a common standard.

## Output
```
Benchmark Results:
+--------------+-------------+----------------+-----------+-----------+
|   Batch Size | Drop Last   |   Original (s) |   New (s) | Speedup   |
+==============+=============+================+===========+===========+
|            4 | True        |         0.004  |    0.0088 | -119.62%  |
+--------------+-------------+----------------+-----------+-----------+
|            4 | False       |         0.0083 |    0.009  | -9.23%    |
+--------------+-------------+----------------+-----------+-----------+
|            8 | True        |         0.003  |    0.0074 | -147.64%  |
+--------------+-------------+----------------+-----------+-----------+
|            8 | False       |         0.0054 |    0.0075 | -38.72%   |
+--------------+-------------+----------------+-----------+-----------+
|           64 | True        |         0.0021 |    0.0056 | -161.92%  |
+--------------+-------------+----------------+-----------+-----------+
|           64 | False       |         0.0029 |    0.0055 | -92.50%   |
+--------------+-------------+----------------+-----------+-----------+
|          640 | True        |         0.002  |    0.0055 | -168.75%  |
+--------------+-------------+----------------+-----------+-----------+
|          640 | False       |         0.0024 |    0.0062 | -161.35%  |
+--------------+-------------+----------------+-----------+-----------+
|         6400 | True        |         0.0021 |    0.0055 | -160.13%  |
+--------------+-------------+----------------+-----------+-----------+
|         6400 | False       |         0.0021 |    0.0068 | -215.46%  |
+--------------+-------------+----------------+-----------+-----------+
|        64000 | True        |         0.0042 |    0.0065 | -55.29%   |
+--------------+-------------+----------------+-----------+-----------+
|        64000 | False       |         0.0029 |    0.0077 | -169.56%  |
+--------------+-------------+----------------+-----------+-----------+
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156974
Approved by: https://github.com/ramanishsingh

2025-06-27 04:49:43 +00:00

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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:

pip install numpy tabulate

Running the benchmarks

To run the BatchSampler benchmark:

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:

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
Run the benchmark to compare its performance against the original

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