xangma fe8d66d9a6 Faster Faster BatchSampler (#137423) ... Builds upon #76951. Benchmarking code is the same as in #76950. AMD Ryzen Threadripper PRO 3995WX: ``` batch_size drop_last origin new speedup ------------ ----------- -------- ------ --------- 4 True 0.94 0.5706 64.74% 4 False 0.9745 0.9468 2.93% 8 True 0.7423 0.3715 99.82% 8 False 0.7974 0.5666 40.73% 64 True 0.5394 0.2085 158.76% 64 False 0.6083 0.2697 125.51% 640 True 0.5448 0.1985 174.41% 640 False 0.7085 0.2308 206.91% 6400 True 0.5554 0.2028 173.88% 6400 False 0.7711 0.2109 265.60% 64000 True 0.556 0.2091 165.82% 64000 False 0.7803 0.2078 275.58% ``` When `drop_last == True`, it uses `zip` to speed things up. When `drop_last == False`, it uses `itertools` to speed things up. `itertools` was the fastest way I could find that deals with the last batch if it is smaller than `batch_size`. I have a pure python method too, but it is slower when `batch_size` is 4 or 8, so I have committed the `itertools` version for now. Happy to chat further about this change :-) I understand you may not want to introduce the `itertools` package into [sampler.py](https://github.com/pytorch/pytorch/blob/main/torch/utils/data/sampler.py). Pull Request resolved: https://github.com/pytorch/pytorch/pull/137423 Approved by: https://github.com/Skylion007		2024-10-13 09:36:03 +00:00
..
_utils	Add None return type to init (#132335)	2024-08-01 15:26:45 +00:00
datapipes	[BE][Easy] enable ruff rule PIE790: unnecessary pass statement (#133200)	2024-08-15 15:50:19 +00:00
__init__.py
backward_compatibility.py
dataframes_pipes.ipynb
dataloader.py
dataset.py
distributed.py
graph_settings.py
graph.py
sampler.py	Faster Faster BatchSampler (#137423)	2024-10-13 09:36:03 +00:00
standard_pipes.ipynb
typing.ipynb