63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
# A reimplemented version in public environments by Xiao Fu and Mu Hu
|
|
|
|
import torch
|
|
import math
|
|
|
|
|
|
# Search table for suggested max. inference batch size
|
|
bs_search_table = [
|
|
# tested on A100-PCIE-80GB
|
|
{"res": 768, "total_vram": 79, "bs": 35, "dtype": torch.float32},
|
|
{"res": 1024, "total_vram": 79, "bs": 20, "dtype": torch.float32},
|
|
# tested on A100-PCIE-40GB
|
|
{"res": 768, "total_vram": 39, "bs": 15, "dtype": torch.float32},
|
|
{"res": 1024, "total_vram": 39, "bs": 8, "dtype": torch.float32},
|
|
{"res": 768, "total_vram": 39, "bs": 30, "dtype": torch.float16},
|
|
{"res": 1024, "total_vram": 39, "bs": 15, "dtype": torch.float16},
|
|
# tested on RTX3090, RTX4090
|
|
{"res": 512, "total_vram": 23, "bs": 20, "dtype": torch.float32},
|
|
{"res": 768, "total_vram": 23, "bs": 7, "dtype": torch.float32},
|
|
{"res": 1024, "total_vram": 23, "bs": 3, "dtype": torch.float32},
|
|
{"res": 512, "total_vram": 23, "bs": 40, "dtype": torch.float16},
|
|
{"res": 768, "total_vram": 23, "bs": 18, "dtype": torch.float16},
|
|
{"res": 1024, "total_vram": 23, "bs": 10, "dtype": torch.float16},
|
|
# tested on GTX1080Ti
|
|
{"res": 512, "total_vram": 10, "bs": 5, "dtype": torch.float32},
|
|
{"res": 768, "total_vram": 10, "bs": 2, "dtype": torch.float32},
|
|
{"res": 512, "total_vram": 10, "bs": 10, "dtype": torch.float16},
|
|
{"res": 768, "total_vram": 10, "bs": 5, "dtype": torch.float16},
|
|
{"res": 1024, "total_vram": 10, "bs": 3, "dtype": torch.float16},
|
|
]
|
|
|
|
|
|
def find_batch_size(ensemble_size: int, input_res: int, dtype: torch.dtype) -> int:
|
|
"""
|
|
Automatically search for suitable operating batch size.
|
|
|
|
Args:
|
|
ensemble_size (`int`):
|
|
Number of predictions to be ensembled.
|
|
input_res (`int`):
|
|
Operating resolution of the input image.
|
|
|
|
Returns:
|
|
`int`: Operating batch size.
|
|
"""
|
|
if not torch.cuda.is_available():
|
|
return 1
|
|
|
|
total_vram = torch.cuda.mem_get_info()[1] / 1024.0**3
|
|
filtered_bs_search_table = [s for s in bs_search_table if s["dtype"] == dtype]
|
|
for settings in sorted(
|
|
filtered_bs_search_table,
|
|
key=lambda k: (k["res"], -k["total_vram"]),
|
|
):
|
|
if input_res <= settings["res"] and total_vram >= settings["total_vram"]:
|
|
bs = settings["bs"]
|
|
if bs > ensemble_size:
|
|
bs = ensemble_size
|
|
elif bs > math.ceil(ensemble_size / 2) and bs < ensemble_size:
|
|
bs = math.ceil(ensemble_size / 2)
|
|
return bs
|
|
|
|
return 1 |