Kipsora
commented
5 months ago Hi @ErinTUDelft, thanks for your interests in Minuet. Here is a piece of code that illustrates how to use Minuet for inference (training is not supported currently but is in our plans).
# Filename: run_simplenet_inference.py
import argparse
from typing import Optional
import numpy as np
import torch
import minuet
class SimplePCNet(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.conv1 = minuet.nn.SparseConv3d(
in_channels=4,
out_channels=32,
kernel_size=3,
)
self.conv2 = minuet.nn.SparseConv3d(
in_channels=32,
out_channels=32,
kernel_size=3,
stride=2,
)
self.conv3 = minuet.nn.SparseConv3d(
in_channels=32,
out_channels=32,
kernel_size=3,
)
def forward(self, x: minuet.SparseTensor):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = minuet.nn.functional.global_avg_pool(x)
return x
def make_dummy_point_cloud(
batch_size: Optional[int],
num_points: int,
num_features: int = 4,
# Minuet always requires sorted coordinates
ordered: bool = True,
c_min: int = 0,
c_max: int = 100):
if batch_size is None:
coordinates = minuet.utils.random.random_unique_points(ndim=3,
n=num_points,
c_min=c_min,
c_max=c_max)
coordinates = torch.tensor(coordinates).cuda()
batch_dims = None
else:
coordinates = [
minuet.utils.random.random_unique_points(ndim=3,
n=num_points,
c_min=c_min,
c_max=c_max)
for _ in range(batch_size)
]
batch_dims = [0]
batch_dims.extend([len(c) for c in coordinates])
batch_dims = np.cumsum(np.asarray(batch_dims))
coordinates = torch.concat([torch.tensor(c) for c in coordinates])
coordinates = coordinates.cuda()
batch_dims = torch.tensor(batch_dims, device=coordinates.device)
features = torch.randn(len(coordinates),
num_features,
device=coordinates.device)
if ordered:
index = minuet.nn.functional.arg_sort_coordinates(coordinates,
batch_dims=batch_dims)
coordinates = coordinates[index]
# Don't forget to permute your features
# It doesn't matter for dummy inputs though
features = features[index]
return minuet.SparseTensor(features=features,
coordinates=coordinates,
batch_dims=batch_dims)
def main(args):
tuning_data = [
make_dummy_point_cloud(num_points=args.num_points,
batch_size=args.batch_size) for _ in range(5)
]
net = SimplePCNet().cuda()
net.eval()
cache = minuet.nn.KernelMapCache(ndim=3, dtype=torch.int32, device="cuda:0")
minuet.set_kernel_map_cache(module=net, cache=cache)
# Autotuning is optional but it is better for performance
minuet.autotune(net, cache, data=tuning_data)
# At the current moment, Minuet does not support training
with torch.no_grad():
for i in range(10):
# Before each different input the model cache must be reset
# Note that the minuet.autotune may touch the cache as will
cache.reset()
dummy_input = make_dummy_point_cloud(num_points=args.num_points,
batch_size=args.batch_size)
print(net(dummy_input))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size",
"-B",
type=int,
default=None,
help="batch size for inference")
parser.add_argument("--num_points",
"-N",
type=int,
required=True,
help="number of points for random generated point cloud")
main(parser.parse_args())You can then execute this piece of code with python3 run_simplenet_inference.py -N 500000 -B 2
Regarding to the issue you posted, Minuet reuses the cuBLAS handles created by PyTorch. So if PyTorch have not created it, the cuBLAS handle will be invalid. However, this looks like a pretty rare case. If would be great if you could provide a minimal code snippet for us to better understand which situation that triggers such error.
I am getting the following error when running a custom sparse model: RuntimeError: [Minuet] [.../csrc/minuet/cuda/helpers.cu (77)] CUBLAS Error: CUBLAS_STATUS_NOT_INITIALIZED
I think it might be due to an improper definition for my SparseTensor but I'm not sure. Would it be possible to provide an example file with dummy input tensors? That would be enormously helpful!
Kind regards, Erin