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import argparse
import itertools
import os.path as osp
import time
import torch
import wget
from scipy.io import loadmat
from torch_scatter import scatter_add
from torch_sparse.tensor import SparseTensor
short_rows = [
('DIMACS10', 'citationCiteseer'),
('SNAP', 'web-Stanford'),
]
long_rows = [
('Janna', 'StocF-1465'),
('GHS_psdef', 'ldoor'),
]
def download(dataset):
url = 'https://sparse.tamu.edu/mat/{}/{}.mat'
for group, name in itertools.chain(long_rows, short_rows):
if not osp.exists(f'{name}.mat'):
print(f'Downloading {group}/{name}:')
wget.download(url.format(group, name))
print('')
def bold(text, flag=True):
return f'\033[1m{text}\033[0m' if flag else text
@torch.no_grad()
def correctness(dataset):
group, name = dataset
mat_scipy = loadmat(f'{name}.mat')['Problem'][0][0][2].tocsr()
row = torch.from_numpy(mat_scipy.tocoo().row).to(args.device, torch.long)
col = torch.from_numpy(mat_scipy.tocoo().col).to(args.device, torch.long)
mat = SparseTensor(row=row, col=col, sparse_sizes=mat_scipy.shape)
mat.fill_cache_()
mat_pytorch = mat.to_torch_sparse_coo_tensor().coalesce()
for size in sizes:
try:
x = torch.randn((mat.size(1), size), device=args.device)
out1 = mat @ x
out2 = mat_pytorch @ x
assert torch.allclose(out1, out2, atol=1e-4)
except RuntimeError as e:
if 'out of memory' not in str(e):
raise RuntimeError(e)
torch.cuda.empty_cache()
def time_func(func, x):
try:
if torch.cuda.is_available():
torch.cuda.synchronize()
elif torch.backends.mps.is_available():
import torch.mps
torch.mps.synchronize()
t = time.perf_counter()
if not args.with_backward:
with torch.no_grad():
for _ in range(iters):
func(x)
else:
x = x.requires_grad_()
for _ in range(iters):
out = func(x)
out = out[0] if isinstance(out, tuple) else out
torch.autograd.grad(out, x, out, only_inputs=True)
if torch.cuda.is_available():
torch.cuda.synchronize()
elif torch.backends.mps.is_available():
import torch.mps
torch.mps.synchronize()
return time.perf_counter() - t
except RuntimeError as e:
if 'out of memory' not in str(e):
raise RuntimeError(e)
torch.cuda.empty_cache()
return float('inf')
def timing(dataset):
group, name = dataset
mat_scipy = loadmat(f'{name}.mat')['Problem'][0][0][2].tocsr()
row = torch.from_numpy(mat_scipy.tocoo().row).to(args.device, torch.long)
col = torch.from_numpy(mat_scipy.tocoo().col).to(args.device, torch.long)
mat = SparseTensor(row=row, col=col, sparse_sizes=mat_scipy.shape)
mat.fill_cache_()
mat_pytorch = mat.to_torch_sparse_coo_tensor().coalesce()
mat_scipy = mat.to_scipy(layout='csr')
def scatter(x):
return scatter_add(x[col], row, dim=0, dim_size=mat_scipy.shape[0])
def spmm_scipy(x):
if x.is_cuda:
raise RuntimeError('out of memory')
return mat_scipy @ x
def spmm_pytorch(x):
return mat_pytorch @ x
def spmm(x):
return mat @ x
t1, t2, t3, t4 = [], [], [], []
for size in sizes:
try:
x = torch.randn((mat.size(1), size), device=args.device)
t1 += [time_func(scatter, x)]
t2 += [time_func(spmm_scipy, x)]
t3 += [time_func(spmm_pytorch, x)]
t4 += [time_func(spmm, x)]
del x
except RuntimeError as e:
if 'out of memory' not in str(e):
raise RuntimeError(e)
torch.cuda.empty_cache()
for t in (t1, t2, t3, t4):
t.append(float('inf'))
ts = torch.tensor([t1, t2, t3, t4])
winner = torch.zeros_like(ts, dtype=torch.bool)
winner[ts.argmin(dim=0), torch.arange(len(sizes))] = 1
winner = winner.tolist()
name = f'{group}/{name}'
print(f'{bold(name)} (avg row length: {mat.avg_row_length():.2f}):')
print('\t'.join([' '] + [f'{size:>5}' for size in sizes]))
print('\t'.join([bold('Scatter ')] +
[bold(f'{t:.5f}', f) for t, f in zip(t1, winner[0])]))
print('\t'.join([bold('SPMM SciPy ')] +
[bold(f'{t:.5f}', f) for t, f in zip(t2, winner[1])]))
print('\t'.join([bold('SPMM PyTorch')] +
[bold(f'{t:.5f}', f) for t, f in zip(t3, winner[2])]))
print('\t'.join([bold('SPMM Own ')] +
[bold(f'{t:.5f}', f) for t, f in zip(t4, winner[3])]))
print()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--with_backward', action='store_true')
parser.add_argument('--device', type=str, default='cuda')
args = parser.parse_args()
iters = 1 if args.device == 'cpu' else 20
sizes = [1, 16, 32, 64, 128, 256, 512]
sizes = sizes[:4] if args.device == 'cpu' else sizes
for _ in range(10): # Warmup.
torch.randn(100, 100, device=args.device).sum()
for dataset in itertools.chain(short_rows, long_rows):
download(dataset)
correctness(dataset)
timing(dataset)
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