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import time
import os.path as osp
import itertools
import argparse
import wget
import torch
from scipy.io import loadmat
from torch_scatter import scatter, segment_coo, segment_csr
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 = loadmat(f'{name}.mat')['Problem'][0][0][2].tocsr()
rowptr = torch.from_numpy(mat.indptr).to(args.device, torch.long)
row = torch.from_numpy(mat.tocoo().row).to(args.device, torch.long)
dim_size = rowptr.size(0) - 1
for size in sizes:
try:
x = torch.randn((row.size(0), size), device=args.device)
x = x.squeeze(-1) if size == 1 else x
out1 = scatter(x, row, dim=0, dim_size=dim_size, reduce='add')
out2 = segment_coo(x, row, dim_size=dim_size, reduce='add')
out3 = segment_csr(x, rowptr, reduce='add')
assert torch.allclose(out1, out2, atol=1e-4)
assert torch.allclose(out1, out3, atol=1e-4)
out1 = scatter(x, row, dim=0, dim_size=dim_size, reduce='mean')
out2 = segment_coo(x, row, dim_size=dim_size, reduce='mean')
out3 = segment_csr(x, rowptr, reduce='mean')
assert torch.allclose(out1, out2, atol=1e-4)
assert torch.allclose(out1, out3, atol=1e-4)
out1 = scatter(x, row, dim=0, dim_size=dim_size, reduce='min')
out2 = segment_coo(x, row, reduce='min')
out3 = segment_csr(x, rowptr, reduce='min')
assert torch.allclose(out1, out2, atol=1e-4)
assert torch.allclose(out1, out3, atol=1e-4)
out1 = scatter(x, row, dim=0, dim_size=dim_size, reduce='max')
out2 = segment_coo(x, row, reduce='max')
out3 = segment_csr(x, rowptr, reduce='max')
assert torch.allclose(out1, out2, atol=1e-4)
assert torch.allclose(out1, out3, 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()
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()
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 = loadmat(f'{name}.mat')['Problem'][0][0][2].tocsr()
rowptr = torch.from_numpy(mat.indptr).to(args.device, torch.long)
row = torch.from_numpy(mat.tocoo().row).to(args.device, torch.long)
row2 = row[torch.randperm(row.size(0))]
dim_size = rowptr.size(0) - 1
avg_row_len = row.size(0) / dim_size
def sca1_row(x):
out = x.new_zeros(dim_size, *x.size()[1:])
row_tmp = row.view(-1, 1).expand_as(x) if x.dim() > 1 else row
return out.scatter_add_(0, row_tmp, x)
def sca1_col(x):
out = x.new_zeros(dim_size, *x.size()[1:])
row2_tmp = row2.view(-1, 1).expand_as(x) if x.dim() > 1 else row2
return out.scatter_add_(0, row2_tmp, x)
def sca2_row(x):
return scatter(x, row, dim=0, dim_size=dim_size, reduce=args.reduce)
def sca2_col(x):
return scatter(x, row2, dim=0, dim_size=dim_size, reduce=args.reduce)
def seg_coo(x):
return segment_coo(x, row, reduce=args.reduce)
def seg_csr(x):
return segment_csr(x, rowptr, reduce=args.reduce)
def dense1(x):
return getattr(torch, args.reduce)(x, dim=-2)
def dense2(x):
return getattr(torch, args.reduce)(x, dim=-1)
t1, t2, t3, t4, t5, t6, t7, t8 = [], [], [], [], [], [], [], []
for size in sizes:
try:
x = torch.randn((row.size(0), size), device=args.device)
x = x.squeeze(-1) if size == 1 else x
t1 += [time_func(sca1_row, x)]
t2 += [time_func(sca1_col, x)]
t3 += [time_func(sca2_row, x)]
t4 += [time_func(sca2_col, x)]
t5 += [time_func(seg_coo, x)]
t6 += [time_func(seg_csr, 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, t5, t6):
t.append(float('inf'))
try:
x = torch.randn((dim_size, int(avg_row_len + 1), size),
device=args.device)
t7 += [time_func(dense1, x)]
x = x.view(dim_size, size, int(avg_row_len + 1))
t8 += [time_func(dense2, 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 (t7, t8):
t.append(float('inf'))
ts = torch.tensor([t1, t2, t3, t4, t5, t6, t7, t8])
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: {avg_row_len:.2f}):')
print('\t'.join([' '] + [f'{size:>5}' for size in sizes]))
print('\t'.join([bold('SCA1_ROW')] +
[bold(f'{t:.5f}', f) for t, f in zip(t1, winner[0])]))
print('\t'.join([bold('SCA1_COL')] +
[bold(f'{t:.5f}', f) for t, f in zip(t2, winner[1])]))
print('\t'.join([bold('SCA2_ROW')] +
[bold(f'{t:.5f}', f) for t, f in zip(t3, winner[2])]))
print('\t'.join([bold('SCA2_COL')] +
[bold(f'{t:.5f}', f) for t, f in zip(t4, winner[3])]))
print('\t'.join([bold('SEG_COO ')] +
[bold(f'{t:.5f}', f) for t, f in zip(t5, winner[4])]))
print('\t'.join([bold('SEG_CSR ')] +
[bold(f'{t:.5f}', f) for t, f in zip(t6, winner[5])]))
print('\t'.join([bold('DENSE1 ')] +
[bold(f'{t:.5f}', f) for t, f in zip(t7, winner[6])]))
print('\t'.join([bold('DENSE2 ')] +
[bold(f'{t:.5f}', f) for t, f in zip(t8, winner[7])]))
print()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--reduce', type=str, required=True,
choices=['sum', 'mean', 'min', 'max'])
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[:3] 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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