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import time
import itertools
import argparse
import torch
from scipy.io import loadmat
from torch_scatter import gather_coo, gather_csr
from scatter_segment import short_rows, long_rows, download, bold
@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[1:]:
try:
x = torch.randn((dim_size, size), device=args.device)
x = x.squeeze(-1) if size == 1 else x
out1 = x.index_select(0, row)
out2 = gather_coo(x, row)
out3 = gather_csr(x, rowptr)
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)
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)
dim_size = rowptr.size(0) - 1
avg_row_len = row.size(0) / dim_size
def select(x):
return x.index_select(0, row)
def gather(x):
return x.gather(0, row.view(-1, 1).expand(-1, x.size(1)))
def gat_coo(x):
return gather_coo(x, row)
def gat_csr(x):
return gather_csr(x, rowptr)
t1, t2, t3, t4 = [], [], [], []
for size in sizes:
try:
x = torch.randn((dim_size, size), device=args.device)
t1 += [time_func(select, x)]
t2 += [time_func(gather, x)]
t3 += [time_func(gat_coo, x)]
t4 += [time_func(gat_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):
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: {avg_row_len:.2f}):')
print('\t'.join([' '] + [f'{size:>5}' for size in sizes]))
print('\t'.join([bold('SELECT ')] +
[bold(f'{t:.5f}', f) for t, f in zip(t1, winner[0])]))
print('\t'.join([bold('GAT ')] +
[bold(f'{t:.5f}', f) for t, f in zip(t2, winner[1])]))
print('\t'.join([bold('GAT_COO')] +
[bold(f'{t:.5f}', f) for t, f in zip(t3, winner[2])]))
print('\t'.join([bold('GAT_CSR')] +
[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[: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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