def test_dropout(self):
def func(x):
x = torch.nn.functional.dropout(x)
return torch.nn.functional.relu(x)
a = torch.randn(4,
4,
dtype=torch.float,
device='cuda',
requires_grad=True)
s = torch.jit.script(func)
c = s(a)
c = s(a)
warmup_backward(c.sum())
# skip_check to skip extra bailout nodes in between
graph = backward_graph(s, skip_check=True)
self.assertAllFused(graph, except_for={'aten::div', 'prim::Constant'})
def test_fuser_deduplication(self):
# See that fusion kernel outputs are deduplicated when removing _grad_sum_to_size in the fuser's compilation
# see the discussion in PR #14957.
def f(x, y):
return torch.sigmoid(x + y)
b = torch.randn(5, 5, requires_grad=True)
a = torch.randn(5, 5, requires_grad=True)
s = self.checkScript(f, (a, b))
self.assertAllFused(s.graph_for(a, b), except_for={'aten::size'})
c = s(a, b)
ga, gb = torch.autograd.grad(c.sum(), [a, b])
graph = backward_graph(s)
self.assertAllFused(graph)
# check that a, b share storage, i.e. were generated as a single output in the fuser
self.assertEqual(ga.data_ptr(), gb.data_ptr())
def test_lstm_cuda(self):
inputs = get_lstm_inputs('cuda', training=True)
module = self.checkScript(LSTMCellS, inputs)
return
forward_graph = module.graph_for(*inputs)
self.assertGraphContainsExactly(
forward_graph, FUSION_GROUP, 1, consider_subgraphs=True)
self.assertTrue(len(strip_profiling_nodes(forward_graph.nodes())) == 2)
# Everything is differentiable but TupleConstruct return
FileCheck().check("DifferentiableGraph").check_next("TupleConstruct") \
.check_next("return").run(str(forward_graph))
with enable_profiling_mode_for_profiling_tests(True):
hy, cy = module(*inputs)
warmup_backward((hy + cy).sum())
backward = backward_graph(module)
self.assertAllFused(backward, except_for=("aten::t", "aten::mm",
"aten::_grad_sum_to_size"))
def test_lstm_cuda(self):
inputs = get_lstm_inputs('cuda', training=True)
module = self.checkScript(LSTMCellS, inputs)
forward_graph = module.graph_for(*inputs)
self.assertGraphContainsExactly(forward_graph,
'prim::FusionGroup',
1,
consider_subgraphs=True)
self.assertTrue(len(list(forward_graph.nodes())) == 2)
# Everything is differentiable but TupleConstruct return
FileCheck().check("DifferentiableGraph").check_next("TupleConstruct") \
.check_next("return").run(str(forward_graph))
hy, cy = module(*inputs)
(hy + cy).sum().backward()
backward = backward_graph(module)
FileCheck().check("FusionGroup_0").check_next("FusionGroup_1") \
.check_not("FusionGroup_2").run(str(backward))
def test_fuser_iou(self):
# This checks if most of Intersection over Union is fused.
# In particular, the backward contains many _grad_sum_to_size.
def iou(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2):
ltx = torch.max(b1x1, b2x1) # [N,M]
lty = torch.max(b1y1, b2y1)
rbx = torch.min(b1x2, b2x2)
rby = torch.min(b1y2, b2y2)
w = (rbx - ltx).clamp(min=0, max=float('inf')) # [N,M]
h = (rby - lty).clamp(min=0, max=float('inf')) # [N,M]
inter = w * h # [N,M]
area1 = (b1x2 - b1x1) * (b1y2 - b1y2) # [N,1]
area2 = (b2x2 - b2x1) * (b2y2 - b2y2) # [1,M]
iou = inter / (area1 + area2 - inter)
return iou
box1 = torch.randn(5, 4, requires_grad=True)
box2 = torch.randn(5, 4, requires_grad=True)
# unsqueezing can currently not be fused
b1x1 = box1[:, 0].unsqueeze(1) # [N,1]
b1y1 = box1[:, 1].unsqueeze(1)
b1x2 = box1[:, 2].unsqueeze(1)
b1y2 = box1[:, 3].unsqueeze(1)
b2x1 = box2[:, 0].unsqueeze(0) # [1,N]
b2y1 = box2[:, 1].unsqueeze(0)
b2x2 = box2[:, 2].unsqueeze(0)
b2y2 = box2[:, 3].unsqueeze(0)
s = self.checkScript(iou,
(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2))
self.assertAllFused(s.graph_for(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1,
b2x2, b2y2),
except_for={'aten::size', 'prim::BroadcastSizes'})
c = s(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2)
torch.autograd.grad(c.sum(),
[b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2])
graph = backward_graph(s)
self.assertAllFused(graph,
except_for={'aten::size', 'prim::BroadcastSizes'})
def test_clamp(self):
def func2(a, b):
return torch.clamp(a + b, min=0, max=2)
def funcInf(a, b):
return torch.clamp(a + b, min=0, max=float('inf'))
def funcNegInf(a, b):
return torch.clamp(a + b, min=float('-inf'), max=0)
def funcOptMin(a, b):
return torch.clamp(a + b, max=2)
def funcOptMax(a, b):
return torch.clamp(a + b, min=0)
a = torch.randn(4,
4,
dtype=torch.float,
device='cuda',
requires_grad=True)
b = torch.randn(4, 4, dtype=torch.float, device='cuda')
nan = torch.tensor(float('nan'), dtype=torch.float, device='cuda')
funcs = (func2, funcInf, funcNegInf, funcOptMin, funcOptMax)
for f, inputs in product(funcs, [[a, b], [a, nan]]):
inp1, inp2 = inputs
s = self.checkScript(f, (inp1, inp2),
profiling=ProfilingMode.PROFILING)
self.assertAllFused(
s.graph_for(inp1, inp2),
except_for={'aten::size', 'aten::_size_if_not_equal'})
c = s(inp1, inp2)
with enable_profiling_mode():
warmup_backward(c.sum())
graph = backward_graph(s)
self.assertAllFused(
graph, except_for={'aten::Float', 'aten::_grad_sum_to_size'})
