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 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, 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'})
def checkScriptRaisesRegex(self,
script,
inputs,
exception,
regex,
outputs=None,
capture_output=False,
profiling=ProfilingMode.PROFILING):
"""
Checks that a given function will throw the correct exception,
when executed with normal python, the string frontend, and the AST frontend
"""
with enable_profiling_mode():
# normal python
with self.assertRaisesRegex(exception, regex):
script(*inputs)
# string frontend
with self.assertRaisesRegex(exception, regex):
source = textwrap.dedent(inspect.getsource(script))
cu = torch.jit.CompilationUnit(source)
ge = getattr(cu, script.__name__)
# profiling run
with self.assertRaisesRegex(exception, regex):
ge(*inputs)
# optimized run
ge(*inputs)
# python AST frontend
with self.assertRaisesRegex(exception, regex):
ge = torch.jit.script(script)
# profiling run
with self.assertRaisesRegex(exception, regex):
ge(*inputs)
# optimized run
ge(*inputs)
def _perform_ad_subgraph_slicing(self, fn, *input_sizes):
with disable_autodiff_subgraph_inlining():
with enable_profiling_mode():
ge = torch.jit.script(fn)
inputs = [
torch.randn(size, requires_grad=True)
for size in input_sizes
]
ge(*inputs, profile_and_replay=True)
return ge.graph_for(*inputs)
def test_chunk_constant_script_ad(self):
@torch.jit.script
def func(x):
x1, x2 = torch.chunk(x, 2)
return (x1, x2)
input = torch.rand(6, 10).requires_grad_()
with disable_autodiff_subgraph_inlining():
with enable_profiling_mode():
output = func(input, profile_and_replay=True)
self.assertAutodiffNode(func.graph_for(input), True,
['prim::ConstantChunk'], [])
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',
'aten::_size_if_not_equal'
})
with enable_profiling_mode(True):
c = s(b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2)
warmup_backward(c.sum(),
[b1x1, b1y1, b1x2, b1y2, b2x1, b2y1, b2x2, b2y2])
graph = backward_graph(s)
self.assertAllFused(graph,
except_for={
'aten::size', 'prim::BroadcastSizes',
'aten::_size_if_not_equal'
})
def _test_reinforcement_learning(self, device, test_export_import=True):
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.affine2 = nn.Linear(128, 2)
def forward(self, x):
x = F.relu(self.affine1(x))
action_scores = self.affine2(x)
return F.softmax(action_scores, dim=1)
with enable_profiling_mode():
self.checkTrace(Policy().to(device), (torch.rand(1, 4, device=device),),
export_import=test_export_import)
def _test_vae(self, device, check_export_import=True, quantized=False):
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 400)
self.fc21 = nn.Linear(400, 20)
self.fc22 = nn.Linear(400, 20)
self.fc3 = nn.Linear(20, 400)
self.fc4 = nn.Linear(400, 784)
def encode(self, x):
h1 = F.relu(self.fc1(x))
return self.fc21(h1), self.fc22(h1)
def reparameterize(self, mu, logvar):
if self.training:
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return eps.mul(std).add_(mu)
else:
return mu
def decode(self, z):
h3 = F.relu(self.fc3(z))
return torch.sigmoid(self.fc4(h3))
def forward(self, x):
mu, logvar = self.encode(x.view(-1, 784))
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar
if quantized:
vae = VAE().to(device).eval()
torch.jit.quantized.quantize_linear_modules(vae)
# We don't do export/import checks because we would need to call
# _unpack and _pack
self.checkTrace(vae, (torch.rand(128, 1, 28, 28, device=device), ),
export_import=False,
allow_unused=True,
inputs_require_grads=False)
else:
with enable_profiling_mode():
# eval() is present because randn_like makes this nondeterministic
self.checkTrace(VAE().to(device).eval(),
(torch.rand(128, 1, 28, 28, device=device), ),
export_import=check_export_import)
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, 'prim::FusionGroup', 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(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_mnist(self, device, check_export_import=True):
# eval() is present because dropout makes this nondeterministic
with enable_profiling_mode():
self.checkTrace(MnistNet().to(device).eval(), (torch.rand(5, 1, 28, 28, device=device),),
export_import=check_export_import)
def checkScript(self,
script,
inputs,
name='func',
optimize=True,
inputs_requires_grad=False,
capture_output=False,
frames_up=1,
profiling=ProfilingMode.PROFILING):
with torch.jit.optimized_execution(optimize):
with enable_profiling_mode():
if isinstance(script, str):
# Compile the string to a Script function
# with enable_profiling_mode():
cu = torch.jit.CompilationUnit(script,
_frames_up=frames_up)
# Execute the Python function so we can run it later and get its
# outputs
frame = self.get_frame_vars(frames_up)
the_locals = {}
execWrapper(script, glob=frame, loc=the_locals)
frame.update(the_locals)
python_fn = frame[name]
scripted_fn = getattr(cu, name)
else:
# Check the string frontend first
source = textwrap.dedent(inspect.getsource(script))
self.checkScript(source,
inputs,
script.__name__,
capture_output,
profiling=profiling,
frames_up=2)
# Continue checking the Python frontend
scripted_fn = torch.jit.script(script, _frames_up=1)
python_fn = script
if inputs_requires_grad:
recording_inputs = do_input_map(
lambda t: t.detach().requires_grad_(), inputs)
else:
recording_inputs = inputs
if capture_output:
with self.capture_stdout() as script_stdout:
script_outputs = scripted_fn(*recording_inputs)
with self.capture_stdout() as opt_script_stdout:
opt_script_outputs = scripted_fn(*recording_inputs)
with self.capture_stdout() as _python_stdout:
python_outputs = python_fn(*inputs)
if not IS_WINDOWS:
self.assertExpected(script_stdout[0], subname='stdout')
self.assertEqual(python_outputs, opt_script_outputs)
else:
# profiling run
script_outputs = scripted_fn(*recording_inputs)
# optimized run
opt_script_outputs = scripted_fn(*recording_inputs)
python_outputs = python_fn(*inputs)
self.assertEqual(python_outputs, script_outputs)
self.assertEqual(script_outputs, opt_script_outputs)
return scripted_fn