def test_body(M, N, L, K):
if not torch.cuda.is_available():
return
cuda_cg_executed = CudaCodeGenExecuted()
cuda_cg_created = CudaCodeGenCreated()
def test(x, y, z):
v1 = torch.add(x, y)
v2 = torch.add(v1, z)
return v2
a_shape = [M, N]
b_shape = [L, M, 1]
c_shape = [K, L, 1, 1]
traced = torch.jit.trace(
test,
(
torch.rand(*a_shape, device="cuda"),
torch.rand(*b_shape, device="cuda"),
torch.rand(*c_shape, device="cuda"),
),
)
a = torch.rand(*a_shape, device="cuda")
b = torch.rand(*b_shape, device="cuda")
c = torch.rand(*c_shape, device="cuda")
x = traced(a, b, c)
npr = a.cpu().numpy() + b.cpu().numpy() + c.cpu().numpy()
np.testing.assert_allclose(npr, x.cpu().numpy())
assert cuda_cg_executed.elapsed_value() >= 1
assert cuda_cg_created.elapsed_value() >= 1
def test_three_arg_cuda(self):
if not torch.cuda.is_available():
return
cuda_cg_executed = CudaCodeGenExecuted()
cuda_cg_created = CudaCodeGenCreated()
def test(x, y, z):
aaa = torch.add(x, y)
bbb = torch.add(aaa, z)
return bbb
M = 32
N = 32
traced = torch.jit.trace(
test,
(
torch.rand(M, N, device="cuda"),
torch.rand(M, N, device="cuda"),
torch.rand(M, N, device="cuda"),
),
)
a = torch.rand(M, N, device="cuda")
b = torch.rand(M, N, device="cuda")
c = torch.rand(M, N, device="cuda")
x = traced(a, b, c)
npr = a.cpu().numpy() + b.cpu().numpy() + c.cpu().numpy()
np.testing.assert_allclose(npr, x.cpu().numpy())
assert cuda_cg_executed.elapsed_value() >= 1
assert cuda_cg_created.elapsed_value() >= 1
def test_dynamic_shape(self):
with num_profiled_runs(2):
@torch.jit.script
def test(x, y, z):
return x * y * z
cuda = CudaCodeGenCreated()
x, y, z = [torch.rand(4, 8).cuda() for _ in range(3)]
ref = test(x, y, z)
_ = test(*[torch.rand(6, 8).cuda() for _ in range(3)])
res = test(x, y, z)
np.testing.assert_allclose(ref.cpu().numpy(), res.cpu().numpy())
assert cuda.elapsed_value() == 1
# A wild broadcast appears.
x = torch.rand(4, 8).cuda()
y = torch.rand(1, 8).cuda()
z = torch.rand(4, 1).cuda()
res = test(x, y, z)
xn, yn, zn = [t.cpu().numpy() for t in (x, y, z)]
np.testing.assert_allclose(res.cpu().numpy(), xn * yn * zn)
assert cuda.elapsed_value() == 1
# Mismatched shapes shouldn't reach codegen.
x = torch.rand(4, 8).cuda()
y = torch.rand(4, 8).cuda()
z = torch.rand(5, 8).cuda()
try:
res = test(x, y, z)
except RuntimeError as e:
assert "The size of tensor a (4) must match" in e.args[0]
assert cuda.elapsed_value() == 1