def test_sliced_stride(self):
@torch.jit.script
def test(x, y, z):
return x + y + z
llvm = LLVMCodeGenExecuted()
interp = SimpleIREvalExecuted()
x = torch.rand(16, 4, 2, 3)[::2]
y = torch.rand(8, 4, 2, 3)
z = torch.rand(8, 4, 2, 3)
ref = test(x, y, z)
res = test(x, y, z)
np.testing.assert_allclose(ref.numpy(), res.numpy())
assert llvm.elapsed_value() == 1 or interp.elapsed_value() == 1
def test_transpose(self):
@torch.jit.script
def test(x, y, z):
return x.transpose(0, 1) + y + z
llvm = LLVMCodeGenExecuted()
interp = SimpleIREvalExecuted()
x = torch.rand(4, 5, 2, 3)
y = torch.rand(5, 4, 2, 3)
z = torch.rand(5, 4, 2, 3)
ref = test(x, y, z)
res = test(x, y, z)
np.testing.assert_allclose(ref.numpy(), res.numpy())
assert llvm.elapsed_value() == 1 or interp.elapsed_value() == 1
def test_unsqueeze(self):
def easy(x, y):
a = torch.unsqueeze(x, 0)
b = torch.unsqueeze(y, 0)
return a + b
traced = torch.jit.trace(easy, (torch.ones(1024, 1024), torch.zeros(1024, 1024)))
llvm = LLVMCodeGenExecuted()
interp = SimpleIREvalExecuted()
a = torch.rand(1024, 1024)
x = traced(a, a)
npr = np.expand_dims(a, 0)
npr = npr + npr
np.testing.assert_allclose(npr, x.numpy())
assert llvm.elapsed_value() == 1 or interp.elapsed_value() == 1
def test_slice(self):
def easy(x, y):
a = x[0:512:2]
b = y[0:512:2]
return a + b
traced = torch.jit.trace(easy, (torch.ones(1024, 1024), torch.zeros(1024, 1024)))
llvm = LLVMCodeGenExecuted()
interp = SimpleIREvalExecuted()
a = torch.ones(1024, 1024)
x = traced(a, a)
npr = a[0:512:2]
npr = npr + npr
np.testing.assert_allclose(npr.numpy(), x.numpy())
assert llvm.elapsed_value() == 1 or interp.elapsed_value() == 1
def test_three_arg(self):
llvm_executed = LLVMCodeGenExecuted()
simple_ir_eval_executed = SimpleIREvalExecuted()
def easy(x, y, z):
aaa = torch.add(x, y)
bbb = torch.add(aaa, z)
return bbb
traced = torch.jit.trace(
easy, (torch.rand(1024), torch.rand(1024), torch.rand(1024)))
a = torch.rand(1024)
b = torch.rand(1024)
c = torch.rand(1024)
x = traced(a, b, c)
npr = a.numpy() + b.numpy() + c.numpy()
np.testing.assert_allclose(npr, x.numpy())
assert (llvm_executed.elapsed_value() >= 1
or simple_ir_eval_executed.elapsed_value() >= 1)
def test_scalar(self):
@torch.jit.script
def test_float(x, y, z, a, b):
# type: (Tensor, Tensor, Tensor, float, float) -> Tensor
return torch.add(torch.add(x, y, alpha=a), z, alpha=b)
@torch.jit.script
def test_int(x, y, z, a, b):
# type: (Tensor, Tensor, Tensor, int, int) -> Tensor
return torch.add(torch.add(x, y, alpha=a), z, alpha=b)
for test in (test_float, test_int):
llvm = LLVMCodeGenExecuted()
interp = SimpleIREvalExecuted()
x, y, z = [torch.rand(4) for i in range(3)]
a, b = 1, 2
test(x, y, z, a, b)
r = test(x, y, z, a, b)
xn, yn, zn = [t.numpy() for t in (x, y, z)]
np.testing.assert_allclose(r.numpy(), xn + yn * a + zn * b)
assert llvm.elapsed_value() == 1 or interp.elapsed_value() == 1
