def test_matmul(self):
lhs = Variable(torch.randn(5, 3), requires_grad=True)
rhs = Variable(torch.randn(3, 4), requires_grad=True)
covar = MatmulLazyVariable(lhs, rhs)
mat = Variable(torch.randn(4, 10))
res = covar.matmul(mat)
lhs_clone = Variable(lhs.data.clone(), requires_grad=True)
rhs_clone = Variable(rhs.data.clone(), requires_grad=True)
mat_clone = Variable(mat.data.clone())
actual = lhs_clone.matmul(rhs_clone).matmul(mat_clone)
self.assertTrue(approx_equal(res.data, actual.data))
actual.sum().backward()
res.sum().backward()
self.assertTrue(approx_equal(lhs.grad.data, lhs_clone.grad.data))
self.assertTrue(approx_equal(rhs.grad.data, rhs_clone.grad.data))
def test_matmul(self):
lhs = torch.randn(5, 3, requires_grad=True)
rhs = torch.randn(3, 4, requires_grad=True)
covar = MatmulLazyVariable(lhs, rhs)
mat = torch.randn(4, 10)
res = covar.matmul(mat)
lhs_clone = lhs.clone().detach()
rhs_clone = rhs.clone().detach()
mat_clone = mat.clone().detach()
lhs_clone.requires_grad = True
rhs_clone.requires_grad = True
mat_clone.requires_grad = True
actual = lhs_clone.matmul(rhs_clone).matmul(mat_clone)
self.assertTrue(approx_equal(res, actual))
actual.sum().backward()
res.sum().backward()
self.assertTrue(approx_equal(lhs.grad, lhs_clone.grad))
self.assertTrue(approx_equal(rhs.grad, rhs_clone.grad))