Change:
res2 += i * j self.assertEqual(res1, res2) <ins> # Test 0-strided for tname, _prec in types.items(): v1 = torch.randn(1).type(tname).expand(100) v2 = torch.randn(100).type(tname) res1 = torch.dot(v1, v2) res2 = 0 for i, j in zip(v1, v2): res2 += i * j self.assertEqual(res1, res2) def test_ger(self): types = { 'torch.DoubleTensor': 1e-8, 'torch.FloatTensor': 1e-4, } for tname, _prec in types.items(): v1 = torch.randn(100).type(tname) v2 = torch.randn(100).type(tname) res1 = torch.ger(v1, v2) res2 = torch.zeros(100, 100).type(tname) for i in range(100): for j in range(100): res2[i, j] = v1[i] * v2[j] self.assertEqual(res1, res2) # Test 0-strided for tname, _prec in types.items(): v1 = torch.randn(1).type(tname).expand(100) v2 = torch.randn(100).type(tname) res1 = torch.ger(v1, v2) res2 = torch.zeros(100, 100).type(tname) for i in range(100): for j in range(100): res2[i, j] = v1[i] * v2[j] self.assertEqual(res1, res2) def test_addmv(self): types = { 'torch.DoubleTensor': 1e-8, 'torch.FloatTensor': 1e-4, } for tname, _prec in types.items(): t = torch.randn(10).type(tname) m = torch.randn(10, 100).type(tname) v = torch.randn(100).type(tname) res1 = torch.addmv(t, m, v) res2 = torch.zeros(10).type(tname) res2 += t for i in range(10): for j in range(100): res2[i] += m[i, j] * v[j] self.assertEqual(res1, res2) # Test 0-strided for tname, _prec in types.items(): t = torch.randn(1).type(tname).expand(10) m = torch.randn(10, 1).type(tname).expand(10, 100) v = torch.randn(100).type(tname) res1 = torch.addmv(t, m, v) res2 = torch.zeros(10).type(tname) res2 += t for i in range(10): for j in range(100): res2[i] += m[i, j] * v[j] self.assertEqual(res1, res2) def test_addmm(self): types = { 'torch.DoubleTensor': 1e-8, 'torch.FloatTensor': 1e-4, } for tname, _prec in types.items(): M = torch.randn(10, 25).type(tname) m1 = torch.randn(10, 50).type(tname) m2 = torch.randn(50, 25).type(tname) res1 = torch.addmm(M, m1, m2) res2 = torch.zeros(10, 25).type(tname) res2 += M for i in range(10): for j in range(25): for k in range(50): res2[i, j] += m1[i, k] * m2[k, j] self.assertEqual(res1, res2) # Test 0-strided for tname, _prec in types.items(): M = torch.randn(10, 1).type(tname).expand(10, 25) m1 = torch.randn(10, 1).type(tname).expand(10, 50) m2 = torch.randn(50, 25).type(tname) res1 = torch.addmm(M, m1, m2) res2 = torch.zeros(10, 25).type(tname) res2 += M for i in range(10): for j in range(25): for k in range(50): res2[i, j] += m1[i, k] * m2[k, j] self.assertEqual(res1, res2) </ins> def _testMath(self, torchfn, mathfn): size = (10, 5) # contiguous

Discussion:
It seems like autodiff of matrix terms of .mv() doesn't behave as I'd expect. But if we use .mm(), things work fine: python A = Variable((3,2), requiresgrad=True) x = Variable((2), requiresgrad=True) (A.mm(x[:,None])).sum().backward() print(A.grad) print(x.grad)
Hi, As a temporary fix, you can use . The problem is the behavior between the backward and the backward. The sum returns a strided gradient which is not supported by MKL/BLAS implementations of (used in the backward). not sure where this was introduced and how to fix this properly. Should the blas wrapper clone the input if it is strided? Or should it just raise an error (meaning that we need to add some s in some places in the code like the sum backward)?
It should clone the input. Still, this problem should have been caught. We must be missing some return code checks from BLAS
Ah apparently these functions have no return codes and the docs clearly state that vector strides can't be 0... We have to fix those bindings