add test to check zero_strided tensors in blas level 2 and 3 functions

test/test_torch.py

@@ -35,6 +35,104 @@ def test_dot(self):
                 res2 += i * 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.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)
+
     def _testMath(self, torchfn, mathfn):
         size = (10, 5)
         # contiguous