Update deprecated PyTorch functions in fbcode/deeplearning

crypten/common/util.py

@@ -49,7 +49,7 @@ def chebyshev_series(func, width, terms):
     n_range = torch.arange(start=0, end=terms).float()
     x = width * torch.cos((n_range + 0.5) * np.pi / terms)
     y = func(x)
-    cos_term = torch.cos(torch.ger(n_range, n_range + 0.5) * np.pi / terms)
+    cos_term = torch.cos(torch.outer(n_range, n_range + 0.5) * np.pi / terms)
     coeffs = (2 / terms) * torch.sum(y * cos_term, axis=1)
     return coeffs
 

examples/bandits/plain_contextual_bandits.py

@@ -81,7 +81,7 @@ def online_learner(
 
         # update linear least squares accumulators (using Sherman–Morrison formula):
         A_inv_context = A_inv[selected_arm, :, :].mv(context)
-        numerator = torch.ger(A_inv_context, A_inv_context)
+        numerator = torch.outer(A_inv_context, A_inv_context)
         denominator = A_inv_context.dot(context).add(1.0)
         A_inv[selected_arm, :, :].sub_(numerator.div_(denominator))
         b[selected_arm, :].add_(context.mul(reward))

test/test_arithmetic.py

@@ -345,7 +345,7 @@ def test_dot_ger(self):
             tensor1 = get_random_test_tensor(is_float=True).squeeze()
             tensor2 = get_random_test_tensor(is_float=True).squeeze()
             dot_reference = tensor1.dot(tensor2)
-            ger_reference = torch.ger(tensor1, tensor2)
+            ger_reference = torch.outer(tensor1, tensor2)
 
             tensor2 = tensor_type(tensor2)
 

test/test_mpc.py

@@ -366,7 +366,7 @@ def test_dot_ger(self):
             tensor1 = self._get_random_test_tensor(is_float=True).squeeze()
             tensor2 = self._get_random_test_tensor(is_float=True).squeeze()
             dot_reference = tensor1.dot(tensor2)
-            ger_reference = torch.ger(tensor1, tensor2)
+            ger_reference = torch.outer(tensor1, tensor2)
 
             tensor2 = tensor_type(tensor2)