[MPS] Handle 1D inputs for NLL (#81290)

* Add test for NLL 1d
* Fix forward NLL for 1D case
* Handle NLL backward for 1d

Pull Request resolved: https://github.com/pytorch/pytorch/pull/81290
Approved by: https://github.com/razarmehr

test/test_mps.py

diff --git a/test/test_mps.py b/test/test_mps.py
index 9d94ad1..fd735b1 100644
--- a/test/test_mps.py
+++ b/test/test_mps.py
@@ -1701,6 +1701,26 @@
         output_mps.sum().backward()
         self.assertEqual(input.grad, input_mps.grad.to('cpu'))
 
+    def _nll_loss_1d_helper(self, input_size, reduction):
+
+        # CPU
+        input = torch.rand(input_size, requires_grad=True, device='cpu')
+        num_channels = input_size[0]
+        target = torch.randint(num_channels, [], device='cpu')
+
+        # MPS
+        input_mps = input.detach().clone().to('mps').requires_grad_()
+        target_mps = target.detach().clone().to('mps')
+
+        output_cpu = F.nll_loss(input, target, reduction=reduction)
+        output_mps = F.nll_loss(input_mps, target_mps, reduction=reduction)
+        # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095
+        self.assertEqualIgnoreType(output_cpu, output_mps.to('cpu'))
+
+        output_cpu.sum().backward()
+        output_mps.sum().backward()
+        self.assertEqual(input.grad, input_mps.grad.to('cpu'))
+
     def test_as_strided(self):
         values = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
         values_1 = [[1.0, 1.0], [1.0, 1.0]]
@@ -1743,6 +1763,11 @@
 
         helper(3, 3)
 
+    def test_nll_loss_1d(self, device='cpu'):
+        self._nll_loss_1d_helper([10], "none")
+        self._nll_loss_1d_helper([10], "mean")
+        self._nll_loss_1d_helper([10], "sum")
+
     def test_nll_loss_empty_tensor_reduction_none(self, device='cpu'):
         self._nll_loss_helper([1, 3], "none", torch.empty([0], device=device))
         self._nll_loss_helper([3, 5, 7], "none", torch.empty([5, 7], device=device))