diff --git a/aten/src/ATen/native/cuda/GridSampler.cu b/aten/src/ATen/native/cuda/GridSampler.cu
index e65f6d59c7d3..384658e75187 100644
--- a/aten/src/ATen/native/cuda/GridSampler.cu
+++ b/aten/src/ATen/native/cuda/GridSampler.cu
@@ -58,8 +58,8 @@ namespace {
       const index_t grid_offset = n * grid_sN + h * grid_sH + w * grid_sW;
 
       // get the corresponding input x, y co-ordinates from grid
-      scalar_t x = grid.data[grid_offset];
-      scalar_t y = grid.data[grid_offset + grid_sCoor];
+      opmath_t x = grid.data[grid_offset];
+      opmath_t y = grid.data[grid_offset + grid_sCoor];
 
       opmath_t ix = grid_sampler_compute_source_index(x, inp_W, padding_mode, align_corners);
       opmath_t iy = grid_sampler_compute_source_index(y, inp_H, padding_mode, align_corners);
@@ -194,9 +194,9 @@ namespace {
       const index_t grid_offset = n * grid_sN + d * grid_sD + h * grid_sH + w * grid_sW;
 
       // get the corresponding input x, y, z co-ordinates from grid
-      scalar_t x = grid.data[grid_offset];
-      scalar_t y = grid.data[grid_offset + grid_sCoor];
-      scalar_t z = grid.data[grid_offset + 2 * grid_sCoor];
+      opmath_t x = grid.data[grid_offset];
+      opmath_t y = grid.data[grid_offset + grid_sCoor];
+      opmath_t z = grid.data[grid_offset + 2 * grid_sCoor];
 
       opmath_t ix = grid_sampler_compute_source_index(x, inp_W, padding_mode, align_corners);
       opmath_t iy = grid_sampler_compute_source_index(y, inp_H, padding_mode, align_corners);
diff --git a/test/test_nn.py b/test/test_nn.py
index 3e18244951b1..b883c97f9054 100644
--- a/test/test_nn.py
+++ b/test/test_nn.py
@@ -9954,10 +9954,11 @@ class TestNNDeviceType(NNTestCase):
                 (1, 1, 3, 3, 3))
             grid[:, 1, 1, 1, 0] = float('inf')
             result = torch.nn.functional.grid_sample(image, grid, padding_mode='zeros')
+            tol_override = {'atol': 0.005, 'rtol': 0} if dtype == torch.half else {}
             self.assertEqual(result, torch.tensor([[[[[27., 26., 25.], [24., 23., 22.], [21., 20., 19.]],
                                                      [[18., 17., 16.], [15., 0., 13.], [12., 11., 10.]],
                                                      [[9., 8., 7.], [6., 5., 4.], [3., 2., 1.]]]]],
-                                                  device=device, dtype=dtype))
+                                                  device=device, dtype=dtype), **tol_override)
             result.backward(torch.ones_like(result))
             expected_grad = torch.ones_like(image)
             expected_grad[0, 0, 1, 1, 1] = 0
@@ -10066,20 +10067,23 @@ class TestNNDeviceType(NNTestCase):
 
     @onlyCUDA
     def test_grid_sample_half_precision(self):
-        def helper(shape_in, shape_out):
+        def helper(shape_in, shape_out, align_corners):
             for mode in ('bilinear', 'nearest', 'bicubic'):
                 if len(shape_in) != 4 and mode == 'bicubic':
                     continue
                 data = torch.randn(shape_in, device='cuda', dtype=torch.half)
                 grid = torch.rand(shape_out, device='cuda', dtype=torch.half) * 2.0 - 1.0
 
-                out_half = F.grid_sample(data, grid, mode=mode, padding_mode='zeros', align_corners=False)
-                out_double = F.grid_sample(data.double(), grid.double(), mode=mode, padding_mode='zeros', align_corners=False)
+                out_half = F.grid_sample(data, grid, mode=mode, padding_mode='zeros', align_corners=align_corners)
+                out_double = F.grid_sample(data.double(), grid.double(), mode=mode, padding_mode='zeros',
+                                           align_corners=align_corners)
 
                 self.assertEqual(out_half, out_double.half(), msg="grid_sample with mode = {} doesn't match".format(mode))
 
-        helper((32, 64, 16, 16), (32, 8, 8, 2))
-        helper((32, 64, 16, 16, 16), (32, 8, 8, 8, 3))
+        helper((32, 64, 16, 16), (32, 8, 8, 2), True)
+        helper((32, 64, 16, 16, 16), (32, 8, 8, 8, 3), True)
+        helper((32, 64, 16, 16), (32, 8, 8, 2), False)
+        helper((32, 64, 16, 16, 16), (32, 8, 8, 8, 3), False)
 
     def _test_gumbel_softmax_st_shapes(self, device, dtype, shape, dim, count_expected):
         logits = torch.randn(shape, dtype=torch.float, device=device)