Separate CPU and GPU tests for functions torchscript test + Fix devices in two functionals (#528)

* Separate CPU and GPU tests for functions torchscript test

* fix indentation
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

torchaudio/functional.py

@@ -1210,13 +1210,16 @@ def mask_along_axis_iid(
     if axis != 2 and axis != 3:
         raise ValueError('Only Frequency and Time masking are supported')
 
-    value = torch.rand(specgrams.shape[:2]) * mask_param
-    min_value = torch.rand(specgrams.shape[:2]) * (specgrams.size(axis) - value)
+    device = specgrams.device
+    dtype = specgrams.dtype
+
+    value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * mask_param
+    min_value = torch.rand(specgrams.shape[:2], device=device, dtype=dtype) * (specgrams.size(axis) - value)
 
     # Create broadcastable mask
-    mask_start = (min_value.long())[..., None, None].float()
-    mask_end = (min_value.long() + value.long())[..., None, None].float()
-    mask = torch.arange(0, specgrams.size(axis)).float()
+    mask_start = min_value[..., None, None]
+    mask_end = (min_value + value)[..., None, None]
+    mask = torch.arange(0, specgrams.size(axis), device=device, dtype=dtype)
 
     # Per batch example masking
     specgrams = specgrams.transpose(axis, -1)
@@ -1298,6 +1301,8 @@ def compute_deltas(
         >>> delta = compute_deltas(specgram)
         >>> delta2 = compute_deltas(delta)
     """
+    device = specgram.device
+    dtype = specgram.dtype
 
     # pack batch
     shape = specgram.size()
@@ -1312,7 +1317,7 @@ def compute_deltas(
 
     specgram = torch.nn.functional.pad(specgram, (n, n), mode=mode)
 
-    kernel = (torch.arange(-n, n + 1, 1, device=specgram.device, dtype=specgram.dtype).repeat(specgram.shape[1], 1, 1))
+    kernel = torch.arange(-n, n + 1, 1, device=device, dtype=dtype).repeat(specgram.shape[1], 1, 1)
 
     output = torch.nn.functional.conv1d(specgram, kernel, groups=specgram.shape[1]) / denom
 
@@ -1431,7 +1436,7 @@ def _apply_probability_distribution(
         signal_scaled_dis = signal_scaled + gaussian
     else:
         # dtype needed for https://github.com/pytorch/pytorch/issues/32358
-        TPDF = torch.bartlett_window(time_size + 1, dtype=torch.float)
+        TPDF = torch.bartlett_window(time_size + 1, dtype=signal_scaled.dtype, device=signal_scaled.device)
         TPDF = TPDF.repeat((channel_size + 1), 1)
         signal_scaled_dis = signal_scaled + TPDF