Ensure axis masking operations are not in-place (#1481)

It was reported in #1478 that spectrogram masking operations were done in-place and modified the original input tensors. This PR fixes this behavior and adds tests to ensure that the input tensor is not changed.

test/torchaudio_unittest/functional/functional_impl.py

@@ -227,6 +227,38 @@ def test_mask_along_axis_iid(self, mask_param, mask_value, axis):
         assert mask_specgrams.size() == specgrams.size()
         assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel()
 
+    @parameterized.expand(
+        list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0., 30.], [1, 2]))
+    )
+    def test_mask_along_axis_preserve(self, shape, mask_param, mask_value, axis):
+        """mask_along_axis should not alter original input Tensor
+
+        Test is run 5 times to bound the probability of no masking occurring to 1e-10
+        See https://github.com/pytorch/audio/issues/1478
+        """
+        torch.random.manual_seed(42)
+        for _ in range(5):
+            specgram = torch.randn(*shape, dtype=self.dtype, device=self.device)
+            specgram_copy = specgram.clone()
+            F.mask_along_axis(specgram, mask_param, mask_value, axis)
+
+            self.assertEqual(specgram, specgram_copy)
+
+    @parameterized.expand(list(itertools.product([100], [0., 30.], [2, 3])))
+    def test_mask_along_axis_iid_preserve(self, mask_param, mask_value, axis):
+        """mask_along_axis_iid should not alter original input Tensor
+
+        Test is run 5 times to bound the probability of no masking occurring to 1e-10
+        See https://github.com/pytorch/audio/issues/1478
+        """
+        torch.random.manual_seed(42)
+        for _ in range(5):
+            specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device)
+            specgrams_copy = specgrams.clone()
+            F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis)
+
+            self.assertEqual(specgrams, specgrams_copy)
+
 
 class FunctionalComplex(TestBaseMixin):
     complex_dtype = None

test/torchaudio_unittest/functional/torchscript_consistency_impl.py

@@ -13,16 +13,20 @@
 
 
 class Functional(TempDirMixin, TestBaseMixin):
-    """Implements test for `functinoal` modul that are performed for different devices"""
+    """Implements test for `functional` module that are performed for different devices"""
     def _assert_consistency(self, func, tensor, shape_only=False):
         tensor = tensor.to(device=self.device, dtype=self.dtype)
 
         path = self.get_temp_path('func.zip')
         torch.jit.script(func).save(path)
         ts_func = torch.jit.load(path)
 
+        torch.random.manual_seed(40)
         output = func(tensor)
+
+        torch.random.manual_seed(40)
         ts_output = ts_func(tensor)
+
         if shape_only:
             ts_output = ts_output.shape
             output = output.shape

torchaudio/functional/functional.py

@@ -746,7 +746,7 @@ def mask_along_axis_iid(
 
     # Per batch example masking
     specgrams = specgrams.transpose(axis, -1)
-    specgrams.masked_fill_((mask >= mask_start) & (mask < mask_end), mask_value)
+    specgrams = specgrams.masked_fill((mask >= mask_start) & (mask < mask_end), mask_value)
     specgrams = specgrams.transpose(axis, -1)
 
     return specgrams
@@ -772,24 +772,25 @@ def mask_along_axis(
     Returns:
         Tensor: Masked spectrogram of dimensions (channel, freq, time)
     """
+    if axis != 1 and axis != 2:
+        raise ValueError('Only Frequency and Time masking are supported')
 
     # pack batch
     shape = specgram.size()
     specgram = specgram.reshape([-1] + list(shape[-2:]))
-
     value = torch.rand(1) * mask_param
     min_value = torch.rand(1) * (specgram.size(axis) - value)
 
     mask_start = (min_value.long()).squeeze()
     mask_end = (min_value.long() + value.long()).squeeze()
+    mask = torch.arange(0, specgram.shape[axis], device=specgram.device, dtype=specgram.dtype)
+    mask = (mask >= mask_start) & (mask < mask_end)
+    if axis == 1:
+        mask = mask.unsqueeze(-1)
 
     assert mask_end - mask_start < mask_param
-    if axis == 1:
-        specgram[:, mask_start:mask_end] = mask_value
-    elif axis == 2:
-        specgram[:, :, mask_start:mask_end] = mask_value
-    else:
-        raise ValueError('Only Frequency and Time masking are supported')
+
+    specgram = specgram.masked_fill(mask, mask_value)
 
     # unpack batch
     specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:])