[Torchaudio] allow masked_value to be tensor (pytorch#4015)

Summary:
Pull Request resolved: pytorch#4015

Add support for mask_value to be tensor: for example, if we want to fill with mean values of featured.

when mask_value is tensor masked_fill creates additional D2D copy, rewriting with torch.where to avoid it

Differential Revision: D79451615

Author: ynonaolga

src/torchaudio/functional/functional.py

@@ -817,7 +817,7 @@ def _get_mask_param(mask_param: int, p: float, axis_length: int) -> int:
 def mask_along_axis_iid(
     specgrams: Tensor,
     mask_param: int,
-    mask_value: float,
+    mask_value: Union[float, Tensor],
     axis: int,
     p: float = 1.0,
 ) -> Tensor:
@@ -874,7 +874,12 @@ def mask_along_axis_iid(
 
     # Per batch example masking
     specgrams = specgrams.transpose(axis, -1)
-    specgrams = specgrams.masked_fill((mask >= mask_start) & (mask < mask_end), mask_value)
+    # this aims to avoid CPU-GPU sync from upstream
+    specgrams = (
+        torch.where((mask >= mask_start) & (mask < mask_end), mask_value.repeat(*specgrams.shape), specgrams)
+        if isinstance(mask_value, Tensor)
+        else specgrams.masked_fill((mask >= mask_start) & (mask < mask_end), mask_value)
+    )
     specgrams = specgrams.transpose(axis, -1)
 
     return specgrams

src/torchaudio/transforms/_transforms.py

@@ -1185,7 +1185,7 @@ def __init__(self, mask_param: int, axis: int, iid_masks: bool, p: float = 1.0)
         self.iid_masks = iid_masks
         self.p = p
 
-    def forward(self, specgram: Tensor, mask_value: float = 0.0) -> Tensor:
+    def forward(self, specgram: Tensor, mask_value: Union[float, torch.Tensor] = 0.0) -> Tensor:
         r"""
         Args:
             specgram (Tensor): Tensor of dimension `(..., freq, time)`.

test/torchaudio_unittest/functional/functional_impl.py

@@ -456,6 +456,18 @@ def test_mask_along_axis_iid(self, mask_param, mask_value, axis, p):
         assert mask_specgrams.size() == specgrams.size()
         assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel()
 
+    @parameterized.expand(list(itertools.product([100], [torch.tensor(0.0), torch.tensor(30.0)], [2, 3], [0.2, 1.0])))
+    def test_mask_along_axis_iid_mask_value(self, mask_param, mask_value, axis, p):
+        specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device)
+        torch.manual_seed(0)
+        # as this operation is random we need to fix the seed for results to match
+        mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis, p=p)
+        torch.manual_seed(0)
+        mask_specgrams_float = F.mask_along_axis_iid(specgrams, mask_param, mask_value.item(), axis, p=p)
+        assert torch.allclose(
+            mask_specgrams, mask_specgrams_float
+        ), f"Masking with float and tensor should be the same diff = {torch.abs(mask_specgrams - mask_specgrams_float).max()}"
+
     @parameterized.expand(list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0.0, 30.0], [1, 2])))
     def test_mask_along_axis_preserve(self, shape, mask_param, mask_value, axis):
         """mask_along_axis should not alter original input Tensor