Run functional tests on GPU as well as CPU (#1475)

Author: msaroufim

test/torchaudio_unittest/functional/functional_cpu_test.py

@@ -16,18 +16,18 @@ def test_lfilter_9th_order_filter_stability(self):
         super().test_lfilter_9th_order_filter_stability()
 
 
-class TestFunctionalFloat64(Functional, FunctionalCPUOnly, PytorchTestCase):
+class TestFunctionalFloat64(Functional, PytorchTestCase):
     dtype = torch.float64
     device = torch.device('cpu')
 
 
-class TestFunctionalComplex64(FunctionalComplex, FunctionalCPUOnly, PytorchTestCase):
+class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase):
     complex_dtype = torch.complex64
     real_dtype = torch.float32
     device = torch.device('cpu')
 
 
-class TestFunctionalComplex128(FunctionalComplex, FunctionalCPUOnly, PytorchTestCase):
+class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase):
     complex_dtype = torch.complex128
     real_dtype = torch.float64
     device = torch.device('cpu')

test/torchaudio_unittest/functional/functional_impl.py

@@ -93,73 +93,17 @@ def test_spectogram_grad_at_zero(self, power):
         spec.sum().backward()
         assert not x.grad.isnan().sum()
 
-
-class FunctionalComplex(TestBaseMixin):
-    complex_dtype = None
-    real_dtype = None
-    device = None
-
-    @nested_params(
-        [0.5, 1.01, 1.3],
-        [True, False],
-    )
-    def test_phase_vocoder_shape(self, rate, test_pseudo_complex):
-        """Verify the output shape of phase vocoder"""
-        hop_length = 256
-        num_freq = 1025
-        num_frames = 400
-        batch_size = 2
-
-        torch.random.manual_seed(42)
-        spec = torch.randn(
-            batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device)
-        if test_pseudo_complex:
-            spec = torch.view_as_real(spec)
-
-        phase_advance = torch.linspace(
-            0,
-            np.pi * hop_length,
-            num_freq,
-            dtype=self.real_dtype, device=self.device)[..., None]
-
-        spec_stretch = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance)
-
-        assert spec.dim() == spec_stretch.dim()
-        expected_shape = torch.Size([batch_size, num_freq, int(np.ceil(num_frames / rate))])
-        output_shape = (torch.view_as_complex(spec_stretch) if test_pseudo_complex else spec_stretch).shape
-        assert output_shape == expected_shape
-
-
-class FunctionalCPUOnly(TestBaseMixin):
-    def test_create_fb_matrix_no_warning_high_n_freq(self):
-        with warnings.catch_warnings(record=True) as w:
-            warnings.simplefilter("always")
-            F.create_fb_matrix(288, 0, 8000, 128, 16000)
-        assert len(w) == 0
-
-    def test_create_fb_matrix_no_warning_low_n_mels(self):
-        with warnings.catch_warnings(record=True) as w:
-            warnings.simplefilter("always")
-            F.create_fb_matrix(201, 0, 8000, 89, 16000)
-        assert len(w) == 0
-
-    def test_create_fb_matrix_warning(self):
-        with warnings.catch_warnings(record=True) as w:
-            warnings.simplefilter("always")
-            F.create_fb_matrix(201, 0, 8000, 128, 16000)
-        assert len(w) == 1
-
     def test_compute_deltas_one_channel(self):
-        specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]])
-        expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]])
+        specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]], dtype=self.dtype, device=self.device)
+        expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]], dtype=self.dtype, device=self.device)
         computed = F.compute_deltas(specgram, win_length=3)
         self.assertEqual(computed, expected)
 
     def test_compute_deltas_two_channels(self):
         specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0],
-                                  [1.0, 2.0, 3.0, 4.0]]])
+                                  [1.0, 2.0, 3.0, 4.0]]], dtype=self.dtype, device=self.device)
         expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5],
-                                  [0.5, 1.0, 1.0, 0.5]]])
+                                  [0.5, 1.0, 1.0, 0.5]]], dtype=self.dtype, device=self.device)
         computed = F.compute_deltas(specgram, win_length=3)
         self.assertEqual(computed, expected)
 
@@ -190,7 +134,7 @@ def test_amplitude_to_DB_reversible(self, shape):
         db_mult = math.log10(max(amin, ref))
 
         torch.manual_seed(0)
-        spec = torch.rand(*shape) * 200
+        spec = torch.rand(*shape, dtype=self.dtype, device=self.device) * 200
 
         # Spectrogram amplitude -> DB -> amplitude
         db = F.amplitude_to_DB(spec, amplitude_mult, amin, db_mult, top_db=None)
@@ -218,7 +162,7 @@ def test_amplitude_to_DB_top_db_clamp(self, shape):
         # each spectrogram still need to be predictable. The max determines the
         # decibel cutoff, and the distance from the min must be large enough
         # that it triggers a clamp.
-        spec = torch.rand(*shape)
+        spec = torch.rand(*shape, dtype=self.dtype, device=self.device)
         # Ensure each spectrogram has a min of 0 and a max of 1.
         spec -= spec.amin([-2, -1])[..., None, None]
         spec /= spec.amax([-2, -1])[..., None, None]
@@ -245,7 +189,7 @@ def test_amplitude_to_DB_top_db_clamp(self, shape):
     )
     def test_complex_norm(self, shape, power):
         torch.random.manual_seed(42)
-        complex_tensor = torch.randn(*shape)
+        complex_tensor = torch.randn(*shape, dtype=self.dtype, device=self.device)
         expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2)
         norm_tensor = F.complex_norm(complex_tensor, power)
         self.assertEqual(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5)
@@ -255,7 +199,7 @@ def test_complex_norm(self, shape, power):
     )
     def test_mask_along_axis(self, shape, mask_param, mask_value, axis):
         torch.random.manual_seed(42)
-        specgram = torch.randn(*shape)
+        specgram = torch.randn(*shape, dtype=self.dtype, device=self.device)
         mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis)
 
         other_axis = 1 if axis == 2 else 2
@@ -271,7 +215,7 @@ def test_mask_along_axis(self, shape, mask_param, mask_value, axis):
     @parameterized.expand(list(itertools.product([100], [0., 30.], [2, 3])))
     def test_mask_along_axis_iid(self, mask_param, mask_value, axis):
         torch.random.manual_seed(42)
-        specgrams = torch.randn(4, 2, 1025, 400)
+        specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device)
 
         mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis)
 
@@ -282,3 +226,59 @@ 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()
+
+
+class FunctionalComplex(TestBaseMixin):
+    complex_dtype = None
+    real_dtype = None
+    device = None
+
+    @nested_params(
+        [0.5, 1.01, 1.3],
+        [True, False],
+    )
+    def test_phase_vocoder_shape(self, rate, test_pseudo_complex):
+        """Verify the output shape of phase vocoder"""
+        hop_length = 256
+        num_freq = 1025
+        num_frames = 400
+        batch_size = 2
+
+        torch.random.manual_seed(42)
+        spec = torch.randn(
+            batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device)
+        if test_pseudo_complex:
+            spec = torch.view_as_real(spec)
+
+        phase_advance = torch.linspace(
+            0,
+            np.pi * hop_length,
+            num_freq,
+            dtype=self.real_dtype, device=self.device)[..., None]
+
+        spec_stretch = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance)
+
+        assert spec.dim() == spec_stretch.dim()
+        expected_shape = torch.Size([batch_size, num_freq, int(np.ceil(num_frames / rate))])
+        output_shape = (torch.view_as_complex(spec_stretch) if test_pseudo_complex else spec_stretch).shape
+        assert output_shape == expected_shape
+
+
+class FunctionalCPUOnly(TestBaseMixin):
+    def test_create_fb_matrix_no_warning_high_n_freq(self):
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            F.create_fb_matrix(288, 0, 8000, 128, 16000)
+        assert len(w) == 0
+
+    def test_create_fb_matrix_no_warning_low_n_mels(self):
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            F.create_fb_matrix(201, 0, 8000, 89, 16000)
+        assert len(w) == 0
+
+    def test_create_fb_matrix_warning(self):
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            F.create_fb_matrix(201, 0, 8000, 128, 16000)
+        assert len(w) == 1