Fixes

Author: Alejandro Gaston Alvarez Franceschi

coremltools/converters/mil/frontend/torch/test/test_torch_ops.py

@@ -9504,9 +9504,8 @@ def test_stft(self, compute_unit, backend, input_shape, complex, n_fft, hop_leng
         class STFTModel(torch.nn.Module):
             def forward(self, x):
                 applied_window = window(win_length) if window and win_length else None
-                x = torch.complex(x, x) if complex else x
                 x = torch.stft(
-                    x,
+                    torch.complex(x, x) if complex else x,
                     n_fft=n_fft,
                     hop_length=hop_length,
                     win_length=win_length,
@@ -9534,28 +9533,26 @@ class TestISTFT(TorchBaseTest):
             compute_units,
             backends,
             [(1, 32, 9), (32, 9), (3, 32, 9)], # input shape
-            [False, True], # complex
             [16], # n_fft
             [None, 4, 5], # hop_length
             [None, 16, 9], # win_length
             [None, torch.hann_window], # window
             [None, False, True], # center
-            ["constant", "reflect", "replicate"], # pad mode
             [False, True], # normalized
             [None, False, True], # onesided
             [None, 60], # length
+            [False, True], # return_complex
         )
     )
-    def test_istft(self, compute_unit, backend, input_shape, complex, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided):
-        if complex and onesided:
-            pytest.skip("Onesided stft not possible for complex inputs")
+    def test_istft(self, compute_unit, backend, input_shape, n_fft, hop_length, win_length, window, center, normalized, onesided, length, return_complex):
+        if return_complex and onesided:
+            pytest.skip("Complex output is incompatible with onesided")
 
         class ISTFTModel(torch.nn.Module):
             def forward(self, x):
                 applied_window = window(win_length) if window and win_length else None
-                x = torch.complex(x, x)
                 x = torch.istft(
-                    x,
+                    torch.complex(x, x),
                     n_fft=n_fft,
                     hop_length=hop_length,
                     win_length=win_length,
@@ -9564,8 +9561,9 @@ def forward(self, x):
                     normalized=normalized,
                     onesided=onesided,
                     length=length,
-                    return_complex=True)
-                x = torch.stack([torch.real(x), torch.imag(x)], dim=0)
+                    return_complex=return_complex)
+                if return_complex:
+                    x = torch.stack([torch.real(x), torch.imag(x)], dim=0)
                 return x
 
         TorchBaseTest.run_compare_torch(

coremltools/converters/mil/mil/input_type.py

@@ -251,7 +251,7 @@ class TensorInputType(_InputType):
         class conv(Operation):
             input_spec = InputSpec(
                 x=TensorInputType(type_domain="T"),
-                weight=TensorInputType(type_domain="U"),
+                weight=TensorInputType(type_domain="T"),
             )
 
             type_domains = {

coremltools/converters/mil/mil/ops/defs/complex_dialect_ops.py

@@ -893,6 +893,7 @@ class complex_istft(Operation):
 
     Attributes
     ----------
+    V: complex64
     T: fp32, complex64
 
     References
@@ -901,7 +902,7 @@ class complex_istft(Operation):
     """
 
     input_spec = InputSpec(
-        input=TensorInputType(type_domain="T"),
+        input=TensorInputType(type_domain="V"),
         n_fft=TensorInputType(const=True, type_domain=types.int32),
         hop_length=TensorInputType(const=True, optional=True, type_domain=types.int32),
         win_length=TensorInputType(const=True, optional=True, type_domain=types.int32),
@@ -912,7 +913,7 @@ class complex_istft(Operation):
     )
 
     type_domains = {
-        "T": (types.fp32, types.complex64),
+        "V": types.complex64,
     }
 
     def default_inputs(self):
@@ -937,7 +938,6 @@ def type_inference(self):
             output_shape += [self.length]
             return types.tensor(output_type, tuple(output_shape))
 
-
         n_frames = self.input.shape[-1]
         output_shape = self.n_fft.val + self.hop_length.val * (n_frames - 1)
 

coremltools/converters/mil/mil/passes/defs/lower_complex_dialect_ops.py

@@ -325,7 +325,7 @@ def _stft(
     We can write STFT in terms of convolutions with a DFT kernel.
     At the end:
         * The real part output is: cos_base * input_real + sin_base * input_imag
-        * The imaginary part output is: - (sin_base * input_real - cos_base * input_imag)
+        * The imaginary part output is: cos_base * input_imag - sin_base * input_real
     Adapted from: https://github.com/adobe-research/convmelspec/blob/main/convmelspec/mil.py
     """
     hop_length = hop_length or mb.floor_div(x=n_fft, y=4, before_op=before_op)
@@ -358,12 +358,13 @@ def _stft(
     if input_imaginary:
         signal_imaginary = mb.expand_dims(x=input_imaginary, axes=(1,), before_op=before_op)
 
-    # conv with DFT kernel across the input signal
-    # The DFT matrix is obtained with the equation e^(2pi/N i) but the definition is:
-    # DFT(x) => X[k] = Σx[n]*e^(-2kpi/N i)
-    # If x is complex then x[n]=(a+i*b)
-    # So the real part = Σ(a*cos(2kpi/N)+b*sin(2kpi/N))
-    # So the imag part = Σ(b*cos(2kpi/N)-a*sin(2kpi/N))
+    # Convolve the DFT kernel with the input signal
+    # DFT(x[n]) --> X[k] = Σx[n]*e^(-2π*n/N*k), then if x is complex x[n]=(a[n]+i*b[n])
+    #   real(X[k]) = Σ(a[n]*cos(2π*n/N*k)+b[n]*sin(2π*n/N*k))
+    #   imag(X[k]) = Σ(b[n]*cos(2π*n/N*k)-a[n]*sin(2π*n/N*k))
+    # But because our DFT matrix is obtained with the conjugate --> e^(2π*n/N*k):
+    #   real(X[k]) = Σ(a[n]*cos(2π*n/N*k)-b[n]*sin(2π*n/N*k))
+    #   imag(X[k]) = Σ(b[n]*cos(2π*n/N*k)+a[n]*sin(2π*n/N*k))
     cos_windows_real = mb.conv(x=signal_real, weight=cos_base, strides=hop_size, pad_type='valid', before_op=before_op)
     sin_windows_real = mb.conv(x=signal_real, weight=sin_base, strides=hop_size, pad_type='valid', before_op=before_op)
     if input_imaginary:
@@ -372,11 +373,11 @@ def _stft(
 
     # add everything together
     if input_imaginary:
-        real_result = mb.add(x=cos_windows_real, y=sin_windows_imag, before_op=before_op)
-        imag_result = mb.sub(x=cos_windows_imag, y=sin_windows_real, before_op=before_op)
+        real_result = mb.sub(x=cos_windows_real, y=sin_windows_imag, before_op=before_op)
+        imag_result = mb.add(x=cos_windows_imag, y=sin_windows_real, before_op=before_op)
     else:
         real_result = cos_windows_real
-        imag_result = mb.sub(x=0., y=sin_windows_real, before_op=before_op)
+        imag_result = sin_windows_real
 
     # reduce the rank of the output
     if should_increase_rank:
@@ -417,10 +418,10 @@ def _istft(
     # By default, use the entire frame
     win_length = win_length or n_fft
 
-    input_shape = mb.shape(x=x, before_op=before_op)
+    input_shape = mb.shape(x=input_real, before_op=before_op)
     n_frames = input_shape.val[-1]
     fft_size = input_shape.val[-2]
-    # expected_output_signal_len = n_fft.val + hop_length.val * (n_frames - 1)
+    expected_output_signal_len = n_fft.val + hop_length.val * (n_frames - 1)
 
     is_onesided = onesided.val if onesided else fft_size != n_fft
     cos_base, sin_base = _calculate_dft_matrix(n_fft, onesided=is_onesided, before_op=before_op)
@@ -447,14 +448,13 @@ def _istft(
         signal_real = mb.mul(x=signal_real, y=multiplier, before_op=before_op)
         signal_imaginary = mb.mul(x=signal_imaginary, y=multiplier, before_op=before_op)
 
-    # Conv with DFT kernel across the input signal
-    # We can describe the IDFT in terms of DFT just by swapping the input and output
+    # Convolve the DFT kernel with the input signal
+    # We can describe the IDFT in terms of DFT just by swapping the input and output.
     # ref: https://en.wikipedia.org/wiki/Discrete_Fourier_transform#Expressing_the_inverse_DFT_in_terms_of_the_DFT
-    # So IDFT(x) = (1/N) * swap(DFT(swap(x)))
-    # and DFT(x) = X[k] = Σx[n]*e^(-2kpi/N i) but we are using the conjugate e^(2kpi/N i)
-    # If x is complex then x[n]=(a+i*b)
-    # then real part = (1/N)*Σ(a*cos(2kpi/N)+b*sin(2kpi/N))
-    # then imag part = (1/N)*Σ(b*cos(2kpi/N)-a*sin(2kpi/N))
+    # IDFT(X[K]) --> x[n]=(1/N)*swap(DFT(swap(X[k]))), and K=N
+    # So using the definition in stft function, we get:
+    #   real(x[n]) = Σ(a[k]*cos(2π*k/K*n)+b[k]*sin(2π*k/K*n))
+    #   imag(x[n]) = Σ(b[k]*cos(2π*k/K*n)-a[k]*sin(2π*k/K*n))
     cos_windows_real = mb.conv(x=signal_real, weight=cos_base, strides=hop_size, pad_type='valid', before_op=before_op)
     sin_windows_real = mb.conv(x=signal_real, weight=sin_base, strides=hop_size, pad_type='valid', before_op=before_op)
     cos_windows_imag = mb.conv(x=signal_imaginary, weight=cos_base, strides=hop_size, pad_type='valid', before_op=before_op)
@@ -750,17 +750,21 @@ def _lower_complex_istft(op: Operation):
     is_complex = types.is_complex(op.input.dtype)
 
     # check parameters for validity
+    if is_complex:
+        raise ValueError("Only complex inputs are allowed")
     if op.win_length and op.win_length.val > op.n_fft.val:
         raise ValueError("Window length must be less than or equal to n_fft")
-    if is_complex and op.onesided and op.onesided.val:
-        raise ValueError("Onesided is only valid for real inputs")
+    if op.return_complex and op.onesided and op.onesided.val:
+        raise ValueError("Complex output is not compatible with onesided")
 
     real, imag = _istft(
-        op.input.real if is_complex else op.input,
-        op.input.imag if is_complex else None,
-        op.n_fft, op.hop_length, op.win_length, op.window, op.normalized, op.onesided, before_op=op)
+        op.input.real, op.input.imag,
+        op.n_fft, op.hop_length, op.win_length, op.window, op.normalized, op.onesided, op.length, before_op=op)
 
-    return _wrap_complex_output(op.outputs[0], real, imag)
+    if op.return_complex:
+        return _wrap_complex_output(op.outputs[0], real, imag)
+    else
+        return real
 
 
 @LowerComplex.register_lower_func(op_type="complex_shape")