Add test and fix impl

Author: moatom

onnxscript/function_libs/torch_lib/ops/core.py

@@ -8167,7 +8167,6 @@ def aten_stft(
         # hop_length = op.Div(op.Constant(value_ints=n_fft), op.Constant(value_ints=[4]))
         hop_length = n_fft // 4
     frame_step_const = op.Reshape(hop_length, op.Constant(value_ints=[1]))
-    frame_length_const = op.Reshape(n_fft, op.Constant(value_ints=[1]))
 
     # Pre-process input if needed
     is_signal_rank1 = len(self.shape) == 1
@@ -8203,7 +8202,7 @@ def aten_stft(
     else:
         onesided = 0
     window = op.CastLike(window, self)
-    result = op.STFT(self, frame_step_const, window, frame_length_const, onesided=onesided)
+    result = op.STFT(self, frame_step_const, window, n_fft, onesided=onesided)
     result = op.Transpose(result, perm=[0, 2, 1, 3])
     # Remove batch dimension, if needed
     if is_signal_rank1:

tests/function_libs/torch_lib/e2e_ops_tests.py

@@ -238,6 +238,75 @@ def forward(self, x):
         )
         _testing.assert_onnx_program(onnx_program)
 
+    def test_aten_stft_1(self):
+        class Model(torch.nn.Module):
+            def forward(self, x):
+                return torch.stft(x, n_fft=4, return_complex=True)
+
+        x = torch.randn(4, 16, dtype=torch.float32)
+
+        onnx_program = torch.onnx.export(
+            Model(),
+            (x,),
+            dynamo=True,
+            verbose=False,
+        )
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_aten_stft_2(self):
+        class Model(torch.nn.Module):
+            def forward(self, x):
+                return torch.stft(x, n_fft=4, return_complex=False)
+
+        x = torch.randn(4, 16, dtype=torch.float32)
+
+        onnx_program = torch.onnx.export(
+            Model(),
+            (x,),
+            dynamo=True,
+            verbose=False,
+        )
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_aten_stft_3(self):
+        class Model(torch.nn.Module):
+            def forward(self, x):
+                window = torch.ones(16, dtype=torch.float32)
+                return torch.stft(x, n_fft=16, window=window, return_complex=False)
+
+        x = torch.randn(100, dtype=torch.float32)
+
+        onnx_program = torch.onnx.export(
+            Model(),
+            (x,),
+            dynamo=True,
+            verbose=False,
+        )
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_aten_stft_4(self):
+        class Model(torch.nn.Module):
+            def forward(self, x):
+                return torch.stft(
+                    x,
+                    n_fft=4,
+                    hop_length=1,
+                    win_length=4,
+                    center=True,
+                    onesided=True,
+                    return_complex=True,
+                )
+
+        x = torch.randn(4, 16, dtype=torch.float32)
+
+        onnx_program = torch.onnx.export(
+            Model(),
+            (x,),
+            dynamo=True,
+            verbose=False,
+        )
+        _testing.assert_onnx_program(onnx_program)
+
 
 if __name__ == "__main__":
     unittest.main()