Ensure correct encoding for non-contiguous WF

src/torchcodec/_core/Encoder.cpp

@@ -13,10 +13,8 @@ torch::Tensor validateWf(torch::Tensor wf) {
       wf.dtype() == torch::kFloat32,
       "waveform must have float32 dtype, got ",
       wf.dtype());
-  // TODO-ENCODING check contiguity of the input wf to ensure that it is indeed
-  // planar (fltp).
   TORCH_CHECK(wf.dim() == 2, "waveform must have 2 dimensions, got ", wf.dim());
-  return wf;
+  return wf.contiguous();
 }
 
 void validateSampleRate(const AVCodec& avCodec, int sampleRate) {

test/test_ops.py

@@ -1267,6 +1267,39 @@ def test_encode_to_tensor_long_output(self):
 
         torch.testing.assert_close(self.decode(encoded_tensor), samples)
 
+    def test_contiguity(self):
+        # Ensure that 2 waveforms with the same values are encoded in the same
+        # way, regardless of their memory layout. Here we encode 2 equal
+        # waveforms, one is row-aligned while the other is column-aligned.
+
+        num_samples = 10_000  # per channel
+        contiguous_samples = torch.rand(2, num_samples).contiguous()
+        assert contiguous_samples.stride() == (num_samples, 1)
+
+        encoded_from_contiguous = encode_audio_to_tensor(
+            wf=contiguous_samples,
+            sample_rate=16_000,
+            format="flac",
+            bit_rate=44_000,
+        )
+        non_contiguous_samples = contiguous_samples.T.contiguous().T
+        assert non_contiguous_samples.stride() == (1, 2)
+
+        torch.testing.assert_close(
+            contiguous_samples, non_contiguous_samples, rtol=0, atol=0
+        )
+
+        encoded_from_non_contiguous = encode_audio_to_tensor(
+            wf=non_contiguous_samples,
+            sample_rate=16_000,
+            format="flac",
+            bit_rate=44_000,
+        )
+
+        torch.testing.assert_close(
+            encoded_from_contiguous, encoded_from_non_contiguous, rtol=0, atol=0
+        )
+
 
 if __name__ == "__main__":
     pytest.main()