Adds FFT Auto Scale Op (#2134)

This PR adds additional FFT op functionality in the Signal library, namely adding the FFT Auto Scale operation.
Testing added in the original `fft_test.cc` and `fft_ops_test.py`.
BUG=[287346710](http://b/287346710)

Author: suleshahid

python/tflite_micro/python_ops_resolver.cc

@@ -51,6 +51,7 @@ PythonOpsResolver::PythonOpsResolver() {
   AddEthosU();
   AddExp();
   AddExpandDims();
+  AddFftAutoScale();
   AddFill();
   AddFloor();
   AddFloorDiv();

python/tflite_micro/signal/BUILD

@@ -46,6 +46,10 @@ py_tflm_signal_library(
 py_test(
     name = "fft_ops_test",
     srcs = ["ops/fft_ops_test.py"],
+    data = [
+        "//python/tflite_micro/signal/ops/testdata:fft_auto_scale_test1.txt",
+        "//python/tflite_micro/signal/ops/testdata:rfft_test1.txt",
+    ],
     python_version = "PY3",
     srcs_version = "PY3",
     deps = [

python/tflite_micro/signal/ops/fft_ops.py

@@ -65,5 +65,22 @@ def _fft(input_tensor, fft_length, name=default_name):
   return _fft
 
 
+def _fft_auto_scale_wrapper(fft_auto_scale_fn, default_name):
+  """Wrapper around gen_fft_ops.fft_auto_scale*."""
+
+  def _fft_auto_scale(input_tensor, name=default_name):
+    with tf.name_scope(name) as name:
+      input_tensor = tf.convert_to_tensor(input_tensor, dtype=tf.int16)
+      dim_list = input_tensor.shape.as_list()
+      if len(dim_list) != 1:
+        raise ValueError("Input tensor must have a rank of 1")
+      return fft_auto_scale_fn(input_tensor, name=name)
+
+  return _fft_auto_scale
+
+
 rfft = _fft_wrapper(gen_fft_ops.signal_rfft, "signal_rfft")
+fft_auto_scale = _fft_auto_scale_wrapper(gen_fft_ops.signal_fft_auto_scale,
+                                         "signal_fft_auto_scale")
 tf.no_gradient("signal_rfft")
+tf.no_gradient("signal_fft_auto_scale")

python/tflite_micro/signal/ops/fft_ops_test.py

@@ -33,6 +33,31 @@ def GetResource(self, filepath):
       file_text = f.read()
     return file_text
 
+  def SingleFftAutoScaleTest(self, filename):
+    lines = self.GetResource(filename).splitlines()
+    func = tf.function(fft_ops.fft_auto_scale)
+    input_size = len(lines[0].split())
+    concrete_function = func.get_concrete_function(
+        tf.TensorSpec(input_size, dtype=tf.int16))
+    interpreter = util.get_tflm_interpreter(concrete_function, func)
+    i = 0
+    while i < len(lines):
+      in_frame = np.array([int(j) for j in lines[i].split()], dtype=np.int16)
+      out_frame_exp = [int(j) for j in lines[i + 1].split()]
+      scale_exp = [int(j) for j in lines[i + 2].split()]
+      # TFLM
+      interpreter.set_input(in_frame, 0)
+      interpreter.invoke()
+      out_frame = interpreter.get_output(0)
+      scale = interpreter.get_output(1)
+      self.assertAllEqual(out_frame_exp, out_frame)
+      self.assertEqual(scale_exp, scale)
+      # TF
+      out_frame, scale = self.evaluate(fft_ops.fft_auto_scale(in_frame))
+      self.assertAllEqual(out_frame_exp, out_frame)
+      self.assertEqual(scale_exp, scale)
+      i += 3
+
   def SingleRfftTest(self, filename):
     lines = self.GetResource(filename).splitlines()
     args = lines[0].split()
@@ -43,8 +68,6 @@ def SingleRfftTest(self, filename):
         tf.TensorSpec(input_size, dtype=tf.int16), fft_length)
     # TODO(b/286252893): make test more robust (vs scipy)
     interpreter = util.get_tflm_interpreter(concrete_function, func)
-    input_details = interpreter.get_input_details()
-    output_details = interpreter.get_output_details()
     # Skip line 0, which contains the configuration params.
     # Read lines in pairs <input, expected>
     i = 1
@@ -53,9 +76,9 @@ def SingleRfftTest(self, filename):
       out_frame_exp = [int(j) for j in lines[i + 1].split()]
       # Compare TFLM inference against the expected golden values
       # TODO(b/286252893): validate usage of testing vs interpreter here
-      interpreter.set_tensor(input_details[0]['index'], in_frame)
+      interpreter.set_input(in_frame, 0)
       interpreter.invoke()
-      out_frame = interpreter.get_tensor(output_details[0]['index'])
+      out_frame = interpreter.get_output(0)
       self.assertAllEqual(out_frame_exp, out_frame)
       # TF
       out_frame = self.evaluate(fft_ops.rfft(in_frame, fft_length))
@@ -83,11 +106,9 @@ def MultiDimRfftTest(self, filename):
     concrete_function = func.get_concrete_function(
         tf.TensorSpec(np.shape(in_frames), dtype=tf.int16), fft_length)
     interpreter = util.get_tflm_interpreter(concrete_function, func)
-    input_details = interpreter.get_input_details()
-    output_details = interpreter.get_output_details()
-    interpreter.set_tensor(input_details[0]['index'], in_frames)
+    interpreter.set_input(in_frames, 0)
     interpreter.invoke()
-    out_frame = interpreter.get_tensor(output_details[0]['index'])
+    out_frame = interpreter.get_output(0)
     self.assertAllEqual(out_frames_exp, out_frame)
     # TF
     out_frames = self.evaluate(fft_ops.rfft(in_frames, fft_length))
@@ -204,6 +225,12 @@ def testRfftSineTest(self):
                                  delta=1)
       fft_length = 2 * fft_length
 
+  def testRfft(self):
+    self.SingleRfftTest('testdata/rfft_test1.txt')
+
+  def testRfftLargeOuterDimension(self):
+    self.MultiDimRfftTest('testdata/rfft_test1.txt')
+
   def testFftTooLarge(self):
     for dtype in [np.int16, np.int32, np.float32]:
       fft_input = np.zeros(round(fft_ops._MAX_FFT_LENGTH * 2), dtype=dtype)
@@ -224,6 +251,9 @@ def testFftLengthNoEven(self):
       with self.assertRaises((tf.errors.InvalidArgumentError, ValueError)):
         self.evaluate(fft_ops.rfft(fft_input, 127))
 
+  def testAutoScale(self):
+    self.SingleFftAutoScaleTest('testdata/fft_auto_scale_test1.txt')
+
   def testPow2FftLengthTest(self):
     fft_length, fft_bits = fft_ops.get_pow2_fft_length(131)
     self.assertEqual(fft_length, 256)

python/tflite_micro/signal/ops/testdata/BUILD

@@ -7,6 +7,7 @@ package(
 )
 
 exports_files([
+    "fft_auto_scale_test1.txt",
     "rfft_test1.txt",
     "window_test1.txt",
 ])