Fix some comments and specifications for int8 quantization.

The cl made the following changes:
- add int8 to all the related argument comments
- when the "inference_type" is int8, grappler optimization is disabled
- use "inference_type", instead of "inference_input_type" to verify quant stats is specified when it is not post-training quantization.

PiperOrigin-RevId: 285229735
Change-Id: Ie8da5c4d79fb60100c1041bd4573fe603cd304e6

tensorflow/lite/python/convert.py

@@ -255,10 +255,10 @@ def build_toco_convert_protos(input_tensors,
       `foo.shape` and `foo.dtype`.
     output_tensors: List of output tensors (only .name is used from this).
     inference_type: Target data type of real-number arrays in the output file.
-      Must be `{tf.float32, tf.uint8}`.  (default tf.float32)
-      Must be `{tf.float32, tf.uint8}`. (default `inference_type`)
+      Must be `{tf.float32, tf.uint8, tf.int8}`.  (default tf.float32)
     inference_input_type: Target data type of real-number input arrays. Allows
       for a different type for input arrays in the case of quantization.
+      Must be `{tf.float32, tf.uint8, tf.int8}`. (default `inference_type`)
     input_format: Type of data to read Currently must be
       `{TENSORFLOW_GRAPHDEF}`. (default TENSORFLOW_GRAPHDEF)
     input_shapes: Input array shape. It needs to be a list of the same length
@@ -267,7 +267,7 @@ def build_toco_convert_protos(input_tensors,
       GRAPHVIZ_DOT}`. (default TFLITE)
     quantized_input_stats: List of tuples of floats representing the mean and
       standard deviation. Each tuple maps to the corresponding input tensor.
-      Only need if `inference_input_type` is `QUANTIZED_UINT8`.
+      Only need if `inference_input_type` is `QUANTIZED_UINT8` or `INT8`.
       real_input_value = (quantized_input_value - mean_value) / std_dev_value.
       (default None)
     default_ranges_stats: Tuple of integers representing (min, max) range values
@@ -363,11 +363,10 @@ def build_toco_convert_protos(input_tensors,
     input_array.data_type = util.convert_dtype_to_tflite_type(
         input_tensor.dtype)
 
-    if toco.inference_input_type in \
-        [_types_pb2.QUANTIZED_UINT8, _types_pb2.INT8]:
-      if not quantized_input_stats:
-        raise ValueError("std_dev and mean must be defined when "
-                         "inference_input_type is QUANTIZED_UINT8.")
+    if toco.inference_type in [_types_pb2.QUANTIZED_UINT8, _types_pb2.INT8]:
+      if not quantized_input_stats and not post_training_quantize:
+        raise ValueError("std_dev and mean must be defined when inference_type "
+                         "is QUANTIZED_UINT8 or INT8.")
       input_array.mean_value, input_array.std_value = quantized_input_stats[idx]
     if input_shapes is None:
       shape = input_tensor.shape
@@ -418,11 +417,13 @@ def toco_convert_graph_def(input_data, input_arrays_with_shape, output_arrays,
 
   for idx, (name, shape) in enumerate(input_arrays_with_shape):
     input_array = model_flags.input_arrays.add()
-    if toco_flags.inference_input_type == _types_pb2.QUANTIZED_UINT8:
-      if (("quantized_input_stats" not in kwargs) or
-          (not kwargs["quantized_input_stats"])):
+    if toco_flags.inference_type in (
+        [_types_pb2.QUANTIZED_UINT8, _types_pb2.INT8]):
+      if ((("quantized_input_stats" not in kwargs) or
+           (not kwargs["quantized_input_stats"])) and
+          not toco_flags.post_training_quantize):
         raise ValueError("std_dev and mean must be defined when "
-                         "inference_input_type is QUANTIZED_UINT8.")
+                         "inference_type is QUANTIZED_UINT8 or INT8.")
       input_array.mean_value, input_array.std_value = kwargs[
           "quantized_input_stats"][idx]
     input_array.name = name

tensorflow/lite/python/convert_test.py

@@ -76,8 +76,17 @@ class ConvertTest(test_util.TensorFlowTestCase):
           sess.graph_def, [in_tensor], [out_tensor],
           inference_type=lite_constants.QUANTIZED_UINT8)
     self.assertEqual(
-        "std_dev and mean must be defined when inference_input_type is "
-        "QUANTIZED_UINT8.", str(error.exception))
+        "std_dev and mean must be defined when inference_type is "
+        "QUANTIZED_UINT8 or INT8.", str(error.exception))
+
+    with self.assertRaises(ValueError) as error:
+      convert.toco_convert(
+          sess.graph_def, [in_tensor], [out_tensor],
+          inference_type=lite_constants.QUANTIZED_UINT8,
+          inference_input_type=lite_constants.FLOAT)
+    self.assertEqual(
+        "std_dev and mean must be defined when inference_type is "
+        "QUANTIZED_UINT8 or INT8.", str(error.exception))
 
   def testGraphDefBasic(self):
     with ops.Graph().as_default():
@@ -176,8 +185,8 @@ class ConvertTest(test_util.TensorFlowTestCase):
           enable_mlir_converter=False,
           inference_type=lite_constants.QUANTIZED_UINT8)
     self.assertEqual(
-        "std_dev and mean must be defined when inference_input_type is "
-        "QUANTIZED_UINT8.", str(error.exception))
+        "std_dev and mean must be defined when inference_type is "
+        "QUANTIZED_UINT8 or INT8.", str(error.exception))
 
 
 class ConvertTestOpHint(test_util.TensorFlowTestCase):

tensorflow/lite/python/lite.py

@@ -998,7 +998,12 @@ class TFLiteConverter(TFLiteConverterBase):
           "are not enabled.")
 
     optimized_graph = self._graph_def
-    if self.inference_type != constants.QUANTIZED_UINT8:
+    # if it is not uint8 or int8 with post-training quantization, it is not
+    # quantization aware training, then graph optimization is applied.
+    # Graph optimization is disabled for quantization aware training.
+    if (self.inference_type != constants.QUANTIZED_UINT8 or
+        (self.inference_type == constants.INT8 and
+         (post_training_optimize or weight_only_quantize))):
       try:
         optimized_graph = _run_graph_optimizations(
             self._graph_def,

tensorflow/lite/python/lite_v2_test.py

@@ -787,6 +787,12 @@ class GrapplerTest(TestModels):
     actual_value = self._evaluateTFLiteModel(tflite_model, [input_data])
     np.testing.assert_almost_equal(expected_value.numpy(), actual_value[0])
 
+    # Enable hybrid quantization, same result
+    converter.experimental_new_converter = True
+    converter.optimizations = [lite.Optimize.DEFAULT]
+    hybrid_tflite_model = converter.convert()
+    actual_value = self._evaluateTFLiteModel(hybrid_tflite_model, [input_data])
+    np.testing.assert_almost_equal(expected_value.numpy(), actual_value[0])
 
 if __name__ == '__main__':
   test.main()