[TFLite] Added check for dynamic range quantization (apache#7114)

* [TFLite] Added check for dynamic range quantization

Added check to prevent optimized with "dynamic range quantization"
tflite files to be loaded as the optimization is not fully supported.

https://www.tensorflow.org/lite/performance/post_training_quantization#dynamic_range_quantization

* linter

* linter

* unit test fix

python/tvm/relay/frontend/tflite.py

@@ -176,17 +176,45 @@ def __init__(self, model, subgraph, exp_tab):
     def check_unsupported_ops(self):
         """Check unsupported TFLite ops in our converter."""
         unsupported_ops_set = set()
-
+        dynamic_range_ops_set = set()
         for op_idx in range(self.subgraph.OperatorsLength()):
             op = self.subgraph.Operators(op_idx)
             op_code_str = self.get_op_code_str(op)
             if op_code_str not in self.convert_map:
                 unsupported_ops_set.add(op_code_str)
+                continue
+
+            # Trying to exclude "dynamic range quantization" optimized ops as not supported in TVM
+            qnn_in_cnt = len(
+                [_.qnn_params for _ in self.get_input_tensors(op)[0:1] if _.qnn_params is not None]
+            )
+            qnn_weight_cnt = len(
+                [_.qnn_params for _ in self.get_input_tensors(op)[1:] if _.qnn_params is not None]
+            )
+            qnn_out_cnt = len(
+                [_.qnn_params for _ in self.get_output_tensors(op) if _.qnn_params is not None]
+            )
+
+            if qnn_in_cnt == 0 and qnn_out_cnt == 0 and qnn_weight_cnt > 0:
+                dynamic_range_ops_set.add(op_code_str)
+
+        raise_msg = ""
 
         if unsupported_ops_set:
-            msg = "The following operators are not supported in frontend " "TFLite: {}"
+            msg = "The following operators are not supported in frontend " "TFLite: {}\n"
             ops = str(list(unsupported_ops_set)).strip("[,]")
-            raise tvm.error.OpNotImplemented(msg.format(ops))
+            raise_msg += msg.format(ops)
+
+        if dynamic_range_ops_set:
+            msg = (
+                "The following operators are likely to have dynamic range quantization: {}. "
+                "If you are running an optimized graph, please turn off dynamic range quantization "
+                "or use full integer quantization"
+            )
+            raise_msg += msg.format(str(list(dynamic_range_ops_set)).strip("[,]"))
+
+        if len(raise_msg) > 0:
+            raise tvm.error.OpNotImplemented(raise_msg)
 
     def convert_op_to_relay(self):
         """Convert TFLite ops to relay ops"""

tests/python/frontend/tflite/test_forward.py

@@ -4156,6 +4156,27 @@ def test_forward_mediapipe_hand_landmark():
         )
 
 
+#######################################################################
+# Test check for Tensorflow "dynamic range quantization" optimization
+# --------------
+def test_prevent_tensorflow_dynamic_range():
+    """
+    Should prevent runnung "dynamic range quantization" optimized TFLite graph
+    """
+    data_array = np.random.randint(0, 2, (1, 1024, 1024)).astype(dtype=np.float32)
+    filter_array = np.random.randint(0, 2, (1024, 1024)).astype(dtype=np.float32)
+    data_in = tf.keras.layers.Input(shape=data_array.shape[1:])
+    dense = tf.keras.layers.Dense(units=filter_array.shape[-1], use_bias=False)(data_in)
+    keras_model = tf.keras.models.Model(data_in, dense)
+    keras_model.layers[1].set_weights([filter_array])
+
+    converter = interpreter_wrapper.TFLiteConverter.from_keras_model(keras_model)
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    tflite_model = converter.convert()
+    with pytest.raises(tvm.error.OpNotImplemented):
+        tvm_output = run_tvm_graph(tflite_model, data_array, data_in.name.replace(":0", ""))
+
+
 #######################################################################
 # Main
 # ----