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PiperOrigin-RevId: 636683030

quantization_toolkit/transformation_instruction_generator.py

@@ -454,9 +454,50 @@ def _quant_params_to_transformation_insts(
     # Adding other consumers rules
     transformations += other_consumer_transformations
     tensor_trans_insts.instructions = transformations
+    # Check the generated transformation instructions are valid, the function
+    # will raise an error if the instructions are not valid
+    self._check_tensor_transformation_instructions_valid(tensor_trans_insts)
 
     return tensor_trans_insts
 
+  def _check_tensor_transformation_instructions_valid(
+      self, instructions: qtyping.TensorTransformationInsts
+  ):
+    """Check if the tensor transformation instructions are valid.
+
+    Args:
+      instructions: Transformation instructions for a tensor.
+
+    Raises:
+      ValueError: If the instructions are not valid.
+    """
+    is_tensor_unquantized = False
+    is_tensor_quantized = False
+    is_operator_emulated = False
+    for instruction in instructions.instructions:
+      transform_type = instruction.transformation
+      if transform_type == qtyping.QuantTransformation.NO_QUANTIZE:
+        is_tensor_unquantized = True
+      elif (
+          transform_type == qtyping.QuantTransformation.ADD_QUANTIZE
+          or transform_type == qtyping.QuantTransformation.QUANTIZE_TENSOR
+          or transform_type == qtyping.QuantTransformation.ADD_DEQUANTIZE
+      ):
+        is_tensor_quantized = True
+      elif transform_type == qtyping.QuantTransformation.EMULATED_SUBCHANNEL:
+        is_operator_emulated = True
+    if is_tensor_unquantized and is_tensor_quantized:
+      raise ValueError(
+          "Tensor %s can not be both quantized and unquantized"
+          % instructions.tensor_name
+      )
+    if is_operator_emulated and len(instructions.instructions) > 1:
+      raise ValueError(
+          "Tensor %s : op replacement transformation can not be combined with"
+          " other transformations."
+          % instructions.tensor_name
+      )
+
   def quant_params_to_transformation_insts(
       self,
       params: dict[str, qtyping.TensorTransformationParams],

quantization_toolkit/transformation_instruction_generator_test.py

@@ -822,6 +822,70 @@ def test_generate_instruction_for_single_fc_bias(self):
         instructions["StatefulPartitionedCall:0"], output_transformation
     )
 
+  def test_raise_error_on_op_replacement_transformation_is_not_unique(self):
+    test_model_path = os.path.join(
+        TEST_DATA_PREFIX_PATH, "test_models/insert_dequant_test.tflite"
+    )
+    quant_parameters = {}
+    quant_parameters["tfl.quantize"] = qtyping.TensorTransformationParams(
+        "tfl.quantize",
+        qtyping.OpToTensorParams(
+            subgraph_op_id=0,
+            transformations=[
+                qtyping.QuantTransformation.ADD_DEQUANTIZE,
+                qtyping.QuantTransformation.EMULATED_SUBCHANNEL,
+            ],
+            parameters=qtyping.UniformQuantParams(
+                8, None, np.array([1]), np.array([0])
+            ),
+        ),
+        [],
+    )
+    ins_gen = instruction_generator.TransformationInstructionsGenerator(
+        test_model_path
+    )
+    with self.assertRaisesRegex(
+        ValueError, "op replacement transformation can not be combined"
+    ):
+      ins_gen.quant_params_to_transformation_insts(quant_parameters)
+
+  def test_raise_error_on_no_quant_conflict(self):
+    test_model_path = os.path.join(
+        TEST_DATA_PREFIX_PATH, "test_models/insert_dequant_test.tflite"
+    )
+    quant_parameters = {}
+    quant_parameters["tfl.quantize"] = qtyping.TensorTransformationParams(
+        "tfl.quantize",
+        qtyping.OpToTensorParams(
+            subgraph_op_id=0,
+            transformations=[qtyping.QuantTransformation.ADD_DEQUANTIZE],
+            parameters=qtyping.UniformQuantParams(
+                8, None, np.array([1]), np.array([0])
+            ),
+        ),
+        [
+            qtyping.OpToTensorParams(
+                subgraph_op_id=1,
+                transformations=[qtyping.QuantTransformation.ADD_QUANTIZE],
+                parameters=qtyping.UniformQuantParams(
+                    8, None, np.array([1]), np.array([0])
+                ),
+            ),
+            qtyping.OpToTensorParams(
+                subgraph_op_id=2,
+                transformations=[qtyping.QuantTransformation.NO_QUANTIZE],
+                parameters=None,
+            ),
+        ],
+    )
+    ins_gen = instruction_generator.TransformationInstructionsGenerator(
+        test_model_path
+    )
+    with self.assertRaisesRegex(
+        ValueError, "can not be both quantized and unquantized"
+    ):
+      ins_gen.quant_params_to_transformation_insts(quant_parameters)
+
   def test_generate_instruction_for_branching(self):
     """test horizontal and vertial optimization on a graph with multi branch."""
     test_model_path = os.path.join(