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Fix lite_tests for new quantizer

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MLIR quantizer test path is added for following tests.

* testQuantizeFloat16
* testIntegerQuantizationWithUnsupportedOps

PiperOrigin-RevId: 347932208
Change-Id: Iefed1daab7e364621c5f9f6d65a234b0eb85bf44
This commit is contained in:
Taehee Jeong 2020-12-16 17:51:01 -08:00 committed by TensorFlower Gardener
parent efa56e2e4b
commit 5ef02d3f57
2 changed files with 68 additions and 44 deletions
tensorflow/lite/python
lite_test.pylite_v2_test.py

74
tensorflow/lite/python/lite_test.py
View File

@ -312,10 +312,14 @@ class FromSessionTest(TestModels, parameterized.TestCase):
('_INT16Quantize_INT16InputOutput', False, True, dtypes.int16),
('_IntOnly_INT8InputOutput', True, False, dtypes.int8),
('_IntOnly_UINT8InputOutput', True, False, dtypes.uint8),
('_IntOnly_INT16Quantize_INT16InputOutput', True, True, dtypes.int16))
def testIntegerQuantizationWithUnsupportedOps(self, is_int_only,
('_IntOnly_INT16Quantize_INT16InputOutput', True, True, dtypes.int16),
('_IntOnly_INT8InputOutputMlirQuant', True, False, dtypes.int8, True),
('_IntOnly_UINT8InputOutputMlirQuant', True, False, dtypes.uint8, True))
def testIntegerQuantizationWithUnsupportedOps(self,
is_int_only,
is_int16_quantize,
inference_input_output_type):
inference_input_output_type,
enable_mlir_quantizer=False):
with ops.Graph().as_default():
in_tensor_a = array_ops.placeholder(shape=[3], dtype=dtypes.float32)
in_tensor_b = array_ops.placeholder(shape=[3], dtype=dtypes.float32)
@ -363,23 +367,25 @@ class FromSessionTest(TestModels, parameterized.TestCase):
quantized_converter.inference_input_type = inference_input_output_type
quantized_converter.inference_output_type = inference_input_output_type
quantized_converter._experimental_new_quantizer = enable_mlir_quantizer
quantized_tflite_model = quantized_converter.convert()
self.assertIsNotNone(quantized_tflite_model)
expected_dtype = inference_input_output_type.as_numpy_dtype
# Allow float32 for fallback on non-quantizable op.
expected_ceil_dtype = (
expected_dtype if enable_mlir_quantizer else dtypes.float32)
interpreter = Interpreter(model_content=quantized_tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertLen(input_details, 2)
# Allow float32 for fallback.
self.assertEqual(input_details[0]['dtype'], dtypes.float32)
self.assertEqual(input_details[1]['dtype'],
inference_input_output_type.as_numpy_dtype)
self.assertEqual(input_details[0]['dtype'], expected_ceil_dtype)
self.assertEqual(input_details[1]['dtype'], expected_dtype)
output_details = interpreter.get_output_details()
self.assertLen(output_details, 2)
# Allow float32 for fallback.
self.assertEqual(output_details[0]['dtype'], dtypes.float32)
self.assertEqual(output_details[1]['dtype'],
inference_input_output_type.as_numpy_dtype)
self.assertEqual(output_details[0]['dtype'], expected_ceil_dtype)
self.assertEqual(output_details[1]['dtype'], expected_dtype)
@parameterized.named_parameters(
('EnableMlirConverter', True), # enable mlir
@ -1108,27 +1114,35 @@ class FromSessionTest(TestModels, parameterized.TestCase):
@parameterized.named_parameters(
# Quantize to Float16 even if rep data provided.
('UseRepresentativeData', True, False, True, False, False, False),
('UseRepresentativeData', True, False, True, False, False, False, False),
# Quantize to Float16 if no rep data provided.
('NoRepresentativeData', False, False, True, False, False, False),
('NoRepresentativeData', False, False, True, False, False, False, False),
# Post training quantization if both rep data and int8 included.
('UseSampleDataIncludeInt8', True, True, False, False, True, False),
('UseSampleDataIncludeInt8', True, True, False, False, True, False, False
),
# Quantize to Float16 even if rep data provided with mlir.
('UseRepresentativeDataMlir', True, False, True, False, False, True),
('UseRepresentativeDataMlir', True, False, True, False, False, True, False
),
# Quantize to Float16 if no rep data provided with mlir.
('NoRepresentativeDataMlir', False, False, True, False, False, True),
('NoRepresentativeDataMlir', False, False, True, False, False, True, False
),
# Post training quantization if both rep data and int8 included with mlir.
('SampleDataIncludeInt8Mlir', True, True, False, False, True, True))
('SampleDataIncludeInt8Mlir', True, True, False, False, True, True, False
),
# Same as above, but using MLIR quantizer
('SampleDataIncludeInt8MlirQuant', True, True, False, False, True, True,
True))
def testQuantizeFloat16(self, use_rep_data, include_int8,
is_float16_quantized, is_error,
is_post_training_quantized, enable_mlir_converter):
is_post_training_quantized, enable_mlir_converter,
enable_mlir_quantizer):
with ops.Graph().as_default():
inp, output, calibration_gen = self._getIntegerQuantizeModel()
sess = session.Session()
idx = 1 if enable_mlir_converter else 0
node_name = 'Conv2D' if enable_mlir_converter else 'Conv2D_bias'
bias_idx = 1 if enable_mlir_converter else 0
bias_name = 'Conv2D' if enable_mlir_converter else 'Conv2D_bias'
# Convert float model.
float_converter = lite.TFLiteConverter.from_session(sess, [inp], [output])
float_converter.experimental_new_converter = enable_mlir_converter
@ -1136,13 +1150,20 @@ class FromSessionTest(TestModels, parameterized.TestCase):
self.assertIsNotNone(float_tflite_model)
interpreter = Interpreter(model_content=float_tflite_model)
interpreter.allocate_tensors()
self.assertEqual(interpreter.get_tensor_details()[idx]['name'], node_name)
self.assertEqual(interpreter.get_tensor_details()[idx]['dtype'],
self.assertEqual(interpreter.get_tensor_details()[bias_idx]['name'],
bias_name)
self.assertEqual(interpreter.get_tensor_details()[bias_idx]['dtype'],
dtypes.float32)
# MLIR quantizer has different bias index.
if enable_mlir_quantizer:
bias_idx = 2
# Convert model to quantized version
quantized_converter = lite.TFLiteConverter.from_session(
sess, [inp], [output])
quantized_converter.experimental_new_converter = enable_mlir_converter
quantized_converter._experimental_new_quantizer = enable_mlir_quantizer
quantized_converter.optimizations = [lite.Optimize.DEFAULT]
quantized_converter.target_spec.supported_types = [dtypes.float16]
if include_int8:
@ -1162,15 +1183,16 @@ class FromSessionTest(TestModels, parameterized.TestCase):
self.assertIsNotNone(quantized_tflite_model)
interpreter = Interpreter(model_content=quantized_tflite_model)
interpreter.allocate_tensors()
self.assertEqual(interpreter.get_tensor_details()[idx]['name'], node_name)
self.assertEqual(interpreter.get_tensor_details()[bias_idx]['name'],
bias_name)
if is_float16_quantized:
# Verify that bias constant is float16 type.
self.assertEqual(interpreter.get_tensor_details()[idx]['dtype'],
self.assertEqual(interpreter.get_tensor_details()[bias_idx]['dtype'],
dtypes.float16)
elif is_post_training_quantized:
# Verify that bias constants is int32 type.
self.assertEqual(interpreter.get_tensor_details()[idx]['dtype'],
self.assertEqual(interpreter.get_tensor_details()[bias_idx]['dtype'],
dtypes.int32)
else:
raise ValueError('Invalid test options.')

38
tensorflow/lite/python/lite_v2_test.py
View File

@ -314,9 +314,7 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
converter = lite.TFLiteConverterV2.from_concrete_functions([func])
# TODO(b/156309549): We should add INT16 to the builtin types.
converter.optimizations = [lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [
lite.OpsSet.TFLITE_BUILTINS_INT8
]
converter.target_spec.supported_ops = [lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.representative_dataset = calibration_gen
converter._experimental_calibrate_only = True
calibrated_tflite = converter.convert()
@ -610,11 +608,15 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
('_INT16Quantize_INT16InputOutput', False, True, dtypes.int16),
('_IntOnly_INT8InputOutput', True, False, dtypes.int8),
('_IntOnly_UINT8InputOutput', True, False, dtypes.uint8),
('_IntOnly_INT16Quantize_INT16InputOutput', True, True, dtypes.int16))
('_IntOnly_INT16Quantize_INT16InputOutput', True, True, dtypes.int16),
('_IntOnly_INT8InputOutputMlirQuant', True, False, dtypes.int8, True),
('_IntOnly_UINT8InputOutputMlirQuant', True, False, dtypes.uint8, True))
@test_util.run_v2_only
def testIntegerQuantizationWithUnsupportedOps(self, is_int_only,
def testIntegerQuantizationWithUnsupportedOps(self,
is_int_only,
is_int16_quantize,
inference_input_output_type):
inference_input_output_type,
enable_mlir_quantizer=False):
func, calib_gen = self._getIntegerQuantizationModelWithUnsupportedOps()
quantized_converter = tf.lite.TFLiteConverter.from_concrete_functions(
@ -646,23 +648,25 @@ class FromConcreteFunctionTest(lite_v2_test_util.ModelTest):
quantized_converter.inference_input_type = inference_input_output_type
quantized_converter.inference_output_type = inference_input_output_type
quantized_converter._experimental_new_quantizer = enable_mlir_quantizer
quantized_tflite_model = quantized_converter.convert()
self.assertIsNotNone(quantized_tflite_model)
expected_dtype = inference_input_output_type.as_numpy_dtype
# Allow float32 for fallback on non-quantizable op.
expected_ceil_dtype = (
expected_dtype if enable_mlir_quantizer else dtypes.float32)
interpreter = Interpreter(model_content=quantized_tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertLen(input_details, 2)
# Allow float32 for fallback.
self.assertEqual(input_details[0]['dtype'], dtypes.float32)
self.assertEqual(input_details[1]['dtype'],
inference_input_output_type.as_numpy_dtype)
self.assertEqual(input_details[0]['dtype'], expected_ceil_dtype)
self.assertEqual(input_details[1]['dtype'], expected_dtype)
output_details = interpreter.get_output_details()
self.assertLen(output_details, 2)
# Allow float32 for fallback.
self.assertEqual(output_details[0]['dtype'], dtypes.float32)
self.assertEqual(output_details[1]['dtype'],
inference_input_output_type.as_numpy_dtype)
self.assertEqual(output_details[0]['dtype'], expected_ceil_dtype)
self.assertEqual(output_details[1]['dtype'], expected_dtype)
class FromSavedModelTest(lite_v2_test_util.ModelTest):
@ -921,8 +925,7 @@ class FromSavedModelTest(lite_v2_test_util.ModelTest):
self.assertEqual(len(signature_defs.values()), 1)
self.assertEqual(
list(signature_defs['mul_add'].keys()), ['inputs', 'outputs'])
self.assertEqual(
sorted(signature_defs['mul_add']['inputs']), ['x', 'y'])
self.assertCountEqual(signature_defs['mul_add']['inputs'], ['x', 'y'])
self.assertEqual(list(signature_defs['mul_add']['outputs']), ['output_0'])
@test_util.run_v2_only
@ -962,8 +965,7 @@ class FromSavedModelTest(lite_v2_test_util.ModelTest):
self.assertEqual(len(signature_defs.values()), 1)
self.assertEqual(
list(signature_defs['mul_add'].keys()), ['inputs', 'outputs'])
self.assertEqual(
sorted(signature_defs['mul_add']['inputs']), ['x', 'y'])
self.assertCountEqual(signature_defs['mul_add']['inputs'], ['x', 'y'])
self.assertEqual(list(signature_defs['mul_add']['outputs']), ['output_0'])
@test_util.run_v2_only