-
Notifications
You must be signed in to change notification settings - Fork 74k
/
lite_flex_test.py
445 lines (371 loc) · 18.6 KB
/
lite_flex_test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for lite.py functionality related to select TF op usage."""
import os
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from tensorflow.core.framework import graph_pb2
from tensorflow.lite.python import lite
from tensorflow.lite.python import test_util as tflite_test_util
from tensorflow.lite.python.convert import register_custom_opdefs
from tensorflow.lite.python.interpreter import Interpreter
from tensorflow.lite.python.testdata import double_op
from tensorflow.python.client import session
from tensorflow.python.eager import def_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.framework.importer import import_graph_def
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import list_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.saved_model import saved_model
from tensorflow.python.trackable import autotrackable
class FromSessionTest(test_util.TensorFlowTestCase, parameterized.TestCase):
@parameterized.named_parameters(
('EnableMlirConverter', True), # enable mlir
('DisableMlirConverter', False)) # disable mlir
def testFlexMode(self, enable_mlir):
with ops.Graph().as_default():
in_tensor = array_ops.placeholder(shape=[1, 4], dtype=dtypes.float32)
out_tensor = in_tensor + in_tensor
sess = session.Session()
# Convert model and ensure model is not None.
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
[out_tensor])
converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
converter.experimental_new_converter = enable_mlir
tflite_model = converter.convert()
self.assertTrue(tflite_model)
# Check the model works with TensorFlow ops.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_details = interpreter.get_output_details()
expected_output = np.array([[2.0, 4.0, 6.0, 8.0]], dtype=np.float32)
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
def testFlexWithAutomaticPassThrough(self):
# Create a graph that has one L2Loss op.
with ops.Graph().as_default():
with session.Session() as sess:
in_tensor = array_ops.placeholder(
shape=[4], dtype=dtypes.float32, name='input')
out_tensor = nn_ops.l2_loss(in_tensor)
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
[out_tensor])
converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
converter._experimental_allow_all_select_tf_ops = True
tflite_model = converter.convert()
self.assertTrue(tflite_model)
self.assertIn('FlexL2Loss', tflite_test_util.get_ops_list(tflite_model))
def testDeprecatedFlags(self):
with ops.Graph().as_default():
in_tensor = array_ops.placeholder(shape=[1, 4], dtype=dtypes.float32)
out_tensor = in_tensor + in_tensor
sess = session.Session()
# Convert model and ensure model is not None.
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
[out_tensor])
converter.target_ops = set([lite.OpsSet.SELECT_TF_OPS])
# Ensure `target_ops` is set to the correct value after flag deprecation.
self.assertEqual(converter.target_ops, set([lite.OpsSet.SELECT_TF_OPS]))
self.assertEqual(converter.target_spec.supported_ops,
set([lite.OpsSet.SELECT_TF_OPS]))
tflite_model = converter.convert()
self.assertTrue(tflite_model)
# Check the model works with TensorFlow ops.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_details = interpreter.get_output_details()
expected_output = np.array([[2.0, 4.0, 6.0, 8.0]], dtype=np.float32)
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
class FromConcreteFunctionTest(test_util.TensorFlowTestCase,
parameterized.TestCase):
@parameterized.named_parameters(
('EnableMlirConverter', True), # enable mlir
('DisableMlirConverter', False)) # disable mlir
@test_util.run_v2_only
def testFloat(self, enable_mlir):
input_data = constant_op.constant(1., shape=[1])
root = autotrackable.AutoTrackable()
root.v1 = variables.Variable(3.)
root.v2 = variables.Variable(2.)
root.f = def_function.function(lambda x: root.v1 * root.v2 * x)
concrete_func = root.f.get_concrete_function(input_data)
# Convert model.
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func],
root)
converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
converter.experimental_new_converter = enable_mlir
tflite_model = converter.convert()
# Check the model works with TensorFlow ops.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
test_input = np.array([4.0], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_details = interpreter.get_output_details()
expected_output = np.array([24.0], dtype=np.float32)
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
# Ensure that input TFLite buffer is not reused for ops such as
# `TensorListSetItem`. The example model has a while loop, and the while body
# has a `TensorListSetItem` op which takes the output from a `Where` op.
@test_util.run_v2_only
def testDisableFlexTensorMemoryReusing(self):
@tf.function(input_signature=[
tf.TensorSpec(shape=[2, 3], dtype=tf.float32, name='x')
])
def model(x):
l = list_ops.tensor_list_reserve(
element_dtype=tf.int64, element_shape=[None, 1], num_elements=2)
init_state = (0, x, l)
condition = lambda i, x, l: i < 2
def body(i, x, l):
element = tf.where(x[i])
l = list_ops.tensor_list_set_item(l, i, element)
return i + 1, x, l
_, _, l_final = tf.while_loop(condition, body, init_state)
return list_ops.tensor_list_stack(l_final, element_dtype=tf.int64)
# Convert model.
converter = lite.TFLiteConverterV2.from_concrete_functions(
[model.get_concrete_function()])
converter.target_spec.supported_ops = set(
[lite.OpsSet.TFLITE_BUILTINS, lite.OpsSet.SELECT_TF_OPS])
tflite_model = converter.convert()
# Check the model produces correct result.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
test_input = np.array([[1.0, 2.0, 0.0], [0.0, 5.0, 6.0]], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_details = interpreter.get_output_details()
expected_output = np.array([0, 1, 1, 2], dtype=np.int64)
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == np.ndarray.flatten(output_data)).all())
class WithCustomOpTest(test_util.TensorFlowTestCase, parameterized.TestCase):
def _createGraphWithCustomOp(self, opname='CustomAdd'):
custom_opdefs_str = (
'name: \'' + opname + '\' input_arg: {name: \'Input1\' type: DT_FLOAT} '
'input_arg: {name: \'Input2\' type: DT_FLOAT} output_arg: {name: '
'\'Output\' type: DT_FLOAT}')
# Create a graph that has one add op.
new_graph = graph_pb2.GraphDef()
with ops.Graph().as_default():
with session.Session() as sess:
in_tensor = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name='input')
out_tensor = in_tensor + in_tensor
inputs = {'x': in_tensor}
outputs = {'z': out_tensor}
new_graph.CopyFrom(sess.graph_def)
# Rename Add op name to opname.
for node in new_graph.node:
if node.op.startswith('Add'):
node.op = opname
del node.attr['T']
# Register custom op defs to import modified graph def.
register_custom_opdefs([custom_opdefs_str])
return (new_graph, inputs, outputs)
def testFlexWithCustomOp(self):
new_graph, inputs, outputs = self._createGraphWithCustomOp(
opname='CustomAdd4')
# Import to load the custom opdef.
saved_model_dir = os.path.join(self.get_temp_dir(), 'model')
with ops.Graph().as_default():
with session.Session() as sess:
import_graph_def(new_graph, name='')
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
converter = lite.TFLiteConverterV2.from_saved_model(saved_model_dir)
converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
converter.target_spec.experimental_select_user_tf_ops = ['CustomAdd4']
tflite_model = converter.convert()
self.assertIn('FlexCustomAdd4', tflite_test_util.get_ops_list(tflite_model))
def testFlexWithDoubleOp(self):
# Create a graph that has one double op.
saved_model_dir = os.path.join(self.get_temp_dir(), 'model2')
with ops.Graph().as_default():
with session.Session() as sess:
in_tensor = array_ops.placeholder(
shape=[1, 4], dtype=dtypes.int32, name='input')
out_tensor = double_op.double(in_tensor)
inputs = {'x': in_tensor}
outputs = {'z': out_tensor}
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
converter = lite.TFLiteConverterV2.from_saved_model(saved_model_dir)
converter.target_spec.supported_ops = set([lite.OpsSet.SELECT_TF_OPS])
converter.target_spec.experimental_select_user_tf_ops = ['Double']
tflite_model = converter.convert()
self.assertTrue(tflite_model)
self.assertIn('FlexDouble', tflite_test_util.get_ops_list(tflite_model))
# Check the model works with TensorFlow ops.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.int32)
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_details = interpreter.get_output_details()
expected_output = np.array([[2.0, 4.0, 6.0, 8.0]], dtype=np.int32)
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
class FromSavedModelTest(test_util.TensorFlowTestCase):
@test_util.run_v2_only
def testFlexResourceVariables(self):
class Model(tf.Module):
def __init__(self):
self.v = tf.Variable([[0.0, 0.0, 0.0, 0.0]])
@tf.function(
input_signature=[tf.TensorSpec(shape=[1, 4], dtype=tf.float32)])
def eval(self, x):
# Control flow is needed to generate "FlexReadVariableOp".
if tf.reduce_mean(x) > 1.0:
self.v.assign_add([[1.0, 1.0, 1.0, 1.0]])
return self.v + x
m = Model()
to_save = m.eval.get_concrete_function()
save_dir = os.path.join(self.get_temp_dir(), 'saved_model')
tf.saved_model.save(m, save_dir, to_save)
converter = tf.lite.TFLiteConverter.from_saved_model(save_dir)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS,
]
converter.experimental_enable_resource_variables = True
tflite_model = converter.convert()
# Check the model works with TensorFlow ops.
interpreter = Interpreter(model_content=tflite_model)
signature_runner = interpreter.get_signature_runner()
outputs = signature_runner(
x=np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32))
expected_output = np.array([[2.0, 3.0, 4.0, 5.0]], dtype=np.float32)
self.assertTrue((expected_output == list(outputs.values())[0]).all)
# Second run.
outputs = signature_runner(
x=np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32))
expected_output = np.array([[3.0, 4.0, 5.0, 6.0]], dtype=np.float32)
self.assertTrue((expected_output == list(outputs.values())[0]).all)
class TFQuantizationTest(test_util.TensorFlowTestCase, parameterized.TestCase):
@parameterized.named_parameters(('DefaultMode', 'DEFAULT'),
('LegacyIntegerMode', 'LEGACY_INTEGER'))
def testAddOp(self, tf_quantization_mode):
root = autotrackable.AutoTrackable()
root.add_func = def_function.function(lambda x: x + x)
input_data = tf.reshape(tf.range(4, dtype=tf.float32), [1, 4])
concrete_func = root.add_func.get_concrete_function(input_data)
# Convert model and check if the op is not flex.
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func],
root)
converter._experimental_tf_quantization_mode = tf_quantization_mode
tflite_model = converter.convert()
self.assertTrue(tflite_model)
if tf_quantization_mode == 'LEGACY_INTEGER':
self.assertIn('ADD', tflite_test_util.get_ops_list(tflite_model))
else:
self.assertIn('FlexAddV2', tflite_test_util.get_ops_list(tflite_model))
# Check the model works.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
test_input = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_details = interpreter.get_output_details()
expected_output = np.array([[2.0, 4.0, 6.0, 8.0]], dtype=np.float32)
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
@parameterized.named_parameters(('DefaultMode', 'DEFAULT'),
('LegacyIntegerMode', 'LEGACY_INTEGER'))
def testL2LossOp(self, tf_quantization_mode):
root = autotrackable.AutoTrackable()
root.l2_loss_func = def_function.function(lambda x: nn_ops.l2_loss(x)) # pylint: disable=unnecessary-lambda
input_data = tf.range(4, dtype=tf.float32)
concrete_func = root.l2_loss_func.get_concrete_function(input_data)
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func],
root)
converter._experimental_tf_quantization_mode = tf_quantization_mode
tflite_model = converter.convert()
self.assertTrue(tflite_model)
self.assertIn('FlexL2Loss', tflite_test_util.get_ops_list(tflite_model))
# Check the model works.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
test_input = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], test_input)
interpreter.invoke()
output_details = interpreter.get_output_details()
expected_output = np.array([15.0], dtype=np.float32)
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
@parameterized.named_parameters(('DefaultMode', 'DEFAULT'),
('LegacyIntegerMode', 'LEGACY_INTEGER'))
def testConvOpWithBias(self, tf_quantization_mode):
class ConvModel(autotrackable.AutoTrackable):
@def_function.function
def conv_func(self, in_tensor, filter_tensor):
bias = constant_op.constant(3., shape=[1])
conv_tensor = tf.nn.conv2d(
in_tensor,
filter_tensor,
strides=[1, 1, 1, 1],
dilations=[1, 1, 1, 1],
padding='VALID',
data_format='NHWC')
conv_tensor = conv_tensor + bias
return tf.nn.relu(conv_tensor)
root = ConvModel()
input_data = tf.reshape(tf.range(4, dtype=tf.float32), [1, 2, 2, 1])
filter_data = tf.reshape(tf.range(2, dtype=tf.float32), [1, 2, 1, 1])
concrete_func = root.conv_func.get_concrete_function(
input_data, filter_data)
converter = lite.TFLiteConverterV2.from_concrete_functions([concrete_func],
root)
converter._experimental_tf_quantization_mode = tf_quantization_mode
tflite_model = converter.convert()
self.assertTrue(tflite_model)
self.assertCountEqual(['CONV_2D', 'RESHAPE'],
tflite_test_util.get_ops_list(tflite_model))
# Check the model works.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
test_input = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32).reshape(
(1, 2, 2, 1))
interpreter.set_tensor(input_details[0]['index'], test_input)
test_filter = np.array([1.0, 0.0], dtype=np.float32).reshape((1, 2, 1, 1))
interpreter.set_tensor(input_details[1]['index'], test_filter)
interpreter.invoke()
output_details = interpreter.get_output_details()
expected_output = np.array([[[[4.]], [[6.]]]], dtype=np.float32)
output_data = interpreter.get_tensor(output_details[0]['index'])
self.assertTrue((expected_output == output_data).all())
if __name__ == '__main__':
test.main()