wehub-resource-sync
210 lines
7.9 KiB
Python
210 lines
7.9 KiB
Python
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for analyzer package."""
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import io
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import sys
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import tempfile
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import tensorflow as tf
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from tensorflow.lite.python import analyzer
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from tensorflow.lite.python import lite
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from tensorflow.python.framework import test_util
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from tensorflow.python.platform import resource_loader
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from tensorflow.python.platform import test
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from tensorflow.python.trackable import autotrackable
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class AnalyzerTest(test_util.TensorFlowTestCase):
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def testTxt(self):
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model_path = resource_loader.get_path_to_datafile('../testdata/add.bin')
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mock_stdout = io.StringIO()
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with test.mock.patch.object(sys, 'stdout', mock_stdout):
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analyzer.ModelAnalyzer.analyze(model_path=model_path)
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txt = mock_stdout.getvalue()
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self.assertIn('Subgraph#0(T#1) -> [T#2]', txt)
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self.assertIn('Op#0 ADD(T#1, T#1) -> [T#0]', txt)
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self.assertIn('Op#1 ADD(T#0, T#1) -> [T#2]', txt)
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self.assertNotIn('Your model looks compatible with GPU delegate', txt)
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def testMlir(self):
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model_path = resource_loader.get_path_to_datafile('../testdata/add.bin')
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mock_stdout = io.StringIO()
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with test.mock.patch.object(sys, 'stdout', mock_stdout):
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analyzer.ModelAnalyzer.analyze(
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model_path=model_path, experimental_use_mlir=True)
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mlir = mock_stdout.getvalue()
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self.assertIn(
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'func @main(%arg0: tensor<1x8x8x3xf32> '
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'{tf_saved_model.index_path = ["a"]}) -> '
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'(tensor<1x8x8x3xf32> {tf_saved_model.index_path = ["x"]}) attributes '
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'{tf.entry_function = {inputs = "input", outputs = "output"}, '
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'tf_saved_model.exported_names = ["serving_default"]}', mlir)
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self.assertIn(
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'%0 = tfl.add %arg0, %arg0 {fused_activation_function = "NONE"} : '
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'tensor<1x8x8x3xf32>', mlir)
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self.assertIn(
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'%1 = tfl.add %0, %arg0 {fused_activation_function = "NONE"} : '
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'tensor<1x8x8x3xf32>', mlir)
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self.assertIn('return %1 : tensor<1x8x8x3xf32>', mlir)
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def testMlirHugeConst(self):
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model_path = resource_loader.get_path_to_datafile(
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'../testdata/conv_huge_im2col.bin')
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mock_stdout = io.StringIO()
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with test.mock.patch.object(sys, 'stdout', mock_stdout):
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analyzer.ModelAnalyzer.analyze(
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model_path=model_path, experimental_use_mlir=True)
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mlir = mock_stdout.getvalue()
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self.assertIn(
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'%1 = "tfl.pseudo_const"() <{value = dense_resource<__elided__> : '
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'tensor<3x3x3x8xf32>}> : () -> tensor<3x3x3x8xf32>', mlir)
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def testTxtWithFlatBufferModel(self):
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@tf.function(
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input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
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def func(x):
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return x + tf.cos(x)
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converter = lite.TFLiteConverterV2.from_concrete_functions(
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[func.get_concrete_function()], func)
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fb_model = converter.convert()
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mock_stdout = io.StringIO()
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with test.mock.patch.object(sys, 'stdout', mock_stdout):
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analyzer.ModelAnalyzer.analyze(model_content=fb_model)
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txt = mock_stdout.getvalue()
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self.assertIn('Subgraph#0 main(T#0) -> [T#2]', txt)
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self.assertIn('Op#0 COS(T#0) -> [T#1]', txt)
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self.assertIn('Op#1 ADD(T#0, T#1) -> [T#2]', txt)
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def testMlirWithFlatBufferModel(self):
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@tf.function(
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input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
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def func(x):
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return x + tf.cos(x)
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converter = lite.TFLiteConverterV2.from_concrete_functions(
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[func.get_concrete_function()], func)
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fb_model = converter.convert()
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mock_stdout = io.StringIO()
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with test.mock.patch.object(sys, 'stdout', mock_stdout):
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analyzer.ModelAnalyzer.analyze(
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model_content=fb_model, experimental_use_mlir=True)
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mlir = mock_stdout.getvalue()
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self.assertIn('func @main(%arg0: tensor<?xf32>) -> tensor<?xf32>', mlir)
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self.assertIn('%0 = "tfl.cos"(%arg0) : (tensor<?xf32>) -> tensor<?xf32>',
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mlir)
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self.assertIn(
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'%1 = tfl.add %arg0, %0 {fused_activation_function = "NONE"} : '
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'tensor<?xf32>', mlir)
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self.assertIn('return %1 : tensor<?xf32', mlir)
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def testTxtSignatureDefs(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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@tf.function(input_signature=[
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tf.TensorSpec(shape=None, dtype=tf.float32),
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tf.TensorSpec(shape=None, dtype=tf.float32)
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])
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def add(a, b):
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return {'add_result': tf.add(a, b)}
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@tf.function(input_signature=[
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tf.TensorSpec(shape=None, dtype=tf.float32),
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tf.TensorSpec(shape=None, dtype=tf.float32)
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])
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def sub(x, y):
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return {'sub_result': tf.subtract(x, y)}
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root = autotrackable.AutoTrackable()
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root.f1 = add.get_concrete_function()
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root.f2 = sub.get_concrete_function()
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tf.saved_model.save(
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root, tmp_dir, signatures={
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'add': root.f1,
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'sub': root.f2
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})
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converter = lite.TFLiteConverterV2.from_saved_model(tmp_dir)
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fb_model = converter.convert()
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mock_stdout = io.StringIO()
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with test.mock.patch.object(sys, 'stdout', mock_stdout):
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analyzer.ModelAnalyzer.analyze(model_content=fb_model)
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txt = mock_stdout.getvalue()
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self.assertIn("Your TFLite model has '2' signature_def(s).", txt)
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self.assertIn("Signature#0 key: 'add'", txt)
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self.assertIn(" 'a' : T#1", txt)
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self.assertIn(" 'b' : T#0", txt)
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self.assertIn(" 'add_result' : T#2", txt)
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self.assertIn("Signature#1 key: 'sub'", txt)
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self.assertIn(" 'x' : T#1_1", txt)
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self.assertIn(" 'y' : T#1_0", txt)
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self.assertIn(" 'sub_result' : T#1_2", txt)
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def testTxtWithoutInput(self):
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@tf.function()
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def func():
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return tf.cos(1.0)
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converter = lite.TFLiteConverterV2.from_concrete_functions(
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[func.get_concrete_function()], func)
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fb_model = converter.convert()
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mock_stdout = io.StringIO()
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with test.mock.patch.object(sys, 'stdout', mock_stdout):
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analyzer.ModelAnalyzer.analyze(model_content=fb_model)
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txt = mock_stdout.getvalue()
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self.assertIn('Subgraph#0 main() -> [T#0]', txt)
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def testTxtWithEinsum(self):
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@tf.function(input_signature=[
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tf.TensorSpec(shape=[1, 100, 512], dtype=tf.float32),
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tf.TensorSpec(shape=[512, 8, 64], dtype=tf.float32)
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])
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def func(lhs, rhs):
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return tf.einsum('ABD,DNH->ABNH', lhs, rhs)
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converter = lite.TFLiteConverterV2.from_concrete_functions(
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[func.get_concrete_function()], func)
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converter.unfold_batchmatmul = True
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fb_model = converter.convert()
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mock_stdout = io.StringIO()
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with test.mock.patch.object(sys, 'stdout', mock_stdout):
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analyzer.ModelAnalyzer.analyze(model_content=fb_model)
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txt = mock_stdout.getvalue()
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self.assertIn('Op#0 RESHAPE(T#1, T#4[512, 512]) -> [T#5]', txt)
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self.assertIn('Op#1 TRANSPOSE(T#5, T#3[1, 0]) -> [T#6]', txt)
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self.assertIn('Op#2 FULLY_CONNECTED(T#0, T#6, T#-1) -> [T#7]', txt)
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self.assertIn('Op#3 RESHAPE(T#7, T#2[1, 100, 8, 64]) -> [T#8]', txt)
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self.assertIn(
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'T#2(einsum/Einsum) shape:[4], type:INT32 RO 16 bytes, '
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'buffer: 3, data:[1, 100, 8, 64]', txt)
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self.assertIn(
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'T#3(einsum/Einsum2) shape:[2], type:INT32 RO 8 bytes, '
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'buffer: 4, data:[1, 0]', txt)
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self.assertIn(
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'T#4(einsum/Einsum3) shape:[2], type:INT32 RO 8 bytes, '
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'buffer: 5, data:[512, 512]', txt)
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if __name__ == '__main__':
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test.main()
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