chore: import upstream snapshot with attribution

tensorflow/lite/python/analyzer_test.py

@@ -0,0 +1,209 @@
+# Copyright 2021 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 analyzer package."""
+
+import io
+import sys
+import tempfile
+
+import tensorflow as tf
+
+from tensorflow.lite.python import analyzer
+from tensorflow.lite.python import lite
+from tensorflow.python.framework import test_util
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.platform import test
+from tensorflow.python.trackable import autotrackable
+
+
+class AnalyzerTest(test_util.TensorFlowTestCase):
+
+  def testTxt(self):
+    model_path = resource_loader.get_path_to_datafile('../testdata/add.bin')
+    mock_stdout = io.StringIO()
+    with test.mock.patch.object(sys, 'stdout', mock_stdout):
+      analyzer.ModelAnalyzer.analyze(model_path=model_path)
+    txt = mock_stdout.getvalue()
+    self.assertIn('Subgraph#0(T#1) -> [T#2]', txt)
+    self.assertIn('Op#0 ADD(T#1, T#1) -> [T#0]', txt)
+    self.assertIn('Op#1 ADD(T#0, T#1) -> [T#2]', txt)
+    self.assertNotIn('Your model looks compatible with GPU delegate', txt)
+
+  def testMlir(self):
+    model_path = resource_loader.get_path_to_datafile('../testdata/add.bin')
+    mock_stdout = io.StringIO()
+    with test.mock.patch.object(sys, 'stdout', mock_stdout):
+      analyzer.ModelAnalyzer.analyze(
+          model_path=model_path, experimental_use_mlir=True)
+    mlir = mock_stdout.getvalue()
+    self.assertIn(
+        'func @main(%arg0: tensor<1x8x8x3xf32> '
+        '{tf_saved_model.index_path = ["a"]}) -> '
+        '(tensor<1x8x8x3xf32> {tf_saved_model.index_path = ["x"]}) attributes '
+        '{tf.entry_function = {inputs = "input", outputs = "output"}, '
+        'tf_saved_model.exported_names = ["serving_default"]}', mlir)
+    self.assertIn(
+        '%0 = tfl.add %arg0, %arg0 {fused_activation_function = "NONE"} : '
+        'tensor<1x8x8x3xf32>', mlir)
+    self.assertIn(
+        '%1 = tfl.add %0, %arg0 {fused_activation_function = "NONE"} : '
+        'tensor<1x8x8x3xf32>', mlir)
+    self.assertIn('return %1 : tensor<1x8x8x3xf32>', mlir)
+
+  def testMlirHugeConst(self):
+    model_path = resource_loader.get_path_to_datafile(
+        '../testdata/conv_huge_im2col.bin')
+    mock_stdout = io.StringIO()
+    with test.mock.patch.object(sys, 'stdout', mock_stdout):
+      analyzer.ModelAnalyzer.analyze(
+          model_path=model_path, experimental_use_mlir=True)
+    mlir = mock_stdout.getvalue()
+    self.assertIn(
+        '%1 = "tfl.pseudo_const"() <{value = dense_resource<__elided__> : '
+        'tensor<3x3x3x8xf32>}> : () -> tensor<3x3x3x8xf32>', mlir)
+
+  def testTxtWithFlatBufferModel(self):
+
+    @tf.function(
+        input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
+    def func(x):
+      return x + tf.cos(x)
+
+    converter = lite.TFLiteConverterV2.from_concrete_functions(
+        [func.get_concrete_function()], func)
+    fb_model = converter.convert()
+    mock_stdout = io.StringIO()
+    with test.mock.patch.object(sys, 'stdout', mock_stdout):
+      analyzer.ModelAnalyzer.analyze(model_content=fb_model)
+    txt = mock_stdout.getvalue()
+    self.assertIn('Subgraph#0 main(T#0) -> [T#2]', txt)
+    self.assertIn('Op#0 COS(T#0) -> [T#1]', txt)
+    self.assertIn('Op#1 ADD(T#0, T#1) -> [T#2]', txt)
+
+  def testMlirWithFlatBufferModel(self):
+
+    @tf.function(
+        input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
+    def func(x):
+      return x + tf.cos(x)
+
+    converter = lite.TFLiteConverterV2.from_concrete_functions(
+        [func.get_concrete_function()], func)
+    fb_model = converter.convert()
+    mock_stdout = io.StringIO()
+    with test.mock.patch.object(sys, 'stdout', mock_stdout):
+      analyzer.ModelAnalyzer.analyze(
+          model_content=fb_model, experimental_use_mlir=True)
+    mlir = mock_stdout.getvalue()
+    self.assertIn('func @main(%arg0: tensor<?xf32>) -> tensor<?xf32>', mlir)
+    self.assertIn('%0 = "tfl.cos"(%arg0) : (tensor<?xf32>) -> tensor<?xf32>',
+                  mlir)
+    self.assertIn(
+        '%1 = tfl.add %arg0, %0 {fused_activation_function = "NONE"} : '
+        'tensor<?xf32>', mlir)
+    self.assertIn('return %1 : tensor<?xf32', mlir)
+
+  def testTxtSignatureDefs(self):
+    with tempfile.TemporaryDirectory() as tmp_dir:
+
+      @tf.function(input_signature=[
+          tf.TensorSpec(shape=None, dtype=tf.float32),
+          tf.TensorSpec(shape=None, dtype=tf.float32)
+      ])
+      def add(a, b):
+        return {'add_result': tf.add(a, b)}
+
+      @tf.function(input_signature=[
+          tf.TensorSpec(shape=None, dtype=tf.float32),
+          tf.TensorSpec(shape=None, dtype=tf.float32)
+      ])
+      def sub(x, y):
+        return {'sub_result': tf.subtract(x, y)}
+
+      root = autotrackable.AutoTrackable()
+      root.f1 = add.get_concrete_function()
+      root.f2 = sub.get_concrete_function()
+
+      tf.saved_model.save(
+          root, tmp_dir, signatures={
+              'add': root.f1,
+              'sub': root.f2
+          })
+
+      converter = lite.TFLiteConverterV2.from_saved_model(tmp_dir)
+      fb_model = converter.convert()
+      mock_stdout = io.StringIO()
+      with test.mock.patch.object(sys, 'stdout', mock_stdout):
+        analyzer.ModelAnalyzer.analyze(model_content=fb_model)
+      txt = mock_stdout.getvalue()
+      self.assertIn("Your TFLite model has '2' signature_def(s).", txt)
+      self.assertIn("Signature#0 key: 'add'", txt)
+      self.assertIn("  'a' : T#1", txt)
+      self.assertIn("  'b' : T#0", txt)
+      self.assertIn("  'add_result' : T#2", txt)
+      self.assertIn("Signature#1 key: 'sub'", txt)
+      self.assertIn("  'x' : T#1_1", txt)
+      self.assertIn("  'y' : T#1_0", txt)
+      self.assertIn("  'sub_result' : T#1_2", txt)
+
+  def testTxtWithoutInput(self):
+
+    @tf.function()
+    def func():
+      return tf.cos(1.0)
+
+    converter = lite.TFLiteConverterV2.from_concrete_functions(
+        [func.get_concrete_function()], func)
+    fb_model = converter.convert()
+    mock_stdout = io.StringIO()
+    with test.mock.patch.object(sys, 'stdout', mock_stdout):
+      analyzer.ModelAnalyzer.analyze(model_content=fb_model)
+    txt = mock_stdout.getvalue()
+    self.assertIn('Subgraph#0 main() -> [T#0]', txt)
+
+  def testTxtWithEinsum(self):
+
+    @tf.function(input_signature=[
+        tf.TensorSpec(shape=[1, 100, 512], dtype=tf.float32),
+        tf.TensorSpec(shape=[512, 8, 64], dtype=tf.float32)
+    ])
+    def func(lhs, rhs):
+      return tf.einsum('ABD,DNH->ABNH', lhs, rhs)
+
+    converter = lite.TFLiteConverterV2.from_concrete_functions(
+        [func.get_concrete_function()], func)
+    converter.unfold_batchmatmul = True
+    fb_model = converter.convert()
+    mock_stdout = io.StringIO()
+    with test.mock.patch.object(sys, 'stdout', mock_stdout):
+      analyzer.ModelAnalyzer.analyze(model_content=fb_model)
+    txt = mock_stdout.getvalue()
+    self.assertIn('Op#0 RESHAPE(T#1, T#4[512, 512]) -> [T#5]', txt)
+    self.assertIn('Op#1 TRANSPOSE(T#5, T#3[1, 0]) -> [T#6]', txt)
+    self.assertIn('Op#2 FULLY_CONNECTED(T#0, T#6, T#-1) -> [T#7]', txt)
+    self.assertIn('Op#3 RESHAPE(T#7, T#2[1, 100, 8, 64]) -> [T#8]', txt)
+    self.assertIn(
+        'T#2(einsum/Einsum) shape:[4], type:INT32 RO 16 bytes, '
+        'buffer: 3, data:[1, 100, 8, 64]', txt)
+    self.assertIn(
+        'T#3(einsum/Einsum2) shape:[2], type:INT32 RO 8 bytes, '
+        'buffer: 4, data:[1, 0]', txt)
+    self.assertIn(
+        'T#4(einsum/Einsum3) shape:[2], type:INT32 RO 8 bytes, '
+        'buffer: 5, data:[512, 512]', txt)
+
+
+if __name__ == '__main__':
+  test.main()