# CHECK-SAME: then_branch = @"key/[[then:[a-zA-Z_0-9]+]]"
# CHECK: func private @"key/[[else]]"(
# CHECK: func private @"key/[[then]]"(
def Test():
data = tf.constant([1, 2, 3, 4, 5, 6])
# Create placeholders to prevent constant folding.
x_op = tf.placeholder(dtype=tf.int32)
y_op = tf.placeholder(dtype=tf.int32)
less_op = tf.less(x_op, y_op)
switch_op = control_flow_ops.switch(data, less_op)
merge_op = control_flow_ops.merge(switch_op)[0]
result = tf.transpose(merge_op)
tensor_info_result = tf.compat.v1.saved_model.utils.build_tensor_info(
result)
signature_def = tf.saved_model.signature_def_utils.build_signature_def(
inputs=None,
outputs={'result': tensor_info_result},
method_name='some_function')
return {'key': signature_def}, None, None
if __name__ == '__main__':
common_v1.set_tf_options()
common_v1.do_test(Test)
table_initializer = tf.lookup.TextFileInitializer(
vocabulary_file, tf.string, tf.lookup.TextFileIndex.WHOLE_LINE,
tf.int64, tf.lookup.TextFileIndex.LINE_NUMBER)
# Incur another bound_input on the asset, but with a different sym_name, i.e.,
# __tf_saved_model_asset1_tokens.txt vs. __tf_saved_model_asset0_tokens.txt.
table = tf.lookup.StaticVocabularyTable(table_initializer,
num_oov_buckets=10)
vocab_file_tensor = tf.convert_to_tensor(vocabulary_file,
tf.string,
name='asset_filepath')
tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, vocab_file_tensor)
x = tf.placeholder(tf.string, shape=(), name='input')
r = table.lookup(x)
tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)
tensor_info_r = tf.compat.v1.saved_model.utils.build_tensor_info(r)
return {
'key':
(tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
inputs={'x': tensor_info_x},
outputs={'r': tensor_info_r},
method_name='some_function'))
}, tf.tables_initializer(), tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS)
if __name__ == '__main__':
common_v1.set_tf_options()
common_v1.do_test(test)
# CHECK-NEXT: [[R0:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
# CHECK-NEXT: [[R1:%.*]] = "tf.MatMul"([[ARG0]], [[R0]]) {{{.*}}} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
# CHECK-NEXT: return [[R1]] : tensor<3x3xf32>
def Test():
x = tf.constant([[1.0], [1.0], [1.0]])
y = tf.compat.v1.get_variable(name='y',
shape=(1, 3),
initializer=tf.random_normal_initializer(),
trainable=True)
r = tf.matmul(x, y)
tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)
tensor_info_r = tf.compat.v1.saved_model.utils.build_tensor_info(r)
return {
'key':
(tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
inputs={'x': tensor_info_x},
outputs={'r': tensor_info_r},
method_name='some_function'))
}, tf.initializers.global_variables(), None
if __name__ == '__main__':
common_v1.set_tf_options()
common_v1.do_test(Test, canonicalize=True)
table_initializer = tf.lookup.TextFileInitializer(
vocabulary_file, tf.string, tf.lookup.TextFileIndex.WHOLE_LINE,
tf.int64, tf.lookup.TextFileIndex.LINE_NUMBER)
# Incur another bound_input on the asset, but with a different sym_name, i.e.,
# __tf_saved_model_asset1_tokens.txt vs. __tf_saved_model_asset0_tokens.txt.
table = tf.lookup.StaticVocabularyTable(table_initializer,
num_oov_buckets=10)
vocab_file_tensor = tf.convert_to_tensor(vocabulary_file,
tf.string,
name='asset_filepath')
tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, vocab_file_tensor)
x = tf.placeholder(tf.string, shape=(), name='input')
r = table.lookup(x)
tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)
tensor_info_r = tf.compat.v1.saved_model.utils.build_tensor_info(r)
return {
'key':
(tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
inputs={'x': tensor_info_x},
outputs={'r': tensor_info_r},
method_name='some_function'))
}, tf.tables_initializer(), tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS)
if __name__ == '__main__':
common_v1.set_tf_options()
common_v1.do_test(test, use_lite=True)
x = tf.constant([[1.0], [1.0], [1.0]])
y = tf.constant([[2.0], [2.0], [2.0]])
# Verify that the function defined using function.Defun
# has a corresponding tf.LegacyCall op.
# CHECK: func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME: [[ARG0:%.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["y"]},
# CHECK-SAME: [[ARG1:%.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["x"]}
#
# CHECK-NEXT: [[R0:%.*]] = "tf.LegacyCall"([[ARG1]], [[ARG0]])
z = plus(x, y)
tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.compat.v1.saved_model.utils.build_tensor_info(y)
tensor_info_z = tf.compat.v1.saved_model.utils.build_tensor_info(z)
return {
'key': (tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
inputs={
'x': tensor_info_x,
'y': tensor_info_y
},
outputs={'z': tensor_info_z},
method_name='test_function'))
}, None, None
if __name__ == '__main__':
common_v1.set_tf_options()
common_v1.do_test(test_defun)
shape=(),
initializer=tf.random_normal_initializer(),
trainable=True)
table_initializer = tf.lookup.KeyValueTensorInitializer(
keys=[1, 2, 3, 4],
values=[5, 6, 7, 8],
key_dtype=tf.int32,
value_dtype=tf.float32)
table = tf.lookup.StaticHashTable(table_initializer,
default_value=tf.constant(0.0))
x = tf.placeholder(tf.int32, shape=(), name='input')
y = table.lookup(x)
r = tf.add(y, z)
tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)
tensor_info_r = tf.compat.v1.saved_model.utils.build_tensor_info(r)
return {
'key':
(tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
inputs={'x': tensor_info_x},
outputs={'r': tensor_info_r},
method_name='some_function'))
}
if __name__ == '__main__':
common_v1.set_tf_options()
common_v1.do_test(Test(), tf.tables_initializer())
# itself is verified in the common graphdef converter, so here just assert
# it is being invoked.
# CHECK: module
# CHECK-NOT: tf_saved_model.global_tensor
def Test():
x = tf.constant([[1.0], [1.0], [1.0]])
y = tf.compat.v1.get_variable(name='y',
shape=(1, 3),
initializer=tf.random_normal_initializer(),
trainable=True)
r = tf.matmul(x, y)
tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)
tensor_info_r = tf.compat.v1.saved_model.utils.build_tensor_info(r)
return {
'key':
(tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
inputs={'x': tensor_info_x},
outputs={'r': tensor_info_r},
method_name='some_function'))
}, None, None
if __name__ == '__main__':
common_v1.set_tf_options()
common_v1.do_test(Test, lift_variables=False)
# CHECK-NEXT: [[R0:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
# CHECK-NEXT: [[R1:%.*]] = "tf.MatMul"([[ARG0]], [[R0]]) {{{.*}}} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
# CHECK-NEXT: return [[R1]] : tensor<3x3xf32>
def Test():
x = tf.constant([[1.0], [1.0], [1.0]])
y = tf.compat.v1.get_variable(
name='y',
shape=(1, 3),
initializer=tf.random_normal_initializer(),
trainable=True)
r = tf.matmul(x, y)
tensor_info_x = tf.compat.v1.saved_model.utils.build_tensor_info(x)
tensor_info_r = tf.compat.v1.saved_model.utils.build_tensor_info(r)
return {
'key': (tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
inputs={'x': tensor_info_x},
outputs={'r': tensor_info_r},
method_name='some_function'))
}
if __name__ == '__main__':
common_v1.set_tf_options()
common_v1.do_test(
Test(), tf.initializers.global_variables(), canonicalize=True)
