[BUG] Upgrade to Tensorflow>2.10.1 breaks several of the DeepRec algos #2018
Description
Description
We had a security alert #2017 and we had to update the TF version.
When we moved to >2.10.1, several algos stopped working: xDeepFM, SUM, LightGCN and SliRec
In which platform does it happen?
=========================== short test summary info ============================
FAILED tests/unit/recommenders/models/test_deeprec_model.py::test_xdeepfm_component_definition
FAILED tests/unit/recommenders/models/test_deeprec_model.py::test_slirec_component_definition
FAILED tests/unit/recommenders/models/test_deeprec_model.py::test_sum_component_definition
============= 3 failed, 12 passed, 7 warnings in 153.99s (0:02:33) =============
=================================== FAILURES ===================================
______________________ test_xdeepfm_component_definition _______________________
deeprec_resource_path = PosixPath('/mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/tests/resources/deeprec')
@pytest.mark.gpu
def test_xdeepfm_component_definition(deeprec_resource_path):
data_path = os.path.join(deeprec_resource_path, "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
hparams = prepare_hparams(yaml_file)
> model = XDeepFMModel(hparams, FFMTextIterator)
E NameError: name 'XDeepFMModel' is not defined
tests/unit/recommenders/models/test_deeprec_model.py:131: NameError
----------------------------- Captured stdout call -----------------------------
INFO:recommenders.datasets.download_utils:Downloading https://recodatasets.z20.web.core.windows.net/deeprec/xdeepfmresources.zip
----------------------------- Captured stderr call -----------------------------
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------------------------------ Captured log call -------------------------------
INFO recommenders.datasets.download_utils:download_utils.py:38 Downloading https://recodatasets.z20.web.core.windows.net/deeprec/xdeepfmresources.zip
_______________________ test_slirec_component_definition _______________________
sequential_files = ('/mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/tests/resources/deeprec/slirec', '/mnt/azureml/cr/j/e49a1d...ab.pkl', '/mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/tests/resources/deeprec/slirec/category_vocab.pkl')
deeprec_config_path = PosixPath('/mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/recommenders/models/deeprec/config')
@pytest.mark.gpu
def test_slirec_component_definition(sequential_files, deeprec_config_path):
yaml_file = os.path.join(deeprec_config_path, "sli_rec.yaml")
data_path, user_vocab, item_vocab, cate_vocab = sequential_files
hparams = prepare_hparams(
yaml_file,
train_num_ngs=4,
embed_l2=0.0,
layer_l2=0.0,
learning_rate=0.001,
epochs=1,
MODEL_DIR=os.path.join(data_path, "model"),
SUMMARIES_DIR=os.path.join(data_path, "summary"),
user_vocab=user_vocab,
item_vocab=item_vocab,
cate_vocab=cate_vocab,
need_sample=True,
)
> model = SLI_RECModel(hparams, SequentialIterator)
tests/unit/recommenders/models/test_deeprec_model.py:248:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
recommenders/models/deeprec/models/sequential/sequential_base_model.py:51: in __init__
super().__init__(hparams, iterator_creator, graph=self.graph, seed=seed)
recommenders/models/deeprec/models/base_model.py:56: in __init__
self.logit = self._build_graph()
recommenders/models/deeprec/models/sequential/sequential_base_model.py:71: in _build_graph
model_output = self._build_seq_graph()
recommenders/models/deeprec/models/sequential/sli_rec.py:61: in _build_seq_graph
rnn_outputs, _ = dynamic_rnn(
/azureml-envs/azureml_c4eff4a5e459820972fa4e3ca572161c/lib/python3.9/site-packages/tensorflow/python/util/deprecation.py:383: in new_func
return func(*args, **kwargs)
/azureml-envs/azureml_c4eff4a5e459820972fa4e3ca572161c/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:153: in error_handler
raise e.with_traceback(filtered_tb) from None
/tmp/__autograph_generated_filekos1if_z.py:110: in tf__call
ag__.if_stmt(ag__.ld(self)._linear1 is None, if_body_4, else_body_4, get_state_4, set_state_4, ('self._linear1',), 1)
/tmp/__autograph_generated_filekos1if_z.py:106: in if_body_4
ag__.ld(self)._linear1 = ag__.converted_call(ag__.ld(_Linear), ([ag__.ld(inputs), ag__.ld(m_prev)], 4 * ag__.ld(self)._num_units, True), None, fscope)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <recommenders.models.deeprec.models.sequential.rnn_cell_implement._Linear object at 0x146050352070>
args = [<tf.Tensor 'time4lstm/time4lstm_cell/strided_slice_2:0' shape=(None, 32) dtype=float32>, <tf.Tensor 'Placeholder_3:0' shape=(None, 40) dtype=float32>]
output_size = 160, build_bias = True, bias_initializer = None
kernel_initializer = None
def __init__(
self,
args,
output_size,
build_bias,
bias_initializer=None,
kernel_initializer=None,
):
self._build_bias = build_bias
> if args is None or (nest.is_sequence(args) and not args):
E AttributeError: in user code:
E
E File "/mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/recommenders/models/deeprec/models/sequential/rnn_cell_implement.py", line 227, in call *
E self._linear1 = _Linear([inputs, m_prev], 4 * self._num_units, True)
E File "/mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/recommenders/models/deeprec/models/sequential/rnn_cell_implement.py", line 584, in __init__ **
E if args is None or (nest.is_sequence(args) and not args):
E
E AttributeError: module 'tensorflow.python.util.nest' has no attribute 'is_sequence'
recommenders/models/deeprec/models/sequential/rnn_cell_implement.py:584: AttributeError
---------------------------- Captured stdout setup -----------------------------
INFO:recommenders.datasets.download_utils:Downloading http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Movies_and_TV_5.json.gz
INFO:recommenders.datasets.download_utils:Downloading http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/meta_Movies_and_TV.json.gz
INFO:root:start reviews preprocessing...
INFO:root:start meta preprocessing...
INFO:root:start create instances...
INFO:root:creating item2cate dict
INFO:root:getting sampled data...
INFO:root:start data processing...
INFO:root:data generating...
INFO:root:vocab generating...
INFO:root:start valid negative sampling
INFO:root:start test negative sampling
---------------------------- Captured stderr setup -----------------------------
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------------------------------ Captured log setup ------------------------------
INFO recommenders.datasets.download_utils:download_utils.py:38 Downloading http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Movies_and_TV_5.json.gz
INFO recommenders.datasets.download_utils:download_utils.py:38 Downloading http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/meta_Movies_and_TV.json.gz
INFO root:amazon_reviews.py:399 start reviews preprocessing...
INFO root:amazon_reviews.py:386 start meta preprocessing...
INFO root:amazon_reviews.py:419 start create instances...
INFO root:amazon_reviews.py:356 creating item2cate dict
INFO root:amazon_reviews.py:367 getting sampled data...
INFO root:amazon_reviews.py:460 start data processing...
INFO root:amazon_reviews.py:202 data generating...
INFO root:amazon_reviews.py:79 vocab generating...
INFO root:amazon_reviews.py:148 start valid negative sampling
INFO root:amazon_reviews.py:170 start test negative sampling
----------------------------- Captured stdout call -----------------------------
WARNING:tensorflow:From /mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/recommenders/models/deeprec/models/sequential/sli_rec.py:61: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.RNN(cell)`, which is equivalent to this API
------------------------------ Captured log call -------------------------------
WARNING tensorflow:deprecation.py:50 From /mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/recommenders/models/deeprec/models/sequential/sli_rec.py:61: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `keras.layers.RNN(cell)`, which is equivalent to this API
________________________ test_sum_component_definition _________________________
sequential_files = ('/mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/tests/resources/deeprec/slirec', '/mnt/azureml/cr/j/e49a1d...ab.pkl', '/mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/tests/resources/deeprec/slirec/category_vocab.pkl')
deeprec_config_path = PosixPath('/mnt/azureml/cr/j/e49a1dc1154c4ee3b42372de2b064c9c/exe/wd/recommenders/models/deeprec/config')
@pytest.mark.gpu
def test_sum_component_definition(sequential_files, deeprec_config_path):
yaml_file_sum = os.path.join(deeprec_config_path, "sum.yaml")
data_path, user_vocab, item_vocab, cate_vocab = sequential_files
# SUM model
hparams_sum = prepare_hparams(
yaml_file_sum,
train_num_ngs=4,
embed_l2=0.0,
layer_l2=0.0,
learning_rate=0.001,
epochs=1,
MODEL_DIR=os.path.join(data_path, "model"),
SUMMARIES_DIR=os.path.join(data_path, "summary"),
user_vocab=user_vocab,
item_vocab=item_vocab,
cate_vocab=cate_vocab,
need_sample=True,
)
> model_sum = SUMModel(hparams_sum, SequentialIterator)
E NameError: name 'SUMModel' is not defined
tests/unit/recommenders/models/test_deeprec_model.py:324: NameError
How do we replicate the issue?
See PR gate failure: https://github.com/recommenders-team/recommenders/actions/runs/6499567929/job/17653180645?pr=2017
Expected behavior (i.e. solution)
We need to put them back to work with the newest TF