BlockLSTM returns 0 tensor values

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System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04
• Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
• TensorFlow installed from (source or binary):
• TensorFlow version (use command below): 2.3.1
• Python version: 3.6.9
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory:

You can collect some of this information using our environment capture script
You can also obtain the TensorFlow version with
python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"

Describe the current behavior
I'm using tensorflow-java as a dependency for my project. I wanted to use BlockLSTM feature but the output returns always the same values. e.g. cell state output always has 0 values.

Describe the expected behavior
I expected to see results similar to LSTM TF Keras layer from Python. In which, each run returns different values
e.g. cell state outputs
run 1: [ 0.12028465, 0.07415504, -0.09205371, -0.14372592, 0.00117318]
run 2: [ 0.07089745, -0.02260131, -0.00052543, -0.19030134, 0.14710784]

Code to reproduce the issue
You can check the code I wrote on this public repo:
https://github.com/danilojsl/tensorflow-java-spikes/blob/main/src/main/java/LSTMSpike.java

Other info / logs
Warning: Could not load Loader: java.lang.UnsatisfiedLinkError: no jnijavacpp in java.library.path
Warning: Could not load Pointer: java.lang.UnsatisfiedLinkError: no jnijavacpp in java.library.path
Warning: Could not load BytePointer: java.lang.UnsatisfiedLinkError: no jnijavacpp in java.library.path
2021-04-01 12:46:24.406136: I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Warning: Could not load IntPointer: java.lang.UnsatisfiedLinkError: no jnijavacpp in java.library.path
Warning: Could not load PointerPointer: java.lang.UnsatisfiedLinkError: no jnijavacpp in java.library.path
2021-04-01 12:46:24.522141: W external/org_tensorflow/tensorflow/core/kernels/rnn/lstm_ops.cc:869] BlockLSTMOp is inefficient when both batch_size and cell_size are odd. You are using: batch_size=1, cell_size=5
Input Gate: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
Cell State: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Forget State: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
Output Gate: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
Cell Input: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Cell Output: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Hidden Output: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

Beginne mit der verlinkten Reproduktion in tensorflow-java-spikes/src/main/java/LSTMSpike.java und vergleiche deren BlockLSTM-Eingaben und -Ausgaben mit dem TensorFlow-Java-Binding. Lies die gemeldete BlockLSTMOp-Warnung in tensorflow/core/kernels/rnn/lstm_ops.cc und überprüfe das Verhalten in der TensorFlow 2.3.1-Umgebung. Als erledigt gilt die Identifizierung und Reproduktion des Grundes, warum die Cell- und Hidden-Ausgaben null bleiben, mit einem bestätigten Fix oder einer dokumentierten Ursache.