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]

Commencez par la reproduction liée dans tensorflow-java-spikes/src/main/java/LSTMSpike.java et comparez ses entrées et sorties BlockLSTM avec le binding Java de TensorFlow. Lisez l’avertissement BlockLSTMOp signalé dans tensorflow/core/kernels/rnn/lstm_ops.cc et vérifiez le comportement dans l’environnement TensorFlow 2.3.1. Le travail est terminé lorsque vous avez identifié et reproduit pourquoi les sorties cell et hidden restent nulles, avec un correctif confirmé ou une cause documentée.