BlockLSTM output values differ from Python

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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 20.04
• 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: 3.8
• 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
The output of BlockLSTM differs from the one on Python.

Describe the expected behavior
I expected the output from BlockLSTM to have the same outputs values as Python

Code to reproduce the issue
I am using TF Java 0.2.0 and created a simple spike with hardcoded values in Java like this:

public static void main(String[] args) {
        EagerSession session = EagerSession.create();
        Ops tf = Ops.create(session);
        Scope scope = new Scope(session);

        float[][][] rawInputSequence = {{{0.1f, 0.2f}}, {{0.3f, 0.4f}}};
        Operand<TFloat32> inputSequence = tf.constant(rawInputSequence);

        int cellSize = 3;
        int[] cellShape = {1, cellSize};
        Operand<TInt32> cellDims = Constant.vectorOf(scope, cellShape);
        Operand<TInt64> seqLenMax = tf.array(2L);
        Operand<TFloat32> initialCellState = Zeros.create(scope, cellDims, TFloat32.DTYPE);
        Operand<TFloat32> initialHiddenState = Zeros.create(scope, cellDims, TFloat32.DTYPE);
        long[] biasShape = {cellSize * 4};
        Operand<TInt64> biasDim = Constant.vectorOf(scope, biasShape);
        Operand<TFloat32> bias = Zeros.create(scope, biasDim, TFloat32.DTYPE);

        FloatNdArray matrix = StdArrays.ndCopyOf(new float[][]{
            {1.6652163f, 1.366376f, 0.7786316f, 0.9834321f, 1.6551187f, -0.6363001f, -0.4229284f, 0.63195646f, 0.6605189f, -0.6906152f, 3.1515226f, 1.970373f},
            {1.9458166f, 0.9790728f, 0.7476161f, -1.6813406f, -0.75150734f, 0.13104685f, 0.004470979f, 0.009482844f, -1.1464607f, 0.5036645f, 1.3567412f, 0.71478313f},
            {0.5393334f, -0.6881541f, 1.5186735f, 1.3431606f, -0.61521095f, -2.1862414f, 1.2603592f, -0.33593372f, -0.48804748f, -0.34496853f, -0.8777565f, 0.9202126f},
            {1.3439888f, 0.32253885f, -0.7401764f, 0.10057431f, -1.3759913f, 0.08382488f, 0.56741005f, 2.207029f, -0.0066946335f, -0.8636334f, 1.9623716f, 0.14416508f},
            {-0.925145f, 0.2283957f, 0.79638815f, 0.2288384f, 0.7052175f, -0.18524477f, -2.308545f, 1.2240901f, 2.014674f, 0.6235778f, -0.15852839f, 0.17711076f}
        });
        Operand<TFloat32> weightMatrix = Constant.tensorOf(scope, matrix);

        FloatNdArray gatesMatrix = StdArrays.ndCopyOf(new float[]{1.6652163f, 1.366376f, 0.7786316f});
        Operand<TFloat32> weighGates = Constant.tensorOf(scope, gatesMatrix);

        BlockLSTM<TFloat32> blockLSTM = BlockLSTM.create(scope, seqLenMax, inputSequence, initialCellState,
                initialHiddenState, weightMatrix, weighGates, weighGates, weighGates, bias);

}

Whereas for Python I created the code below:

input_sequence = tf.constant([[[0.1, 0.2]], [[0.3, 0.4]]])
cell_size = 3
ini_cell_state = tf.zeros(shape=[1, cell_size])
ini_hidden_state = tf.zeros(shape=[1, cell_size])
bias = tf.zeros(shape=[cell_size * 4])
seq_len_max = tf.constant([2], dtype="int64")

weight_matrix = tf.constant([
    [1.6652163, 1.366376, 0.7786316, 0.9834321, 1.6551187, -0.6363001, -0.4229284, 0.63195646, 0.6605189, -0.6906152, 3.1515226, 1.970373],
    [1.9458166, 0.9790728, 0.7476161, -1.6813406, -0.75150734, 0.13104685, 0.004470979, 0.009482844, -1.1464607, 0.5036645, 1.3567412, 0.71478313],
    [0.5393334, -0.6881541, 1.5186735, 1.3431606, -0.61521095, -2.1862414, 1.2603592, -0.33593372, -0.48804748, -0.34496853, -0.8777565, 0.9202126],
    [1.3439888, 0.32253885, -0.7401764, 0.10057431, -1.3759913, 0.08382488, 0.56741005, 2.207029, -0.0066946335, -0.8636334, 1.9623716, 0.14416508],
    [-0.925145, 0.2283957, 0.79638815, 0.2288384, 0.7052175, -0.18524477, -2.308545, 1.2240901, 2.014674, 0.6235778, -0.15852839, 0.17711076]
])

weight_gates = tf.constant([1.6652163, 1.366376, 0.7786316])

block_lstm = tf.raw_ops.BlockLSTM(seq_len_max=seq_len_max, x=input_sequence, cs_prev=ini_cell_state,
                                  h_prev=ini_hidden_state, w=weight_matrix,
                                  wci=weight_gates, wcf=weight_gates,
                                  wco=weight_gates, b=bias)

Other info / logs
Output values for Java:

Input Gate: [0.6354535, 0.5823559, 0.5566029, 0.7944415, 0.6913817, 0.61125195]
Cell State: [-0.026291894, 0.037853386, -0.09006144, -0.016073283, 0.14818707, -0.25373507]
Forget State: [0.4407978, 0.50380254, 0.49064595, 0.4001113, 0.5333729, 0.4756381]
Output Gate: [0.50791717, 0.6425618, 0.58418906, 0.48629615, 0.825764, 0.70244145]
Cell Input: [-0.041375007, 0.06500043, -0.16180556, -0.0069905715, 0.18513232, -0.34502697]
Cell Output: [-0.026285835, 0.03783531, -0.08981872, -0.016071897, 0.1471118, -0.24842645]
Hidden Output: [-0.013351027, 0.024311526, -0.052471116, -0.007815702, 0.12147963, -0.17450504]

Output values for Python:

Input Gate: [[[0.6354535 0.5823559 0.5566029]]
 [[0.7784116 0.7010059 0.5999128]]]
Cell State: [[[-0.14840049  0.00885719 -0.02081871]]
 [[-0.45026863  0.16232333  0.0025551 ]]]
Forget State: [[[0.7228417  0.7436625  0.69778234]]
 [[0.6925149  0.7713927  0.67951703]]]
Output Gate: [[[0.50791717 0.6425618  0.58418906]]
 [[0.501899   0.8273453  0.69143474]]]
Cell Input: [[[-0.23353477  0.01520923 -0.03740317]]
 [[-0.44642073  0.2218112   0.02784033]]]
Cell Output: [[[-0.1473206   0.00885695 -0.02081571]]
 [[-0.4221198   0.1609125   0.0025551 ]]]
Hidden Output: [[[-0.07482666  0.00569114 -0.01216031]]
 [[-0.2118615   0.13313021  0.00176668]]]

Inizia confrontando la chiamata Java a BlockLSTM.create con tf.raw_ops.BlockLSTM di Python, utilizzando gli input, i pesi e gli output hardcoded nell'issue. Riproduci la discrepanza in entrambi gli ambienti e determina se il binding Java corrisponde all'operazione Python; il lavoro è completato quando i valori di output corrispondenti coincidono.