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Round inputs for dense unrolled RNN tests to make pytests more stable #1284

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Round inputs for dense unrolled RNN tests to make pytests more stable #1284

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10dd892
round inputs for dense unrolled RNN tests
JanFSchulte Apr 29, 2025
89399ca
round inputs for dense unrolled RNN tests
JanFSchulte Apr 29, 2025
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1 change: 1 addition & 0 deletions test/pytest/test_dense_unrolled.py
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Expand Up @@ -107,6 +107,7 @@ def test_resource_unrolled_rnn(rnn_layer, backend, io_type, static, reuse_factor
# Subtract 0.5 to include negative values
input_shape = (12, 8)
X = np.random.rand(50, *input_shape) - 0.5
X = np.round(X * 2**16) * 2**-16 # make it exact ap_fixed<32,16>
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This is indeed ap_fixed<17, 0>. Though, as you mentioned that the issue is more prevalent with unrolled dense, is there anything compromising bit-exactness between implementations?

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I think it might be the RNN, or maybe just LSTM, in general. Jovan did this change to get the tests to behave better in the pytorch parser case. It seemed to have worked there, so I'd be in favor of merging this now so get more meaningful test results, and look into why this is such an issue later.


layer_name = rnn_layer.__name__.lower()
keras_model = Sequential()
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