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Update int16 LSTM test case with zero_point=0 #2171

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merged 4 commits into from
Aug 16, 2023
Merged

Update int16 LSTM test case with zero_point=0 #2171

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a76d154
Change symmetry to True to generate golden data with zp=0
advaitjain Aug 15, 2023
823cbb3
update test cases woth zp=0 for int16
advaitjain Aug 15, 2023
0229d2e
change back to int8 activations.
advaitjain Aug 15, 2023
5a94245
fix formatting.
advaitjain Aug 15, 2023
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14 changes: 8 additions & 6 deletions tensorflow/lite/micro/kernels/testdata/lstm_test_data.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -251,12 +251,14 @@ NodeQuantizationParameters Get2X2Int16LstmQuantizationSettings() {

// state quantization parameters
quantization_settings.input = {/*scale=*/3.0518044e-5, /*zp=*/0,
/*symmetry=*/false};
quantization_settings.output = {/*scale=*/1.8310826e-5, /*zp=*/-5461,
/*symmetry=*/false};
quantization_settings.hidden_state = {/*scale=*/1.8310826e-5, /*zp=*/-5461,
/*symmetry=*/false};
quantization_settings.cell_state = {/*scale=*/0.00024414062, /*zp=*/0,
/*symmetry=*/true};
quantization_settings.output = {/*scale=*/2.1362956633198035e-05, /*zp=*/0,
/*symmetry=*/true};
quantization_settings.hidden_state = {/*scale=*/2.1362956633198035e-05,
/*zp=*/0,
/*symmetry=*/true};
quantization_settings.cell_state = {/*scale=*/0.00024414807580797754,
/*zp=*/0,
/*symmetry=*/true};

// gate quantization parameters
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10 changes: 5 additions & 5 deletions tensorflow/lite/micro/kernels/testdata/lstm_test_data_generator.py
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Expand Up @@ -17,15 +17,15 @@
2. Print the intermediate step outputs inside the LSTM for a single step LSTM invocation (Get2X2GateOutputCheckData in .cc)
3. Print the outputs for multi-step LSTM invocation (Get2X2LstmEvalCheckData in .cc)

Every invocation gives three types information:
1. Quantized output: kernel output in integer
Every invocation gives three types information:
1. Quantized output: kernel output in integer
2. Dequantized output: Quantized output in floating point representation
3. Float output: output from the floating point computation (i.e., float kernel)

Note:
Note:
1. Change quantization settings in _KERNEL_CONFIG to see the outcomes from various quantization schema (e.g., 8x8 Vs. 16x8)
2. Only single batch inference is supporte here. Change _GATE_TEST_DATA or _MULTISTEP_TEST_DATA to see kernel outputs on different input data
3. The quantization computation here is not the exact as the c++ implementation. The integer calculation is mimiced here using floating point.
3. The quantization computation here is not the exact as the c++ implementation. The integer calculation is emulated here using floating point.
No fixed point math is implemented here. The purpose is to illustrate the computation procedure and possible quantization error accumulation, not for bit exactness.
"""
from absl import app
Expand Down Expand Up @@ -88,7 +88,7 @@
_MULTISTEP_TEST_DATA = {
'init_hidden_state_vals': [0, 0],
'init_cell_state_vals': [0, 0],
'input_data': [0.2, 0.3, 0.2, 0.3, 0.2, 0.3], # three time steps
'input_data': [0.2, 0.3, 0.2, 0.3, 0.2, 0.3], # three time steps
'hidden_state_range': (-0.5, 0.7),
'cell_state_range': [-8, 8],
'input_data_range': [-1, 1]
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