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[onert-micro] Fix MaxPool2D and FullyConnected training kernels #14486

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Dec 19, 2024
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[onert-micro] Fix MaxPool2D and FullyConnected training kernels #14486

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3 changes: 2 additions & 1 deletion onert-micro/onert-micro/src/train/kernels/FullyConnected.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -153,7 +153,8 @@ OMStatus onert_micro::train::train_kernel_CircleFullyConnected(const OMBackpropE

// 2. Calculate weight gradient
// Init weight grads with zeros
for (int i = 0; i < output_shape.dims(1) * input_shape.dims(1); i += sizeof(float))
const auto kDlossSizeInBytes = output_shape.dims(1) * input_shape.dims(1) * sizeof(float);
for (int i = 0; i < kDlossSizeInBytes; i += sizeof(float))
*static_cast<float *>(static_cast<void *>(dloss_dweight_data + i)) = 0;

pal::FullyConnectedWeightGrad(
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2 changes: 1 addition & 1 deletion onert-micro/onert-micro/src/train/kernels/MaxPool2D.cpp
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Expand Up @@ -119,7 +119,7 @@ OMStatus onert_micro::train::train_kernel_CircleMaxPool2D(const OMBackpropExecut
params.filter_w = options->filter_width();

// Set input grad to zero
for (size_t i = 0; i < input_shape.flatSize(); i += sizeof(float))
for (size_t i = 0; i < input_shape.flatSize() * sizeof(float); i += sizeof(float))
*static_cast<float *>(static_cast<void *>(dloss_dinput_data + i)) = 0;

// Calculate input grad
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