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[onert-micro] Enable ReduceMean op #14520

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[onert-micro] Enable ReduceMean op #14520

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71a1979
[onert-micro] Enable ReducedProd op
chunseoklee Dec 31, 2024
a367654
fix buffer overflow
chunseoklee Jan 3, 2025
ee9dca0
[onert-micro] Enable (Reduce)Mean op
chunseoklee Jan 2, 2025
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4 changes: 2 additions & 2 deletions onert-micro/onert-micro/include/pal/mcu/KernelsToBuild.lst
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ REGISTER_KERNEL(PAD, Pad)
REGISTER_KERNEL(RESHAPE, Reshape)
REGISTER_KERNEL(RELU, Relu)
REGISTER_KERNEL(RELU6, Relu6)
#/*REGISTER_KERNEL(REDUCE_PROD, ReduceCommon)*/
REGISTER_KERNEL(REDUCE_PROD, ReduceProd)
#/*REGISTER_KERNEL(REDUCE_MAX, ReduceMax)*/
REGISTER_KERNEL(ROUND, Round)
REGISTER_KERNEL(LESS, Less)
Expand All @@ -53,7 +53,7 @@ REGISTER_KERNEL(LOG_SOFTMAX, LogSoftmax)
REGISTER_KERNEL(MUL, Mul)
#/*REGISTER_KERNEL(MIRROR_PAD, MirrorPad)*/
REGISTER_KERNEL(MAXIMUM, Maximum)
#/*REGISTER_KERNEL(MEAN, Mean)*/
REGISTER_KERNEL(MEAN, Mean)
REGISTER_KERNEL(MAX_POOL_2D, MaxPool2D)
REGISTER_KERNEL(MINIMUM, Minimum)
REGISTER_KERNEL(SHAPE, Shape)
Expand Down
177 changes: 177 additions & 0 deletions onert-micro/onert-micro/include/pal/mcu/PALReduceCommon.h
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@@ -0,0 +1,177 @@
/*
* Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#ifndef ONERT_MICRO_PAL_TANH_H
#define ONERT_MICRO_PAL_TANH_H

#include "PALUtils.h"

namespace onert_micro
{
namespace execute
{
namespace pal
{

// This method parses the input 'axis' to remove duplicates and handle negative
// values, and returns a valid 'out_axis'
inline bool resolveAxis(const int num_dims, const int *axis, const int64_t num_axis, int *out_axis,
int *out_num_axis)
{
*out_num_axis = 0; // Just in case.
// Short-circuit axis resolution for scalars; the axis will go unused.
if (num_dims == 0)
{
return true;
}
// o(n^2) is fine since out_num_axis should be really small, mostly <= 4
for (int64_t idx = 0; idx < num_axis; ++idx)
{
// Handle negative index. A positive index 'p_idx' can be represented as a
// negative index 'n_idx' as: n_idx = p_idx-num_dims
// eg: For num_dims=3, [0, 1, 2] is the same as [-3, -2, -1] */
int current = axis[idx] < 0 ? (axis[idx] + num_dims) : axis[idx];
if (current < 0 || current >= num_dims)
{
return false;
}
bool is_dup = false;
for (int j = 0; j < *out_num_axis; ++j)
{
if (out_axis[j] == current)
{
is_dup = true;
break;
}
}
if (!is_dup)
{
if (*out_num_axis > 1) {
return false;
}
out_axis[*out_num_axis] = current;
*out_num_axis += 1;
}
}
return true;
}

// Computes the generic value (i.e., sum/max/min/prod) of elements across
// dimensions given in axis. It needs to pass in init_value and reducer.
template <typename T>
inline bool ReduceGeneric(const T *input_data, const int *input_dims, const int input_num_dims,
T *output_data, const int *axis, const int64_t num_axis_dimensions,
T init_value, const int output_flat_size, T reducer(const T, const T))
{
// Return early when input shape has zero dim.
for (int i = 0; i < input_num_dims; ++i)
{
if (input_dims[i] == 0)
return false;
}

for (size_t idx = 0; idx < output_flat_size; ++idx)
{
output_data[idx] = init_value;
}

// Resolve axis.
int num_resolved_axis = 0;
int resolved_axis[2];

if (!resolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, &num_resolved_axis))
{
return false;
}

int temp_index[5];
// Reset input iterator.
for (int idx = 0; idx < input_num_dims; ++idx)
{
temp_index[idx] = 0;
}
// Iterate through input_data.
do
{
size_t input_offset = reducedOutputOffset(input_num_dims, input_dims, temp_index, 0, nullptr);
size_t output_offset =
reducedOutputOffset(input_num_dims, input_dims, temp_index, num_resolved_axis, axis);
output_data[output_offset] = reducer(output_data[output_offset], input_data[input_offset]);
} while (nextIndex(input_num_dims, input_dims, temp_index));

return true;
}

// This method expects that output_data has been initialized.
template <typename T>
inline bool reduceSumImpl(const T *input_data, const int *input_dims, const int input_num_dims,
T *output_data, const int *axis, const int num_axis,
const int num_outputs)
{
return ReduceGeneric<T>(input_data, input_dims, input_num_dims, output_data, axis, num_axis,
static_cast<T>(0), num_outputs,
[](const T current, const T in) -> T { return in + current; });
}

template <typename T>
inline bool Mean(const int *input_dims, const T *input_data, const int input_num_dims,
T *output_data, const int num_outputs, const int *axis,
const int num_axis_dimensions)
{
if (!reduceSumImpl<T>(input_data, input_dims, input_num_dims, output_data, axis,
num_axis_dimensions, num_outputs))
{
return false;
}

// Resolve axis again for computing mean
int num_resolved_axis = 0;
int resolved_axis[2];

if (!resolveAxis(input_num_dims, axis, num_axis_dimensions, resolved_axis, &num_resolved_axis))
{
return false;
}

// Calculate mean by dividing output_data by num of aggregated element.
size_t num_elements_in_axis = 1;
for (int idx = 0; idx < num_resolved_axis; ++idx)
{
size_t current = static_cast<size_t>(input_dims[resolved_axis[idx]]);
// Overflow prevention.
if (current > (std::numeric_limits<size_t>::max() / num_elements_in_axis))
{
return false;
}
num_elements_in_axis *= current;
}

if (num_elements_in_axis > 0)
{
for (size_t idx = 0; idx < num_outputs; ++idx)
{
output_data[idx] = static_cast<T>(output_data[idx] / static_cast<T>(num_elements_in_axis));
}
}
return true;
}

} // namespace pal
} // namespace execute
} // namespace onert_micro

#endif // ONERT_MICRO_PAL_TANH_H
132 changes: 132 additions & 0 deletions onert-micro/onert-micro/include/test_models/mean/FloatMeanKernel.h
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/*
* Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#ifndef ONERT_MICRO_TEST_MODELS_FLOAT_MEAN_KERNEL_H
#define ONERT_MICRO_TEST_MODELS_FLOAT_MEAN_KERNEL_H

#include "TestDataMeanBase.h"

namespace onert_micro
{
namespace test_model
{
namespace mean_float
{
/*
* Mean Kernel:
*
* Input(1, 8, 8, 4)
* |
* Mean
* |
* Output(1, 8, 8, 1)
*/
const unsigned char test_kernel_model_circle[] = {
0x18, 0x00, 0x00, 0x00, 0x43, 0x49, 0x52, 0x30, 0x00, 0x00, 0x0e, 0x00, 0x14, 0x00, 0x00, 0x00,
0x0c, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x48, 0x00, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0xb0, 0x01, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x34, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xf8, 0xff, 0xff, 0xff, 0xfc, 0xff, 0xff, 0xff,
0x04, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0e, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00,
0x0e, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x68, 0x00, 0x00, 0x00,
0x6c, 0x00, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e,
0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00,
0x16, 0x00, 0x00, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x08, 0x00, 0x0e, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x1b, 0x14, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,
0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x07, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,
0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00,
0x04, 0x00, 0x00, 0x00, 0x94, 0xff, 0xff, 0xff, 0x0c, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
0x0c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x6f, 0x66, 0x6d, 0x00, 0x04, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x0c, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0f, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x1c, 0x00, 0x00, 0x00,
0x11, 0x00, 0x00, 0x00, 0x72, 0x65, 0x64, 0x75, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x69, 0x6e,
0x64, 0x69, 0x63, 0x65, 0x73, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x0c, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x0c, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
0x69, 0x66, 0x6d, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x0c, 0x00, 0x0c, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x28, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x28, 0x11, 0x00, 0x00, 0x00, 0x4f, 0x4e, 0x45, 0x2d,
0x74, 0x66, 0x6c, 0x69, 0x74, 0x65, 0x32, 0x63, 0x69, 0x72, 0x63, 0x6c, 0x65, 0x00, 0x00, 0x00};

const std::vector<float> input_data = {
-73.19745, -62.66789, -15.868883, -69.99245, -86.77558, -47.07158, -59.42521, 5.4639907,
-15.482954, 58.430527, 30.962307, -8.479264, 64.87171, 67.23879, 54.92413, -75.001656,
4.095402, -11.012883, 1.7135352, -13.673498, 87.62411, 88.27154, 86.84994, 61.68961,
-67.81691, -36.073383, 54.346165, -83.79197, 35.099308, -23.05919, 26.401726, 20.99549,
-68.63421, -93.027596, 20.0895, -16.020033, 57.642673, 8.66057, 39.191364, 29.198711,
-5.9334397, 11.010835, 82.77485, -34.213863, -38.869553, 16.539444, 51.105484, 25.632273,
-55.436813, -26.42026, 77.96095, -59.019154, -82.52756, -94.416176, -83.77591, 46.43875,
0.7686069, 57.346397, -89.24597, -8.594538, -98.168755, -33.18969, -41.993664, 13.660449,
50.10378, 9.801906, -4.2520585, 27.210102, 48.8715, -19.44194, 38.652195, 23.77053,
-82.0674, -93.96652, 99.148094, 22.794533, 0.5715625, 0.84766275, 87.92019, 37.35077,
-32.265865, 67.46462, -24.098558, 87.36311, 90.409134, 33.023712, -15.923093, 40.05901,
-12.006578, 31.039108, -63.882004, -73.78517, -24.940235, 30.9098, 31.745, -89.77378,
-46.777866, 58.79768, -24.669464, 96.29413, 61.62126, 45.743416, 38.30191, 71.805405,
-31.20969, 33.56755, -1.926614, 72.13441, -22.292011, -16.355177, 21.689945, 87.95895,
-98.04168, 93.35264, -12.684541, -18.105795, 30.574284, 42.890903, -94.390366, -47.013157,
-98.465126, 28.63009, -83.54015, 86.82799, 0.6768988, 6.070787, 43.308678, 1.8557712,
-73.0521, -90.86948, 43.77232, 68.301056, 66.867775, 97.34002, -59.342876, -51.359367,
17.27793, 52.223003, -3.9915564, 29.598532, 34.474148, -80.920456, -30.45005, -17.469683,
-67.02992, -34.23075, -35.53944, 61.557327, -66.91338, -94.03176, -45.88021, 97.36409,
96.45681, -32.885677, 72.40823, -62.28857, 20.948895, 1.259363, -84.97583, 60.83626,
-94.692535, -15.315798, -99.92936, 40.56625, -8.6356325, -7.3984733, 56.255993, -31.700819,
62.08311, 52.800938, 32.27374, -99.46793, -40.924038, 24.67266, -58.954403, 42.263252,
-72.13501, -58.40316, 14.619292, -43.400642, -82.13468, -47.54976, -42.642033, -8.409653,
74.90983, 97.76474, -71.152916, 83.61312, -37.22972, 21.405357, -56.848846, 90.63024,
-70.21143, -29.522697, 94.9647, 74.74478, 37.564766, -40.22343, -63.337795, -65.86191,
-48.546135, -58.20052, 36.73888, 67.78194, -43.096832, 94.7046, 9.798892, -79.97487,
-15.868657, -84.753975, 4.8745494, -18.346195, 54.9818, 75.854, 41.797707, -5.673281,
-36.31264, -73.4931, -41.090492, 6.3805137, -73.66098, 85.20992, 91.28027, -73.26658,
-92.18044, 41.29011, 5.5041995, -73.70062, -16.678818, 30.614132, 92.100555, 11.274231,
-37.915485, 34.91591, 36.32971, -37.70164, -23.708878, 19.026278, -41.71216, 67.325356,
78.23511, -43.154037, 22.667723, 30.742237, -6.086414, 17.191307, 65.828896, -40.83338,
-18.61725, 23.976517, 80.2347, -92.53064, 71.6477, -38.28841, -60.853157, 24.402542};

const std::vector<float> reference_output_data = {
-55.431667, -46.952095, 16.357655, 28.008245, -4.7193613, 81.108795, -33.334023, 14.859333,
-39.398083, 33.673332, 13.409595, 13.601912, -15.728818, -53.57022, -9.9313755, -39.922916,
20.71593, 22.963072, -13.522823, 31.672546, 24.615828, 36.89219, -29.65866, -13.014804,
20.91112, 54.368, 18.141413, 17.750427, -8.869844, -16.984585, -16.636799, 12.978033,
-12.962048, 13.376387, 23.776978, -23.59151, -18.810696, -27.365314, 18.422699, -0.4828272,
-42.342857, 2.1302667, 11.922464, -8.235632, -39.82988, -45.184032, 46.28369, 4.489258,
17.493837, -32.964592, -0.55646133, -4.6420527, -28.523571, 41.74006, -36.128933, 7.3906593,
-29.771688, 29.327526, -1.0928774, 5.232649, 22.122757, 9.025103, -1.7341671, -0.7728319};

} // namespace mean_float

class TestDataFloatMean : public TestDataMeanBase<float>
{
public:
TestDataFloatMean()
{
_input_data = mean_float::input_data;
_reference_output_data = mean_float::reference_output_data;
_test_kernel_model_circle = mean_float::test_kernel_model_circle;
}

~TestDataFloatMean() override = default;
};

} // namespace test_model
} // namespace onert_micro

#endif // ONERT_MICRO_TEST_MODELS_FLOAT_MEAN_KERNEL_H
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