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/*
* Copyright (c) 2020-2021, 2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/core/WindowIterator.h"
#include "arm_compute/runtime/NEON/NEFunctions.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "support/ToolchainSupport.h"
#include "utils/Utils.h"
#include <cstdlib>
using namespace arm_compute;
using namespace utils;
QuantizationInfo dynamic_qinfo(QuantizationInfo qinfo)
{
return QuantizationInfo(qinfo.scale(), qinfo.offset(), true);
}
void set_qinfo_dynamic(Tensor &t)
{
t.info()->set_quantization_info(dynamic_qinfo(t.info()->quantization_info()));
}
void quantize(Tensor &qt, const Tensor &t, float min, float max)
{
DataType dt = DataType::QASYMM8_SIGNED;
// Determine the scale
const float scale = (max - min) / 256.0f;
// Determine the zero-point; using affine equation val = (qval-zerop) * scale
const float zero_point = -128.0f - min / scale;
QuantizationInfo qinfo(scale, (int32_t)round(zero_point), true);
// We now have the quantisation info and can configure the quantised tensor
qt.allocator()->init(TensorInfo(t.info()->tensor_shape(), 1, dt, qinfo));
qt.allocator()->allocate();
NEQuantizationLayer quantization;
quantization.configure(&t, &qt);
quantization.run();
}
void invert_qinfo_offset(Tensor &t)
{
QuantizationInfo qinfo = t.info()->quantization_info();
t.info()->set_quantization_info(QuantizationInfo(qinfo.scale()[0], -qinfo.offset()[0], qinfo.is_dynamic()));
}
void print_quantization_info(const Tensor &t, const std::string &name_prefix)
{
QuantizationInfo qinfo = t.info()->quantization_info();
std::cout << name_prefix << "_qinfo="
<< "QuantizationInfo(" << qinfo.scale()[0] << ", " << qinfo.offset()[0] << ")\n";
}
int main(int argc, char **argv)
{
size_t M = 4;
size_t N = 4;
size_t K = 4;
// Parse args
if (argc < 3) /* case default matrix sizes */
{
// Print help
std::cout << "Usage: ./build/neon_gemm_qasymm8 M N K\n";
std::cout << "Too few or no inputs provided. Using default M=4, N=4, K=4\n\n";
}
else /* case M N K arguments provided */
{
M = strtol(argv[1], nullptr, 10);
N = strtol(argv[2], nullptr, 10);
K = strtol(argv[3], nullptr, 10);
}
/*** Floating point matrix multiplication ***/
// Initialise input matrices
NEGEMM fgemm{};
Tensor src1;
Tensor src2;
Tensor dst;
src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32));
src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32));
dst.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32));
fgemm.configure(&src1, &src2, nullptr, &dst, 1, 0);
// Allocate matrices
src1.allocator()->allocate();
src2.allocator()->allocate();
dst.allocator()->allocate();
float min1 = 0.0f;
float max1 = 1.0f;
fill_random_tensor(src1, 0, min1, max1);
float min2 = -1.0f;
float max2 = 2.0f;
fill_random_tensor(src2, 1, min2, max2);
// Run single precision gemm and print result
fgemm.run();
#if ARM_COMPUTE_DEBUG_ENABLED
std::cout << "# F32 GEMM result:\n";
std::cout << "src1=[ \n";
src1.print(std::cout);
std::cout << "] \n";
std::cout << "src2=[ \n";
src2.print(std::cout);
std::cout << "] \n";
std::cout << "dst=[ \n";
dst.print(std::cout);
std::cout << "] \n";
#endif // ARM_COMPUTE_DEBUG_ENABLED
Tensor q_src1;
quantize(q_src1, src1, min1, max1);
print_quantization_info(q_src1, "src1");
q_src1.info()->set_are_values_constant(false);
// NEGEMMLowpMatrixMultiplyCore adopts the opposite convention for the offset
// compared to NEQuantizeLayer
invert_qinfo_offset(q_src1);
Tensor q_src2;
quantize(q_src2, src2, min2, max2);
print_quantization_info(q_src2, "src2");
q_src2.info()->set_are_values_constant(false);
// NEGEMMLowpMatrixMultiplyCore adopts the opposite convention for the offset
// compared to NEQuantizeLayer
invert_qinfo_offset(q_src2);
// q_dst will be Dequantized to F32 so it doesn't need a QuantizationInfo
Tensor q_dst;
q_dst.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32));
// Configure low precision gemm and initialise result tensor (pre-output)
NEGEMMLowpMatrixMultiplyCore qgemm;
qgemm.configure(&q_src1, &q_src2, nullptr, &q_dst);
q_dst.allocator()->allocate();
// Run low precision matrix multiply kernel
qgemm.run();
#if ARM_COMPUTE_DEBUG_ENABLED
// Print quantized source matrices
std::cout << "q_src1=[ \n";
q_src1.print(std::cout);
std::cout << "] \n";
std::cout << "q_src2=[ \n";
q_src2.print(std::cout);
std::cout << "] \n";
std::cout << "# Lowp GEMM output (FP32):\n";
std::cout << "q_dst=[ \n";
q_dst.print(std::cout);
std::cout << "] \n";
// Expected result
std::cout << "# Expected result:\n";
std::cout << "dst=[ \n";
dst.print(std::cout);
std::cout << "] \n";
#endif // ARM_COMPUTE_DEBUG_ENABLED
// Rerun to test the ability to modify the Tensor contents and QuantizationInfo (dynamic quantization)
min1 = -1.0f;
max1 = 1.0f;
fill_random_tensor(src1, 2, min1, max1);
#if ARM_COMPUTE_DEBUG_ENABLED
std::cout << "# Refilled src1\n";
std::cout << "src1=[ \n";
src1.print(std::cout);
std::cout << "] \n";
std::cout << "src2=[ \n";
src2.print(std::cout);
std::cout << "] \n";
#endif // ARM_COMPUTE_DEBUG_ENABLED
fgemm.run();
quantize(q_src1, src1, min1, max1);
set_qinfo_dynamic(q_src1);
print_quantization_info(q_src1, "src1");
// NEGEMMLowpMatrixMultiplyCore adopts the opposite convention for the offset
// compared to NEQuantizeLayer
invert_qinfo_offset(q_src1);
qgemm.run();
#if ARM_COMPUTE_DEBUG_ENABLED
// Print quantized source matrices
std::cout << "q_src1=[ \n";
q_src1.print(std::cout);
std::cout << "] \n";
std::cout << "q_src2=[ \n";
q_src2.print(std::cout);
std::cout << "] \n";
std::cout << "# Lowp GEMM output (FP32):\n";
std::cout << "q_dst=[ \n";
q_dst.print(std::cout);
std::cout << "] \n";
// Expected result
std::cout << "# Expected result:\n";
std::cout << "dst=[ \n";
dst.print(std::cout);
std::cout << "] \n";
#endif // ARM_COMPUTE_DEBUG_ENABLED
}
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