examples/neon_gemm_qasymm8.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2020-2021 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;

 // Find min and max value in a float array
 void find_min_max(int size, const float *data, float *min, float *max)
 {
     *min = *max = data[0];
     for (int i = 0; i < size; i++)
     {
         const float val = data[i];
         *min            = std::min(*min, val);
         *max            = std::max(*max, val);
     }
 }

 // Return reasonable quantisation parameters to use for an array of floats
 // based on min and max values
 QuantizationInfo choose_quantization_params(float min, float max)
 {
     // Extend the [min,max] interval to contain 0 so we can represent it exactly
     min = std::min(min, 0.f);
     max = std::max(max, 0.f);

     // Set the quantized min and max in float values
     const float qmin = 0;
     const float qmax = 255;

     // Determine the scale
     const float scale = (max - min) / (qmax - qmin);

     // Determine the zero-point; using affine equation val = (qval-zerop) * scale
     const float zero_point_real = qmin - min / scale;

     // But we need to nudge the zero_point to an integer (exact quantized value)
     std::uint8_t zero_point_nudged = 0;
     if (zero_point_real < qmin)
     {
         zero_point_nudged = qmin;
     }
     else if (zero_point_real > qmax)
     {
         zero_point_nudged = qmax;
     }
     else
     {
         zero_point_nudged = static_cast<std::uint8_t>(support::cpp11::round(zero_point_real));
     }

     QuantizationInfo qinfo = QuantizationInfo(scale, zero_point_nudged);
     return qinfo;
 }

 void quantize_values(int size, qasymm8_t *output, float *input, const QuantizationInfo qinfo)
 {
     for (int i = 0; i < size; i++)
     {
         output[i] = quantize_qasymm8(input[i], qinfo);
     }
     std::cout << "\n";
 }

 int main(int argc, char **argv)
 {
     Tensor src1;
     Tensor src2;
     Tensor dst0;
     Tensor q_src1;
     Tensor q_src2;
     Tensor q_dst0;
     Tensor q_res;
     Tensor q_res_output;
     size_t M             = 4;
     size_t N             = 4;
     size_t K             = 4;
     bool   default_input = true;

     // 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);
         default_input = false;
     }

     /*** Floating point matrix multiplication ***/

     // Initialise input matrices
     NEGEMM fgemm{};

     src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32));
     src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32));
     dst0.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32));
     fgemm.configure(&src1, &src2, nullptr, &dst0, 1, 0);

     // Allocate matrices
     src1.allocator()->allocate();
     src2.allocator()->allocate();
     dst0.allocator()->allocate();

     // Fill in tensors, by default fill in with known data - for easy testing
     auto *src1_ptr = reinterpret_cast<float *>(src1.buffer());
     auto *src2_ptr = reinterpret_cast<float *>(src2.buffer());
     auto *dst0_ptr = reinterpret_cast<float *>(dst0.buffer());

     // Fill in: one is the identity matrix, other is sequential values
     // src1: Identity matrix
     for (size_t i = 0; i < M * K; i++)
     {
         src1_ptr[i] = 0;
     }
     for (size_t i = 0; i < M; i++)
     {
         src1_ptr[i * K + i] = 1.0f;
     }

     // src2: Sequential values matrix
     for (size_t i = 0; i < K * N; i++)
     {
         src2_ptr[i] = i * 1.123f;
     }

     // Otherwise if M, N, K is given, fill in with random values
     if (!default_input)
     {
         fill_random_tensor(src1, 0.f, 1.f);
         fill_random_tensor(src2, 0.f, 1.f);
     }

     // Run single precision gemm and print result
     fgemm.run();

 #if ARM_COMPUTE_DEBUG_ENABLED
     std::cout << "Result matrix:\n";
     src1.print(std::cout);
     src2.print(std::cout);
     dst0.print(std::cout);
 #endif // ARM_COMPUTE_DEBUG_ENABLED

     /*** Quantised asymmetric 8bit matrix  multiplication ***/

     // Start by finding the quantisation parameters for each set of values
     float src1_min;
     float src1_max;
     float src2_min;
     float src2_max;
     float dst0_min;
     float dst0_max;

     find_min_max(M * K, src1_ptr, &src1_min, &src1_max);
     find_min_max(K * N, src2_ptr, &src2_min, &src2_max);
     find_min_max(M * N, dst0_ptr, &dst0_min, &dst0_max);

     const QuantizationInfo src1_qinfo = choose_quantization_params(src1_min, src1_max);
     const QuantizationInfo src2_qinfo = choose_quantization_params(src2_min, src2_max);
     const QuantizationInfo dst0_qinfo = choose_quantization_params(dst0_min, dst0_max);

     std::cout << "Matrix 1: min=" << src1_min << ", max=" << src1_max << ", ";
     std::cout << "QuantisationInfo(" << src1_qinfo.scale()[0] << ", " << src1_qinfo.offset()[0] << ")\n";
     std::cout << "Matrix 2: min=" << src2_min << ", max=" << src2_max << ", ";
     std::cout << "QuantisationInfo(" << src2_qinfo.scale()[0] << ", " << src2_qinfo.offset()[0] << ")\n";
     std::cout << "Result  : min=" << dst0_min << ", max=" << dst0_max << ", ";
     std::cout << "QuantisationInfo(" << dst0_qinfo.scale()[0] << ", " << dst0_qinfo.offset()[0] << ")\n";

     // We now have the quantisation info and can configure the quantised tensors
     q_src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::QASYMM8, src1_qinfo));
     q_src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::QASYMM8, src2_qinfo));
     q_dst0.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::QASYMM8, dst0_qinfo));

     // In this approach we use the QuantizationLayer construct to perform quantization
     NEQuantizationLayer q1;
     NEQuantizationLayer q2;
     NEQuantizationLayer q3;
     q1.configure(&src1, &q_src1);
     q2.configure(&src2, &q_src2);
     q3.configure(&dst0, &q_dst0);

     // Configure low precision gemm and initialise result tensor (pre-output)
     NEGEMMLowpMatrixMultiplyCore qgemm;
     q_res.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::S32));
     qgemm.configure(&q_src1, &q_src2, nullptr, &q_res);

     // Configure output stage after computing shift and multiplier parameters
     NEGEMMLowpOutputStage gemmlowp_output_stage;
     int                   output_multiplier;
     int                   output_shift;
     float multiplier = (src1_qinfo.uniform().scale * src2_qinfo.uniform().scale) / dst0_qinfo.uniform().scale;
     quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
     std::cout << "(q_multiplier, q_shift) = (" << output_multiplier << ", " << output_shift << ")\n\n";

     GEMMLowpOutputStageInfo info;
     info.type                = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
     info.gemmlowp_multiplier = output_multiplier;
     info.gemmlowp_shift      = output_shift;
     info.gemmlowp_offset     = dst0_qinfo.uniform().offset;
     info.output_data_type    = DataType::QASYMM8;
     q_res_output.info()->set_data_type(DataType::QASYMM8);
     q_res_output.info()->set_num_channels(1);
     gemmlowp_output_stage.configure(&q_res, nullptr, &q_res_output, info);

     // Allocate all tensors
     q_src1.allocator()->allocate();
     q_src2.allocator()->allocate();
     q_dst0.allocator()->allocate();
     q_res.allocator()->allocate();
     q_res_output.allocator()->allocate();

     // Run quantization layers (quantizes values of each tensor)
     q1.run();
     q2.run();
     q3.run();
     // Run low precision matrix multiply kernel
     qgemm.run();
     // Run output stage kernel
     gemmlowp_output_stage.run();
     std::cout << "\nTest Passed\n";

 #if ARM_COMPUTE_DEBUG_ENABLED
     // Print quantized source matrices
     q_src1.print(std::cout);
     q_src2.print(std::cout);
     // Print result matrix in int32 form - before output stage processing
     std::cout << "Lowp GEMM output (int32):\n";
     q_res.print(std::cout);
     // Print QASYMM8 (quantized) matrix
     std::cout << "Output pipeline result matrix:\n";
     q_res_output.print(std::cout);

     // Expected result
     std::cout << "Expected result:\n";
     q_dst0.print(std::cout);
 #endif // ARM_COMPUTE_DEBUG_ENABLED
 }
	/*
	* Copyright (c) 2020-2021 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;

	// Find min and max value in a float array
	void find_min_max(int size, const float data, float min, float *max)
	{
	min = max = data[0];
	for (int i = 0; i < size; i++)
	{
	const float val = data[i];
	min = std::min(min, val);
	max = std::max(max, val);
	}
	}

	// Return reasonable quantisation parameters to use for an array of floats
	// based on min and max values
	QuantizationInfo choose_quantization_params(float min, float max)
	{
	// Extend the [min,max] interval to contain 0 so we can represent it exactly
	min = std::min(min, 0.f);
	max = std::max(max, 0.f);

	// Set the quantized min and max in float values
	const float qmin = 0;
	const float qmax = 255;

	// Determine the scale
	const float scale = (max - min) / (qmax - qmin);

	// Determine the zero-point; using affine equation val = (qval-zerop) * scale
	const float zero_point_real = qmin - min / scale;

	// But we need to nudge the zero_point to an integer (exact quantized value)
	std::uint8_t zero_point_nudged = 0;
	if (zero_point_real < qmin)
	{
	zero_point_nudged = qmin;
	}
	else if (zero_point_real > qmax)
	{
	zero_point_nudged = qmax;
	}
	else
	{
	zero_point_nudged = static_cast<std::uint8_t>(support::cpp11::round(zero_point_real));
	}

	QuantizationInfo qinfo = QuantizationInfo(scale, zero_point_nudged);
	return qinfo;
	}

	void quantize_values(int size, qasymm8_t output, float input, const QuantizationInfo qinfo)
	{
	for (int i = 0; i < size; i++)
	{
	output[i] = quantize_qasymm8(input[i], qinfo);
	}
	std::cout << "\n";
	}

	int main(int argc, char **argv)
	{
	Tensor src1;
	Tensor src2;
	Tensor dst0;
	Tensor q_src1;
	Tensor q_src2;
	Tensor q_dst0;
	Tensor q_res;
	Tensor q_res_output;
	size_t M = 4;
	size_t N = 4;
	size_t K = 4;
	bool default_input = true;

	// 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);
	default_input = false;
	}

	/* Floating point matrix multiplication */

	// Initialise input matrices
	NEGEMM fgemm{};

	src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32));
	src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32));
	dst0.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32));
	fgemm.configure(&src1, &src2, nullptr, &dst0, 1, 0);

	// Allocate matrices
	src1.allocator()->allocate();
	src2.allocator()->allocate();
	dst0.allocator()->allocate();

	// Fill in tensors, by default fill in with known data - for easy testing
	auto src1_ptr = reinterpret_cast<float >(src1.buffer());
	auto src2_ptr = reinterpret_cast<float >(src2.buffer());
	auto dst0_ptr = reinterpret_cast<float >(dst0.buffer());

	// Fill in: one is the identity matrix, other is sequential values
	// src1: Identity matrix
	for (size_t i = 0; i < M * K; i++)
	{
	src1_ptr[i] = 0;
	}
	for (size_t i = 0; i < M; i++)
	{
	src1_ptr[i * K + i] = 1.0f;
	}

	// src2: Sequential values matrix
	for (size_t i = 0; i < K * N; i++)
	{
	src2_ptr[i] = i * 1.123f;
	}

	// Otherwise if M, N, K is given, fill in with random values
	if (!default_input)
	{
	fill_random_tensor(src1, 0.f, 1.f);
	fill_random_tensor(src2, 0.f, 1.f);
	}

	// Run single precision gemm and print result
	fgemm.run();

	#if ARM_COMPUTE_DEBUG_ENABLED
	std::cout << "Result matrix:\n";
	src1.print(std::cout);
	src2.print(std::cout);
	dst0.print(std::cout);
	#endif // ARM_COMPUTE_DEBUG_ENABLED

	/* Quantised asymmetric 8bit matrix multiplication */

	// Start by finding the quantisation parameters for each set of values
	float src1_min;
	float src1_max;
	float src2_min;
	float src2_max;
	float dst0_min;
	float dst0_max;

	find_min_max(M * K, src1_ptr, &src1_min, &src1_max);
	find_min_max(K * N, src2_ptr, &src2_min, &src2_max);
	find_min_max(M * N, dst0_ptr, &dst0_min, &dst0_max);

	const QuantizationInfo src1_qinfo = choose_quantization_params(src1_min, src1_max);
	const QuantizationInfo src2_qinfo = choose_quantization_params(src2_min, src2_max);
	const QuantizationInfo dst0_qinfo = choose_quantization_params(dst0_min, dst0_max);

	std::cout << "Matrix 1: min=" << src1_min << ", max=" << src1_max << ", ";
	std::cout << "QuantisationInfo(" << src1_qinfo.scale()[0] << ", " << src1_qinfo.offset()[0] << ")\n";
	std::cout << "Matrix 2: min=" << src2_min << ", max=" << src2_max << ", ";
	std::cout << "QuantisationInfo(" << src2_qinfo.scale()[0] << ", " << src2_qinfo.offset()[0] << ")\n";
	std::cout << "Result : min=" << dst0_min << ", max=" << dst0_max << ", ";
	std::cout << "QuantisationInfo(" << dst0_qinfo.scale()[0] << ", " << dst0_qinfo.offset()[0] << ")\n";

	// We now have the quantisation info and can configure the quantised tensors
	q_src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::QASYMM8, src1_qinfo));
	q_src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::QASYMM8, src2_qinfo));
	q_dst0.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::QASYMM8, dst0_qinfo));

	// In this approach we use the QuantizationLayer construct to perform quantization
	NEQuantizationLayer q1;
	NEQuantizationLayer q2;
	NEQuantizationLayer q3;
	q1.configure(&src1, &q_src1);
	q2.configure(&src2, &q_src2);
	q3.configure(&dst0, &q_dst0);

	// Configure low precision gemm and initialise result tensor (pre-output)
	NEGEMMLowpMatrixMultiplyCore qgemm;
	q_res.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::S32));
	qgemm.configure(&q_src1, &q_src2, nullptr, &q_res);

	// Configure output stage after computing shift and multiplier parameters
	NEGEMMLowpOutputStage gemmlowp_output_stage;
	int output_multiplier;
	int output_shift;
	float multiplier = (src1_qinfo.uniform().scale * src2_qinfo.uniform().scale) / dst0_qinfo.uniform().scale;
	quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
	std::cout << "(q_multiplier, q_shift) = (" << output_multiplier << ", " << output_shift << ")\n\n";

	GEMMLowpOutputStageInfo info;
	info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
	info.gemmlowp_multiplier = output_multiplier;
	info.gemmlowp_shift = output_shift;
	info.gemmlowp_offset = dst0_qinfo.uniform().offset;
	info.output_data_type = DataType::QASYMM8;
	q_res_output.info()->set_data_type(DataType::QASYMM8);
	q_res_output.info()->set_num_channels(1);
	gemmlowp_output_stage.configure(&q_res, nullptr, &q_res_output, info);

	// Allocate all tensors
	q_src1.allocator()->allocate();
	q_src2.allocator()->allocate();
	q_dst0.allocator()->allocate();
	q_res.allocator()->allocate();
	q_res_output.allocator()->allocate();

	// Run quantization layers (quantizes values of each tensor)
	q1.run();
	q2.run();
	q3.run();
	// Run low precision matrix multiply kernel
	qgemm.run();
	// Run output stage kernel
	gemmlowp_output_stage.run();
	std::cout << "\nTest Passed\n";

	#if ARM_COMPUTE_DEBUG_ENABLED
	// Print quantized source matrices
	q_src1.print(std::cout);
	q_src2.print(std::cout);
	// Print result matrix in int32 form - before output stage processing
	std::cout << "Lowp GEMM output (int32):\n";
	q_res.print(std::cout);
	// Print QASYMM8 (quantized) matrix
	std::cout << "Output pipeline result matrix:\n";
	q_res_output.print(std::cout);

	// Expected result
	std::cout << "Expected result:\n";
	q_dst0.print(std::cout);
	#endif // ARM_COMPUTE_DEBUG_ENABLED
	}