Numerical overflow happens on GEMMLowpOutputStage

[chenbohua3]

I am currently unable to judge whether my GEMMLowpOutputStageInfo configuration is wrong or there are some bugs in the codes. The GEMMLowpOutputStageInfo I configured is listed below:

arm_compute::GEMMLowpOutputStageInfo osInfo;
osInfo.type = arm_compute::GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
osInfo.output_data_type = DataType::QASYMM8_SIGNED
osInfo.is_quantized_per_channel = true;

The phenomenon is that when convert s32 to s8, there might be a numerical overflow. For example: Given:

int8_t input = [100];
int8_t weight [2];
input_scale==weight_scale==output_scale==1

The output of the GEMMLowpMatrixMultiplyCore returns -56 which might be overflow (100 * 2 - 127 - 128 -1=-56)

the expected target s8 value should be 127 (clamp(100*2, -128, 127)=127).

[chenbohua3]

Some updates:

I modified the neon_gemm_qasymm8.cpp example to reproduce the error, the modified codes is attached. Just run the command:

LD_LIBRARY_PATH=build ./build/examples/neon_gemm_qasymm8 1 1 1

we will get:

Result matrix:
100

3

300

Matrix 1: min=100, max=100, QuantisationInfo(1, 0)
Matrix 2: min=3, max=3, QuantisationInfo(1, 0)
Result  : min=300, max=300, QuantisationInfo(1, 0)
multiplier: 1
(q_multiplier, q_shift) = (0, -1)

Test Passed
100

3

Lowp GEMM output (int32):
300

Output pipeline result matrix:
0

Expected result:
255

The expected result should be 255, however we got 0 instead.

The codes:

/*
 * 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/WindowIterator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.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);
    }
}

QuantizationInfo get_test_quantization_params()
{
    uint8_t zp = 0;
    float scale=1.0f;
    QuantizationInfo qinfo = QuantizationInfo(scale, zp);
    return qinfo;
}

// 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] = 100.0f;
    }
    for(size_t i = 0; i < M; i++)
    {
        src1_ptr[i * K + i] = 100.0f;
    }

    // src2: Sequential values matrix
    for(size_t i = 0; i < K * N; i++)
    {
        src2_ptr[i] = 3.0f;
    }

    // Otherwise if M, N, K is given, fill in with random values
    if(!default_input)
    {
    // std::cout << "fill random tensor\n";
        // 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);
    const QuantizationInfo src1_qinfo = get_test_quantization_params();
    const QuantizationInfo src2_qinfo = get_test_quantization_params();
    const QuantizationInfo dst0_qinfo = get_test_quantization_params();

    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;
    std::cout << "multiplier: " << multiplier << std::endl;
    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
}

[chenbohua3]

@morgolock Please help to look at this question, thanks you:)

[chenbohua3]

Some updates: Under the same src1/src2/dst settings, If I create/configure/run NEGEMMLowpMatrixMultiplyCore and NEGEMMLowpOutputStage separately, than the result is correct. Or, the result is wrong. Here are the codes in which the NEGEMMLowpOutputStage is set through NEGEMMLowpMatrixMultiplyCore.configure and the result is wrong.

src1=100
src2=3
dst should equal to 127 however get 88 instead

just run:

LD_LIBRARY_PATH=build ./build/examples/neon_gemm_qasymm8 1 1 1

the codes：

#include "arm_compute/core/Types.h"
#include "arm_compute/core/WindowIterator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.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);
    }
}

QuantizationInfo get_test_quantization_params1()
{
    int32_t zp = 0;
    float scale=1.0f;
    QuantizationInfo qinfo = QuantizationInfo(scale, zp);
    return qinfo;
}

QuantizationInfo get_test_quantization_params2()
{
    std::vector<int32_t> zp{0};
    std::vector<float> scale{1.0f};
    QuantizationInfo qinfo = QuantizationInfo(scale, zp);
    return qinfo;
}

QuantizationInfo get_test_quantization_params3()
{
    int32_t zp = 0;
    float scale=0.5f;
    QuantizationInfo qinfo = QuantizationInfo(scale, zp);
    return qinfo;
}

// 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] = 100.0f;
    }
    for(size_t i = 0; i < M; i++)
    {
        src1_ptr[i * K + i] = 100.0f;
    }

    // src2: Sequential values matrix
    for(size_t i = 0; i < K * N; i++)
    {
        src2_ptr[i] = 3.0f;
    }

    // Otherwise if M, N, K is given, fill in with random values
    if(!default_input)
    {
    // std::cout << "fill random tensor\n";
        // 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);
    const QuantizationInfo src1_qinfo = get_test_quantization_params1();
    const QuantizationInfo src2_qinfo = get_test_quantization_params2();
    const QuantizationInfo dst0_qinfo = get_test_quantization_params3();

    std::cout << "QuantisationInfo(" << src1_qinfo.scale()[0] << ", " << src1_qinfo.offset()[0] << ")\n";
    for (int i=0; i<src2_qinfo.scale().size(); i++) {
        std::cout << "QuantisationInfo(" << src2_qinfo.scale()[i] << ", " << src2_qinfo.offset()[i] << ")\n";
    }
    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_SIGNED, src1_qinfo));
    q_src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::QSYMM8_PER_CHANNEL, src2_qinfo));
    q_dst0.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::QASYMM8_SIGNED, dst0_qinfo));

    // Configure low precision gemm and initialise result tensor (pre-output)
    NEGEMMLowpMatrixMultiplyCore qgemm;
    GEMMLowpOutputStageInfo osInfo;
    osInfo.is_quantized_per_channel = true;
    osInfo.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
    arm_compute::quantization::calculate_quantized_multipliers(
        src1_qinfo, src2_qinfo,dst0_qinfo, osInfo
    );
    osInfo.output_data_type    = DataType::QASYMM8_SIGNED;
    arm_compute::GEMMInfo gemmInfo;
    gemmInfo.set_gemmlowp_output_stage(osInfo);
    qgemm.configure(&q_src1, &q_src2, nullptr, &q_dst0, gemmInfo);

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

    std::vector<int8_t> input{100};
    q_src1.allocator()->import_memory(input.data());
    std::vector<int8_t> weight{3};
    q_src2.allocator()->import_memory(weight.data());

    // Run low precision matrix multiply kernel
    qgemm.run();
    // Run output stage kernel
    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 QASYMM8 (quantized) matrix
    std::cout << "Output pipeline result matrix:\n";

    q_dst0.print(std::cout);

#endif // ARM_COMPUTE_DEBUG_ENABLED
}

[morgolock]

Closing this due to inactivity, if you still require support please reopen.