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adding unit-test example of per-channel scale and zero-point
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@mortzur
mortzur committed Feb 19, 2019
1 parent 16fa9b2 commit 0b06799
Showing 1 changed file with 254 additions and 48 deletions.
302 changes: 254 additions & 48 deletions test/convolution-operator-tester.h
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Original file line number Diff line number Diff line change
Expand Up @@ -348,15 +348,19 @@ class ConvolutionOperatorTester {
auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);

std::vector<uint8_t> input(batchSize() * ((inputHeight() * inputWidth() - 1) * inputPixelStride() + groups() * groupInputChannels()) + 8);
std::vector<uint8_t> kernel(groups() * groupOutputChannels() * kernelHeight() * kernelWidth() * groupInputChannels());
std::vector<uint8_t> input(
batchSize() * ((inputHeight() * inputWidth() - 1) * inputPixelStride() + groups() * groupInputChannels()) + 8);
std::vector<uint8_t> kernel(
groups() * groupOutputChannels() * kernelHeight() * kernelWidth() * groupInputChannels());
std::vector<int32_t> bias(groups() * groupOutputChannels());
std::vector<uint8_t> output(batchSize() * ((outputHeight() * outputWidth() - 1) * outputPixelStride() + groups() * groupOutputChannels()));
std::vector<uint8_t> output(
batchSize() * ((outputHeight() * outputWidth() - 1) * outputPixelStride() + groups() * groupOutputChannels()));
std::vector<int32_t> accumulators(batchSize() * outputHeight() * outputWidth() * groups() * groupOutputChannels());

const uint8_t* inputPtr = input.data() + 8;
const uint8_t inputZeroPoint = 127;
const uint8_t kernelZeroPoint = 127;
const float kernelScale = 1.0f;

for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(u8rng));
Expand All @@ -370,8 +374,9 @@ class ConvolutionOperatorTester {
for (size_t ox = 0; ox < outputWidth(); ox++) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < groupOutputChannels(); oc++) {
accumulators[(((i * outputHeight() + oy) * outputWidth() + ox) * groups() + g) * groupOutputChannels() + oc] =
bias[g * groupOutputChannels() + oc];
accumulators
[(((i * outputHeight() + oy) * outputWidth() + ox) * groups() + g) * groupOutputChannels() + oc] =
bias[g * groupOutputChannels() + oc];
}
}
}
Expand All @@ -389,9 +394,20 @@ class ConvolutionOperatorTester {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < groupOutputChannels(); oc++) {
for (size_t ic = 0; ic < groupInputChannels(); ic++) {
accumulators[(((i * outputHeight() + oy) * outputWidth() + ox) * groups() + g) * groupOutputChannels() + oc] +=
(int32_t(inputPtr[((i * inputHeight() + iy) * inputWidth() + ix) * inputPixelStride() + g * groupInputChannels() + ic]) - int32_t(inputZeroPoint)) *
(int32_t(kernel[(((g * groupOutputChannels() + oc) * kernelHeight() + ky) * kernelWidth() + kx) * groupInputChannels() + ic]) - int32_t(kernelZeroPoint));
accumulators
[(((i * outputHeight() + oy) * outputWidth() + ox) * groups() + g) *
groupOutputChannels() +
oc] +=
(int32_t(inputPtr
[((i * inputHeight() + iy) * inputWidth() + ix) * inputPixelStride() +
g * groupInputChannels() + ic]) -
int32_t(inputZeroPoint)) *
(int32_t(kernel
[(((g * groupOutputChannels() + oc) * kernelHeight() + ky) * kernelWidth() +
kx) *
groupInputChannels() +
ic]) -
int32_t(kernelZeroPoint));
}
}
}
Expand All @@ -406,44 +422,228 @@ class ConvolutionOperatorTester {
const int32_t accumulatorsMax = *std::max_element(accumulators.cbegin(), accumulators.cend());

const double outputScale = double(uint32_t(accumulatorsMax - accumulatorsMin)) / 255.0;
const uint8_t outputZeroPoint = uint8_t(std::max(std::min(
lrint(127.5 - 0.5 * double(accumulatorsMin + accumulatorsMax) / outputScale),
long(std::numeric_limits<uint8_t>::max())), long(std::numeric_limits<uint8_t>::min())));
const uint8_t outputZeroPoint = uint8_t(std::max(
std::min(
lrint(127.5 - 0.5 * double(accumulatorsMin + accumulatorsMax) / outputScale),
long(std::numeric_limits<uint8_t>::max())),
long(std::numeric_limits<uint8_t>::min())));

ASSERT_EQ(qnnp_status_success, qnnp_initialize());
qnnp_operator_t convolution = nullptr;

ASSERT_EQ(
qnnp_status_success,
qnnp_create_convolution2d_nhwc_q8(
paddingTop(),
paddingRight(),
paddingBottom(),
paddingLeft(),
kernelHeight(),
kernelWidth(),
subsamplingHeight(),
subsamplingWidth(),
dilationHeight(),
dilationWidth(),
groups(),
groupInputChannels(),
groupOutputChannels(),
inputZeroPoint,
1.0f /* input scale */,
kernelZeroPoint,
kernelScale,
kernel.data(),
bias.data(),
outputZeroPoint,
outputScale,
qmin(),
qmax(),
0,
&convolution));

ASSERT_EQ(
qnnp_status_success,
qnnp_setup_convolution2d_nhwc_q8(
convolution,
batchSize(),
inputHeight(),
inputWidth(),
inputPtr,
inputPixelStride(),
output.data(),
outputPixelStride(),
nullptr /* thread pool */));

ASSERT_EQ(qnnp_status_success, qnnp_run_operator(convolution, nullptr /* thread pool */));

ASSERT_EQ(qnnp_status_success, qnnp_delete_operator(convolution));
convolution = nullptr;

for (size_t i = 0; i < batchSize(); i++) {
for (size_t y = 0; y < outputHeight(); y++) {
for (size_t x = 0; x < outputWidth(); x++) {
for (size_t g = 0; g < groups(); g++) {
for (size_t c = 0; c < groupOutputChannels(); c++) {
const double scaledAccumulator =
accumulators
[(((i * outputHeight() + y) * outputWidth() + x) * groups() + g) * groupOutputChannels() + c] *
kernelScale / outputScale;
const double clampedAccumulator = std::max(
std::min(scaledAccumulator, double(qmax()) - double(outputZeroPoint)),
double(qmin()) - double(outputZeroPoint));
ASSERT_NEAR(
clampedAccumulator,
(int32_t(output
[((i * outputHeight() + y) * outputWidth() + x) * outputPixelStride() +
g * groupOutputChannels() + c]) -
outputZeroPoint),
0.9)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
}
}
}
}
}
}
}

void testQ8_perChannel() const {
std::random_device randomDevice;
auto rng = std::mt19937(randomDevice());
auto s32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);

std::vector<uint8_t> input(
batchSize() * ((inputHeight() * inputWidth() - 1) * inputPixelStride() + groups() * groupInputChannels()) + 8);
std::vector<uint8_t> kernel(
groups() * groupOutputChannels() * kernelHeight() * kernelWidth() * groupInputChannels());
std::vector<int32_t> bias(groups() * groupOutputChannels());
std::vector<uint8_t> output(
batchSize() * ((outputHeight() * outputWidth() - 1) * outputPixelStride() + groups() * groupOutputChannels()));
std::vector<int32_t> accumulators(batchSize() * outputHeight() * outputWidth() * groups() * groupOutputChannels());

const uint8_t* inputPtr = input.data() + 8;
const uint8_t inputZeroPoint = 127;
const uint8_t kernelZeroPointFixed = 127;
const float kernelScaleFixed = 1.0f;
std::vector<float> kernelScale(groups() * groupOutputChannels());
std::vector<uint8_t> kernelZeroPoint(groups() * groupOutputChannels());
std::fill(kernelScale.begin(), kernelScale.end(), kernelScaleFixed);
std::fill(kernelZeroPoint.begin(), kernelZeroPoint.end(), kernelZeroPointFixed);

for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
std::generate(bias.begin(), bias.end(), std::ref(s32rng));
std::fill(output.begin(), output.end(), 0xA5);
std::fill(accumulators.begin(), accumulators.end(), 0);

for (size_t i = 0; i < batchSize(); i++) {
for (size_t oy = 0; oy < outputHeight(); oy++) {
for (size_t ox = 0; ox < outputWidth(); ox++) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < groupOutputChannels(); oc++) {
accumulators
[(((i * outputHeight() + oy) * outputWidth() + ox) * groups() + g) * groupOutputChannels() + oc] =
bias[g * groupOutputChannels() + oc];
}
}
}
}
}
for (size_t i = 0; i < batchSize(); i++) {
for (size_t oy = 0; oy < outputHeight(); oy++) {
for (size_t ox = 0; ox < outputWidth(); ox++) {
for (size_t ky = 0; ky < kernelHeight(); ky++) {
const size_t iy = oy * subsamplingHeight() + ky * dilationHeight() - paddingTop();
if (iy < inputHeight()) {
for (size_t kx = 0; kx < kernelWidth(); kx++) {
const size_t ix = ox * subsamplingWidth() + kx * dilationWidth() - paddingLeft();
if (ix < inputWidth()) {
for (size_t g = 0; g < groups(); g++) {
for (size_t oc = 0; oc < groupOutputChannels(); oc++) {
for (size_t ic = 0; ic < groupInputChannels(); ic++) {
accumulators
[(((i * outputHeight() + oy) * outputWidth() + ox) * groups() + g) *
groupOutputChannels() +
oc] +=
(int32_t(inputPtr
[((i * inputHeight() + iy) * inputWidth() + ix) * inputPixelStride() +
g * groupInputChannels() + ic]) -
int32_t(inputZeroPoint)) *
(int32_t(kernel
[(((g * groupOutputChannels() + oc) * kernelHeight() + ky) * kernelWidth() +
kx) *
groupInputChannels() +
ic]) -
int32_t(kernelZeroPoint[g * groupOutputChannels() + oc]));
}
}
}
}
}
}
}
}
}
}
const int32_t accumulatorsMin = *std::min_element(accumulators.cbegin(), accumulators.cend());
const int32_t accumulatorsMax = *std::max_element(accumulators.cbegin(), accumulators.cend());

const double outputScale = double(uint32_t(accumulatorsMax - accumulatorsMin)) / 255.0;
const uint8_t outputZeroPoint = uint8_t(std::max(
std::min(
lrint(127.5 - 0.5 * double(accumulatorsMin + accumulatorsMax) / outputScale),
long(std::numeric_limits<uint8_t>::max())),
long(std::numeric_limits<uint8_t>::min())));

ASSERT_EQ(qnnp_status_success, qnnp_initialize());
qnnp_operator_t convolution = nullptr;

ASSERT_EQ(qnnp_status_success,
qnnp_create_convolution2d_nhwc_q8(
paddingTop(), paddingRight(), paddingBottom(), paddingLeft(),
kernelHeight(), kernelWidth(),
subsamplingHeight(), subsamplingWidth(),
dilationHeight(), dilationWidth(),
groups(), groupInputChannels(), groupOutputChannels(),
inputZeroPoint, 1.0f /* input scale */,
kernelZeroPoint, 1.0f /* kernel scale */,
kernel.data(), bias.data(),
outputZeroPoint, outputScale, qmin(), qmax(),
0, &convolution));

ASSERT_EQ(qnnp_status_success,
qnnp_setup_convolution2d_nhwc_q8(
convolution,
batchSize(),
inputHeight(),
inputWidth(),
inputPtr,
inputPixelStride(),
output.data(),
outputPixelStride(),
nullptr /* thread pool */));

ASSERT_EQ(qnnp_status_success,
qnnp_run_operator(convolution, nullptr /* thread pool */));

ASSERT_EQ(qnnp_status_success,
qnnp_delete_operator(convolution));
ASSERT_EQ(
qnnp_status_success,
qnnp_create_convolution2d_nhwc_q8(
paddingTop(),
paddingRight(),
paddingBottom(),
paddingLeft(),
kernelHeight(),
kernelWidth(),
subsamplingHeight(),
subsamplingWidth(),
dilationHeight(),
dilationWidth(),
groups(),
groupInputChannels(),
groupOutputChannels(),
inputZeroPoint,
1.0f /* input scale */,
kernelZeroPointFixed,
kernelScaleFixed,
kernel.data(),
bias.data(),
outputZeroPoint,
outputScale,
qmin(),
qmax(),
0,
&convolution));

ASSERT_EQ(
qnnp_status_success,
qnnp_setup_convolution2d_nhwc_q8(
convolution,
batchSize(),
inputHeight(),
inputWidth(),
inputPtr,
inputPixelStride(),
output.data(),
outputPixelStride(),
nullptr /* thread pool */));

ASSERT_EQ(qnnp_status_success, qnnp_run_operator(convolution, nullptr /* thread pool */));

ASSERT_EQ(qnnp_status_success, qnnp_delete_operator(convolution));
convolution = nullptr;

for (size_t i = 0; i < batchSize(); i++) {
Expand All @@ -452,14 +652,20 @@ class ConvolutionOperatorTester {
for (size_t g = 0; g < groups(); g++) {
for (size_t c = 0; c < groupOutputChannels(); c++) {
const double scaledAccumulator =
accumulators[(((i * outputHeight() + y) * outputWidth() + x) * groups() + g) * groupOutputChannels() + c] / outputScale;
const double clampedAccumulator = std::max(std::min(scaledAccumulator,
double(qmax()) - double(outputZeroPoint)),
double(qmin()) - double(outputZeroPoint));
accumulators
[(((i * outputHeight() + y) * outputWidth() + x) * groups() + g) * groupOutputChannels() + c] *
kernelScale[g * groupOutputChannels() + c] / outputScale;
const double clampedAccumulator = std::max(
std::min(scaledAccumulator, double(qmax()) - double(outputZeroPoint)),
double(qmin()) - double(outputZeroPoint));
ASSERT_NEAR(
clampedAccumulator,
(int32_t(output[((i * outputHeight() + y) * outputWidth() + x) * outputPixelStride() + g * groupOutputChannels() + c]) - outputZeroPoint),
0.9) << "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
clampedAccumulator,
(int32_t(output
[((i * outputHeight() + y) * outputWidth() + x) * outputPixelStride() +
g * groupOutputChannels() + c]) -
outputZeroPoint),
0.9)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
}
}
}
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