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[WIP] restrict one dim requantize scale bias size #5888

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[WIP] restrict one dim requantize scale bias size #5888

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3d82797
restrict one dim requantize scale bias size
nihui Jan 23, 2025
a491fbf
apply code-format changes
nihui Jan 23, 2025
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256 changes: 40 additions & 216 deletions src/layer/requantize.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -34,11 +34,6 @@ Requantize::Requantize()

int Requantize::load_param(const ParamDict& pd)
{
// scale_in = pd.get(0, 1.f); // bottom_blob_scale * weight_scale
// scale_out = pd.get(1, 1.f); // top_blob_scale
// bias_term = pd.get(2, 0);
// bias_data_size = pd.get(3, 0);

scale_in_data_size = pd.get(0, 1);
scale_out_data_size = pd.get(1, 1);
bias_data_size = pd.get(2, 0);
Expand Down Expand Up @@ -68,253 +63,82 @@ int Requantize::load_model(const ModelBin& mb)
return 0;
}

static void requantize(const int* intptr, signed char* ptr, float scale_in, float bias, float scale_out, int activation_type, const Mat& activation_params, int size)
{
for (int i = 0; i < size; i++)
{
float v = *intptr * scale_in + bias;
v = activation_ss(v, activation_type, activation_params);
*ptr = float2int8(v * scale_out);
intptr++;
ptr++;
}
}

int Requantize::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
{
int dims = bottom_blob.dims;
const int dims = bottom_blob.dims;
const int w = bottom_blob.w;
const int h = bottom_blob.h;
const int channels = bottom_blob.c;

if (dims == 1)
{
int w = bottom_blob.w;

top_blob.create(w, (size_t)1u, opt.blob_allocator);
if (top_blob.empty())
return -100;

// assert scale_in_data_size == 1
// assert bias_data_size == 0 || bias_data_size == 1
// assert scale_out_data_size == 1

const int* intptr = bottom_blob;
signed char* ptr = top_blob;

if (scale_in_data_size == 1 && scale_out_data_size == 1)
{
const float scale_in = scale_in_data[0];
const float scale_out = scale_out_data[0];

if (bias_data_size == 0)
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in;
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
else if (bias_data_size == 1)
{
const float bias = bias_data[0];

#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in + bias;
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
else
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in + bias_data[i];
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
}
else if (scale_in_data_size == 1 && scale_out_data_size > 1)
{
const float scale_in = scale_in_data[0];

if (bias_data_size == 0)
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in;
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out_data[i]);
}
}
else if (bias_data_size == 1)
{
const float bias = bias_data[0];

#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in + bias;
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out_data[i]);
}
}
else
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in + bias_data[i];
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out_data[i]);
}
}
}
else if (scale_in_data_size > 1 && scale_out_data_size == 1)
{
const float scale_out = scale_out_data[0];

if (bias_data_size == 0)
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in_data[i];
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
else if (bias_data_size == 1)
{
const float bias = bias_data[0];

#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in_data[i] + bias;
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
else
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in_data[i] + bias_data[i];
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
}
else // if (scale_in_data_size > 1 && scale_out_data_size > 1)
{
if (bias_data_size == 0)
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in_data[i];
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out_data[i]);
}
}
else if (bias_data_size == 1)
{
const float bias = bias_data[0];
const float scale_in = scale_in_data[0];
const float bias = bias_data_size == 0 ? 0.f : bias_data[0];
const float scale_out = scale_out_data[0];

#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in_data[i] + bias;
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out_data[i]);
}
}
else
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < w; i++)
{
float v = intptr[i] * scale_in_data[i] + bias_data[i];
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out_data[i]);
}
}
}
requantize(intptr, ptr, scale_in, bias, scale_out, activation_type, activation_params, w);
}

if (dims == 2)
{
int w = bottom_blob.w;
int h = bottom_blob.h;

top_blob.create(w, h, (size_t)1u, opt.blob_allocator);
if (top_blob.empty())
return -100;

if (bias_data_size == 0)
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < h; i++)
{
const int* intptr = bottom_blob.row<const int>(i);
signed char* ptr = top_blob.row<signed char>(i);

const float scale_in = scale_in_data_size == 1 ? scale_in_data[0] : scale_in_data[i];
const float scale_out = scale_out_data_size == 1 ? scale_out_data[0] : scale_out_data[i];

for (int j = 0; j < w; j++)
{
float v = intptr[j] * scale_in;
ptr[j] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
}
else
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < h; i++)
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < h; i++)
{
const int* intptr = bottom_blob.row<const int>(i);
signed char* ptr = top_blob.row<signed char>(i);
const int* intptr = bottom_blob.row<const int>(i);
signed char* ptr = top_blob.row<signed char>(i);

const float scale_in = scale_in_data_size == 1 ? scale_in_data[0] : scale_in_data[i];
const float scale_out = scale_out_data_size == 1 ? scale_out_data[0] : scale_out_data[i];
const float bias = bias_data_size == 1 ? bias_data[0] : bias_data[i];
const float scale_in = scale_in_data_size == 1 ? scale_in_data[0] : scale_in_data[i];
const float bias = bias_data_size == 0 ? 0.f : bias_data_size == 1 ? bias_data[0] : bias_data[i];
const float scale_out = scale_out_data_size == 1 ? scale_out_data[0] : scale_out_data[i];

for (int j = 0; j < w; j++)
{
float v = intptr[j] * scale_in + bias;
ptr[j] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
requantize(intptr, ptr, scale_in, bias, scale_out, activation_type, activation_params, w);
}
}

if (dims == 3)
{
int w = bottom_blob.w;
int h = bottom_blob.h;
int channels = bottom_blob.c;
int size = w * h;

top_blob.create(w, h, channels, (size_t)1u, opt.blob_allocator);
if (top_blob.empty())
return -100;

if (bias_data_size == 0)
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int q = 0; q < channels; q++)
{
const int* intptr = bottom_blob.channel(q);
signed char* ptr = top_blob.channel(q);

const float scale_in = scale_in_data_size == 1 ? scale_in_data[0] : scale_in_data[q];
const float scale_out = scale_out_data_size == 1 ? scale_out_data[0] : scale_out_data[q];

for (int i = 0; i < size; i++)
{
float v = intptr[i] * scale_in;
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
}
else
#pragma omp parallel for num_threads(opt.num_threads)
for (int q = 0; q < channels; q++)
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int q = 0; q < channels; q++)
{
const int* intptr = bottom_blob.channel(q);
signed char* ptr = top_blob.channel(q);
const int* intptr = bottom_blob.channel(q);
signed char* ptr = top_blob.channel(q);

const float scale_in = scale_in_data_size == 1 ? scale_in_data[0] : scale_in_data[q];
const float scale_out = scale_out_data_size == 1 ? scale_out_data[0] : scale_out_data[q];
const float bias = bias_data_size == 1 ? bias_data[0] : bias_data[q];
const float scale_in = scale_in_data_size == 1 ? scale_in_data[0] : scale_in_data[q];
const float bias = bias_data_size == 0 ? 0.f : bias_data_size == 1 ? bias_data[0] : bias_data[q];
const float scale_out = scale_out_data_size == 1 ? scale_out_data[0] : scale_out_data[q];

for (int i = 0; i < size; i++)
{
float v = intptr[i] * scale_in + bias;
ptr[i] = float2int8(activation_ss(v, activation_type, activation_params) * scale_out);
}
}
requantize(intptr, ptr, scale_in, bias, scale_out, activation_type, activation_params, w * h);
}
}

Expand Down
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