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fix requantize pack4to8 (#5893)

tags/20250428
nihui GitHub 1 year ago
parent
ff5b554003
commit
4a70be45ed
No known key found for this signature in database GPG Key ID: B5690EEEBB952194
8 changed files with 408 additions and 126 deletions
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  1. +12
    -5
      src/layer/arm/convolution_arm.cpp
  2. +4
    -4
      src/layer/arm/requantize_arm.cpp
  3. +4
    -4
      src/layer/loongarch/requantize_loongarch.cpp
  4. +4
    -4
      src/layer/mips/requantize_mips.cpp
  5. +12
    -1
      src/layer/x86/convolution_x86.cpp
  6. +171
    -20
      src/layer/x86/requantize_x86.cpp
  7. +62
    -88
      tests/test_requantize.cpp
  8. +139
    -0
      tests/test_requantize_oom.cpp

+ 12
- 5
src/layer/arm/convolution_arm.cpp View File

@@ -1376,15 +1376,22 @@ int Convolution_arm::forward_int8_arm(const Mat& bottom_blob, Mat& top_blob, con
#if __ARM_NEON
if (opt.use_packing_layout)
{
#if NCNN_ARM82
if (ncnn::cpu_support_arm_asimdhp() && opt.use_fp16_arithmetic)
if (use_int8_requantize)
{
out_elempack_int32 = num_output % 8 == 0 ? 8 : num_output % 4 == 0 ? 4 : 1;
out_elempack_int32 = num_output % 8 == 0 ? 8 : 1;
}
else
#endif // NCNN_ARM82
{
out_elempack_int32 = num_output % 4 == 0 ? 4 : 1;
#if NCNN_ARM82
if (ncnn::cpu_support_arm_asimdhp() && opt.use_fp16_arithmetic)
{
out_elempack_int32 = num_output % 8 == 0 ? 8 : num_output % 4 == 0 ? 4 : 1;
}
else
#endif // NCNN_ARM82
{
out_elempack_int32 = num_output % 4 == 0 ? 4 : 1;
}
}
}
#endif // __ARM_NEON


+ 4
- 4
src/layer/arm/requantize_arm.cpp View File

@@ -120,8 +120,8 @@ static void requantize_relu(const int* intptr, signed char* ptr, const Mat& scal
for (; i < size; i++)
{
float v = *intptr * scale;
if (v < 0) v = 0;
*ptr = float2int8(v);
if (*ptr < 0) *ptr = 0;
intptr++;
ptr++;
}
@@ -190,8 +190,8 @@ static void requantize_relu(const int* intptr, signed char* ptr, const Mat& scal
for (; i < size; i++)
{
float v = *intptr * scale + bias;
if (v < 0) v = 0;
*ptr = float2int8(v);
if (*ptr < 0) *ptr = 0;
intptr++;
ptr++;
}
@@ -288,8 +288,8 @@ static void requantize_leakyrelu(const int* intptr, signed char* ptr, const Mat&
for (; i < size; i++)
{
float v = *intptr * scale;
if (v < 0) v *= slope;
*ptr = float2int8(v);
if (*ptr < 0) *ptr *= slope;
intptr++;
ptr++;
}
@@ -358,8 +358,8 @@ static void requantize_leakyrelu(const int* intptr, signed char* ptr, const Mat&
for (; i < size; i++)
{
float v = *intptr * scale + bias;
if (v < 0) v *= slope;
*ptr = float2int8(v);
if (*ptr < 0) *ptr *= slope;
intptr++;
ptr++;
}


+ 4
- 4
src/layer/loongarch/requantize_loongarch.cpp View File

@@ -120,8 +120,8 @@ static void requantize_relu(const int* intptr, signed char* ptr, const Mat& scal
for (; i < size; i++)
{
float v = *intptr * scale;
if (v < 0) v = 0;
*ptr = float2int8(v);
if (*ptr < 0) *ptr = 0;
intptr++;
ptr++;
}
@@ -182,8 +182,8 @@ static void requantize_relu(const int* intptr, signed char* ptr, const Mat& scal
for (; i < size; i++)
{
float v = *intptr * scale + bias;
if (v < 0) v = 0;
*ptr = float2int8(v);
if (*ptr < 0) *ptr = 0;
intptr++;
ptr++;
}
@@ -281,8 +281,8 @@ static void requantize_leakyrelu(const int* intptr, signed char* ptr, const Mat&
for (; i < size; i++)
{
float v = *intptr * scale;
if (v < 0) v *= slope;
*ptr = float2int8(v);
if (*ptr < 0) *ptr *= slope;
intptr++;
ptr++;
}
@@ -343,8 +343,8 @@ static void requantize_leakyrelu(const int* intptr, signed char* ptr, const Mat&
for (; i < size; i++)
{
float v = *intptr * scale + bias;
if (v < 0) v *= slope;
*ptr = float2int8(v);
if (*ptr < 0) *ptr *= slope;
intptr++;
ptr++;
}


+ 4
- 4
src/layer/mips/requantize_mips.cpp View File

@@ -120,8 +120,8 @@ static void requantize_relu(const int* intptr, signed char* ptr, const Mat& scal
for (; i < size; i++)
{
float v = *intptr * scale;
if (v < 0) v = 0;
*ptr = float2int8(v);
if (*ptr < 0) *ptr = 0;
intptr++;
ptr++;
}
@@ -182,8 +182,8 @@ static void requantize_relu(const int* intptr, signed char* ptr, const Mat& scal
for (; i < size; i++)
{
float v = *intptr * scale + bias;
if (v < 0) v = 0;
*ptr = float2int8(v);
if (*ptr < 0) *ptr = 0;
intptr++;
ptr++;
}
@@ -281,8 +281,8 @@ static void requantize_leakyrelu(const int* intptr, signed char* ptr, const Mat&
for (; i < size; i++)
{
float v = *intptr * scale;
if (v < 0) v *= slope;
*ptr = float2int8(v);
if (*ptr < 0) *ptr *= slope;
intptr++;
ptr++;
}
@@ -343,8 +343,8 @@ static void requantize_leakyrelu(const int* intptr, signed char* ptr, const Mat&
for (; i < size; i++)
{
float v = *intptr * scale + bias;
if (v < 0) v *= slope;
*ptr = float2int8(v);
if (*ptr < 0) *ptr *= slope;
intptr++;
ptr++;
}


+ 12
- 1
src/layer/x86/convolution_x86.cpp View File

@@ -993,7 +993,18 @@ int Convolution_x86::forward_int8_x86(const Mat& bottom_blob, Mat& top_blob, con
#if __SSE2__
if (opt.use_packing_layout)
{
out_elempack_int32 = num_output % 4 == 0 ? 4 : 1;
if (use_int8_requantize)
{
#if __AVX__
out_elempack_int32 = num_output % 8 == 0 ? 8 : 1;
#else
out_elempack_int32 = num_output % 8 == 0 ? 4 : 1;
#endif
}
else
{
out_elempack_int32 = num_output % 4 == 0 ? 4 : 1;
}
}
#endif // __SSE2__



+ 171
- 20
src/layer/x86/requantize_x86.cpp View File

@@ -330,6 +330,82 @@ static void requantize(const int* intptr, signed char* ptr, const Mat& scale_in_
}
}

#if __SSE2__
#if !__AVX__
static void requantize_pack4to8(const int* intptr0, const int* intptr1, signed char* ptr, const Mat& scale_in_data, const Mat& bias_data, const Mat& scale_out_data, int activation_type, const Mat& activation_params, int elemcount)
{
const int scale_in_data_size = scale_in_data.w;
const int bias_data_size = bias_data.w;
const int scale_out_data_size = scale_out_data.w;

// NCNN_LOGE("requantize_pack4to8 %d %d %d %d", scale_in_data_size, bias_data_size, scale_out_data_size, elemcount);

__m128 _scale_in0 = _mm_set1_ps(scale_in_data[0]);
__m128 _scale_in1 = _scale_in0;
if (scale_in_data_size > 1)
{
_scale_in0 = _mm_loadu_ps((const float*)scale_in_data);
_scale_in1 = _mm_loadu_ps((const float*)scale_in_data + 4);
}

__m128 _scale_out0 = _mm_set1_ps(scale_out_data[0]);
__m128 _scale_out1 = _scale_out0;
if (scale_out_data_size > 1)
{
_scale_out0 = _mm_loadu_ps((const float*)scale_out_data);
_scale_out1 = _mm_loadu_ps((const float*)scale_out_data + 4);
}

if (bias_data_size == 0)
{
int i = 0;
for (; i < elemcount; i++)
{
__m128 _v0 = _mm_cvtepi32_ps(_mm_loadu_si128((const __m128i*)intptr0));
__m128 _v1 = _mm_cvtepi32_ps(_mm_loadu_si128((const __m128i*)intptr1));
_v0 = _mm_mul_ps(_v0, _scale_in0);
_v1 = _mm_mul_ps(_v1, _scale_in1);
_v0 = activation_sse(_v0, activation_type, activation_params);
_v1 = activation_sse(_v1, activation_type, activation_params);
_v0 = _mm_mul_ps(_v0, _scale_out0);
_v1 = _mm_mul_ps(_v1, _scale_out1);
*(int64_t*)ptr = float2int8_sse(_v0, _v1);
intptr0 += 4;
intptr1 += 4;
ptr += 8;
}
}
else
{
__m128 _bias0 = _mm_set1_ps(bias_data[0]);
__m128 _bias1 = _bias0;
if (bias_data_size > 1)
{
_bias0 = _mm_loadu_ps((const float*)bias_data);
_bias1 = _mm_loadu_ps((const float*)bias_data + 4);
}

int i = 0;
for (; i < elemcount; i++)
{
__m128 _v0 = _mm_cvtepi32_ps(_mm_loadu_si128((const __m128i*)intptr0));
__m128 _v1 = _mm_cvtepi32_ps(_mm_loadu_si128((const __m128i*)intptr1));
_v0 = _mm_comp_fmadd_ps(_v0, _scale_in0, _bias0);
_v1 = _mm_comp_fmadd_ps(_v1, _scale_in1, _bias1);
_v0 = activation_sse(_v0, activation_type, activation_params);
_v1 = activation_sse(_v1, activation_type, activation_params);
_v0 = _mm_mul_ps(_v0, _scale_out0);
_v1 = _mm_mul_ps(_v1, _scale_out1);
*(int64_t*)ptr = float2int8_sse(_v0, _v1);
intptr0 += 4;
intptr1 += 4;
ptr += 8;
}
}
}
#endif // !__AVX__
#endif // __SSE2__

int Requantize_x86::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
{
const int dims = bottom_blob.dims;
@@ -337,11 +413,20 @@ int Requantize_x86::forward(const Mat& bottom_blob, Mat& top_blob, const Option&
const int h = bottom_blob.h;
const int channels = bottom_blob.c;
const int elempack = bottom_blob.elempack;
const size_t out_elemsize = elempack * 1u;

if (dims == 1)
{
top_blob.create(w, out_elemsize, elempack, opt.blob_allocator);
int out_elempack = 1;
#if __SSE2__
if (opt.use_packing_layout)
{
out_elempack = w * elempack % 8 == 0 ? 8 : 1;
}
#endif
const int outw = w * elempack / out_elempack;
const size_t out_elemsize = out_elempack * 1u;

top_blob.create(outw, out_elemsize, out_elempack, opt.blob_allocator);
if (top_blob.empty())
return -100;

@@ -368,41 +453,107 @@ int Requantize_x86::forward(const Mat& bottom_blob, Mat& top_blob, const Option&

if (dims == 2)
{
top_blob.create(w, h, out_elemsize, elempack, opt.blob_allocator);
int out_elempack = 1;
#if __SSE2__
if (opt.use_packing_layout)
{
out_elempack = h * elempack % 8 == 0 ? 8 : 1;
}
#endif
const int outh = h * elempack / out_elempack;
const size_t out_elemsize = out_elempack * 1u;

top_blob.create(w, outh, out_elemsize, out_elempack, opt.blob_allocator);
if (top_blob.empty())
return -100;

#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < h; i++)
#if __SSE2__
#if !__AVX__
if (elempack == 4 && out_elempack == 8)
{
const int* intptr = bottom_blob.row<const int>(i);
signed char* ptr = top_blob.row<signed char>(i);
#pragma omp parallel for num_threads(opt.num_threads)
for (int i = 0; i < outh; i++)
{
const int* intptr0 = bottom_blob.row<const int>(i * 2);
const int* intptr1 = bottom_blob.row<const int>(i * 2 + 1);
signed char* ptr = top_blob.row<signed char>(i);

const Mat scale_in_data_i = scale_in_data_size > 1 ? scale_in_data.range(i * elempack, elempack) : scale_in_data;
const Mat bias_data_i = bias_data_size > 1 ? bias_data.range(i * elempack, elempack) : bias_data;
const Mat scale_out_data_i = scale_out_data_size > 1 ? scale_out_data.range(i * elempack, elempack) : scale_out_data;
const Mat scale_in_data_i = scale_in_data_size > 1 ? scale_in_data.range(i * out_elempack, out_elempack) : scale_in_data;
const Mat bias_data_i = bias_data_size > 1 ? bias_data.range(i * out_elempack, out_elempack) : bias_data;
const Mat scale_out_data_i = scale_out_data_size > 1 ? scale_out_data.range(i * out_elempack, out_elempack) : scale_out_data;

requantize(intptr, ptr, scale_in_data_i, bias_data_i, scale_out_data_i, activation_type, activation_params, w, elempack);
requantize_pack4to8(intptr0, intptr1, ptr, scale_in_data_i, bias_data_i, scale_out_data_i, activation_type, activation_params, w);
}
}
#endif // !__AVX__
#endif // __SSE2__
if (elempack == out_elempack)
{
#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 Mat scale_in_data_i = scale_in_data_size > 1 ? scale_in_data.range(i * elempack, elempack) : scale_in_data;
const Mat bias_data_i = bias_data_size > 1 ? bias_data.range(i * elempack, elempack) : bias_data;
const Mat scale_out_data_i = scale_out_data_size > 1 ? scale_out_data.range(i * elempack, elempack) : scale_out_data;

requantize(intptr, ptr, scale_in_data_i, bias_data_i, scale_out_data_i, activation_type, activation_params, w, elempack);
}
}
}

if (dims == 3)
{
top_blob.create(w, h, channels, out_elemsize, elempack, opt.blob_allocator);
int out_elempack = 1;
#if __SSE2__
if (opt.use_packing_layout)
{
out_elempack = channels * elempack % 8 == 0 ? 8 : 1;
}
#endif
const int outc = channels * elempack / out_elempack;
const size_t out_elemsize = out_elempack * 1u;

top_blob.create(w, h, outc, out_elemsize, out_elempack, opt.blob_allocator);
if (top_blob.empty())
return -100;

#pragma omp parallel for num_threads(opt.num_threads)
for (int q = 0; q < channels; q++)
#if __SSE2__
#if !__AVX__
if (elempack == 4 && out_elempack == 8)
{
#pragma omp parallel for num_threads(opt.num_threads)
for (int q = 0; q < outc; q++)
{
const int* intptr0 = bottom_blob.channel(q * 2);
const int* intptr1 = bottom_blob.channel(q * 2 + 1);
signed char* ptr = top_blob.channel(q);

const Mat scale_in_data_q = scale_in_data_size > 1 ? scale_in_data.range(q * out_elempack, out_elempack) : scale_in_data;
const Mat bias_data_q = bias_data_size > 1 ? bias_data.range(q * out_elempack, out_elempack) : bias_data;
const Mat scale_out_data_q = scale_out_data_size > 1 ? scale_out_data.range(q * out_elempack, out_elempack) : scale_out_data;

requantize_pack4to8(intptr0, intptr1, ptr, scale_in_data_q, bias_data_q, scale_out_data_q, activation_type, activation_params, w * h);
}
}
#endif // !__AVX__
#endif // __SSE2__
if (elempack == out_elempack)
{
const int* intptr = bottom_blob.channel(q);
signed char* ptr = top_blob.channel(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 Mat scale_in_data_q = scale_in_data_size > 1 ? scale_in_data.range(q * elempack, elempack) : scale_in_data;
const Mat bias_data_q = bias_data_size > 1 ? bias_data.range(q * elempack, elempack) : bias_data;
const Mat scale_out_data_q = scale_out_data_size > 1 ? scale_out_data.range(q * elempack, elempack) : scale_out_data;
const Mat scale_in_data_q = scale_in_data_size > 1 ? scale_in_data.range(q * elempack, elempack) : scale_in_data;
const Mat bias_data_q = bias_data_size > 1 ? bias_data.range(q * elempack, elempack) : bias_data;
const Mat scale_out_data_q = scale_out_data_size > 1 ? scale_out_data.range(q * elempack, elempack) : scale_out_data;

requantize(intptr, ptr, scale_in_data_q, bias_data_q, scale_out_data_q, activation_type, activation_params, w * h, elempack);
requantize(intptr, ptr, scale_in_data_q, bias_data_q, scale_out_data_q, activation_type, activation_params, w * h, elempack);
}
}
}



+ 62
- 88
tests/test_requantize.cpp View File

@@ -14,7 +14,7 @@

#include "testutil.h"

static int test_requantize(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta)
static int test_requantize_pack1(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta)
{
ncnn::ParamDict pd;
pd.set(0, scale_in_data_size);
@@ -36,25 +36,25 @@ static int test_requantize(const ncnn::Mat& a, int scale_in_data_size, int scale
Randomize(weights[0], 0.0001, 0.001);
Randomize(weights[1], 10, 100);

int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING;
int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING | TEST_LAYER_DISABLE_AUTO_INPUT_PACKING;
int ret = test_layer("Requantize", pd, weights, a, 1, 0, flag);
if (ret != 0)
{
fprintf(stderr, "test_requantize failed a.dims=%d a=(%d %d %d) scale_in_data_size=%d scale_out_data_size=%d bias_data_size=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.c, scale_in_data_size, scale_out_data_size, bias_data_size, activation_type, activation_params[0], activation_params[1]);
fprintf(stderr, "test_requantize_pack1 failed a.dims=%d a=(%d %d %d) scale_in_data_size=%d scale_out_data_size=%d bias_data_size=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.c, scale_in_data_size, scale_out_data_size, bias_data_size, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_requantize(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size)
static int test_requantize_pack1(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size)
{
return 0
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 0, 0.f, 0.f)
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 1, 0.f, 0.f)
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 2, RandomFloat(0, 1), 0.f)
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 3, RandomFloat(-1, 0), RandomFloat(0, 1))
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 4, 0.f, 0.f)
|| test_requantize(a, scale_in_data_size, scale_out_data_size, bias_data_size, 5, 0.f, 0.f);
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 0, 0.f, 0.f)
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 1, 0.f, 0.f)
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 2, RandomFloat(0, 1), 0.f)
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 3, RandomFloat(-1, 0), RandomFloat(0, 1))
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 4, 0.f, 0.f)
|| test_requantize_pack1(a, scale_in_data_size, scale_out_data_size, bias_data_size, 5, 0.f, 0.f);
}

static int test_requantize_pack8(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta)
@@ -103,94 +103,68 @@ static int test_requantize_pack8(const ncnn::Mat& a, int scale_in_data_size, int
static int test_requantize_0()
{
return 0
|| test_requantize(RandomIntMat(5, 7, 24), 1, 1, 24)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 1, 1)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 1, 0)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 24, 24)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 24, 1)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 24, 0)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 24, 24)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 24, 1)
|| test_requantize(RandomIntMat(5, 7, 24), 1, 24, 0)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 1, 24)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 1, 1)
|| test_requantize(RandomIntMat(5, 7, 24), 24, 1, 0)
|| test_requantize(RandomIntMat(7, 9, 12), 1, 1, 12)
|| test_requantize(RandomIntMat(7, 9, 12), 1, 1, 1)
|| test_requantize(RandomIntMat(7, 9, 12), 1, 1, 0)
|| test_requantize(RandomIntMat(7, 9, 12), 12, 12, 12)
|| test_requantize(RandomIntMat(7, 9, 12), 12, 12, 1)
|| test_requantize(RandomIntMat(7, 9, 12), 12, 12, 0)
|| test_requantize(RandomIntMat(7, 9, 12), 1, 12, 12)
|| test_requantize(RandomIntMat(7, 9, 12), 1, 12, 1)
|| test_requantize(RandomIntMat(7, 9, 12), 1, 12, 0)
|| test_requantize(RandomIntMat(7, 9, 12), 12, 1, 12)
|| test_requantize(RandomIntMat(7, 9, 12), 12, 1, 1)
|| test_requantize(RandomIntMat(7, 9, 12), 12, 1, 0)
|| test_requantize(RandomIntMat(3, 5, 13), 1, 1, 13)
|| test_requantize(RandomIntMat(3, 5, 13), 1, 1, 1)
|| test_requantize(RandomIntMat(3, 5, 13), 1, 1, 0)
|| test_requantize(RandomIntMat(3, 5, 13), 13, 13, 13)
|| test_requantize(RandomIntMat(3, 5, 13), 13, 13, 1)
|| test_requantize(RandomIntMat(3, 5, 13), 13, 13, 0)
|| test_requantize(RandomIntMat(3, 5, 13), 1, 13, 13)
|| test_requantize(RandomIntMat(3, 5, 13), 1, 13, 1)
|| test_requantize(RandomIntMat(3, 5, 13), 1, 13, 0)
|| test_requantize(RandomIntMat(3, 5, 13), 13, 1, 13)
|| test_requantize(RandomIntMat(3, 5, 13), 13, 1, 1)
|| test_requantize(RandomIntMat(3, 5, 13), 13, 1, 0);
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 1, 12)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 12, 12)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 12, 1)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 12, 0)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 12, 12)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 12, 1)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 1, 12, 0)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 1, 12)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 1, 1)
|| test_requantize_pack1(RandomIntMat(7, 9, 12), 12, 1, 0)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 1, 13)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 13, 13)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 13, 1)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 13, 0)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 13, 13)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 13, 1)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 1, 13, 0)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 1, 13)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 1, 1)
|| test_requantize_pack1(RandomIntMat(3, 5, 13), 13, 1, 0);
}

static int test_requantize_1()
{
return 0
|| test_requantize(RandomIntMat(15, 24), 1, 1, 24)
|| test_requantize(RandomIntMat(15, 24), 1, 1, 1)
|| test_requantize(RandomIntMat(15, 24), 1, 1, 0)
|| test_requantize(RandomIntMat(15, 24), 24, 24, 24)
|| test_requantize(RandomIntMat(15, 24), 24, 24, 1)
|| test_requantize(RandomIntMat(15, 24), 24, 24, 0)
|| test_requantize(RandomIntMat(15, 24), 1, 24, 24)
|| test_requantize(RandomIntMat(15, 24), 1, 24, 1)
|| test_requantize(RandomIntMat(15, 24), 1, 24, 0)
|| test_requantize(RandomIntMat(15, 24), 24, 1, 24)
|| test_requantize(RandomIntMat(15, 24), 24, 1, 1)
|| test_requantize(RandomIntMat(15, 24), 24, 1, 0)
|| test_requantize(RandomIntMat(17, 12), 1, 1, 12)
|| test_requantize(RandomIntMat(17, 12), 1, 1, 1)
|| test_requantize(RandomIntMat(17, 12), 1, 1, 0)
|| test_requantize(RandomIntMat(17, 12), 12, 12, 12)
|| test_requantize(RandomIntMat(17, 12), 12, 12, 1)
|| test_requantize(RandomIntMat(17, 12), 12, 12, 0)
|| test_requantize(RandomIntMat(17, 12), 1, 12, 12)
|| test_requantize(RandomIntMat(17, 12), 1, 12, 1)
|| test_requantize(RandomIntMat(17, 12), 1, 12, 0)
|| test_requantize(RandomIntMat(17, 12), 12, 1, 12)
|| test_requantize(RandomIntMat(17, 12), 12, 1, 1)
|| test_requantize(RandomIntMat(17, 12), 12, 1, 0)
|| test_requantize(RandomIntMat(19, 15), 1, 1, 15)
|| test_requantize(RandomIntMat(19, 15), 1, 1, 1)
|| test_requantize(RandomIntMat(19, 15), 1, 1, 0)
|| test_requantize(RandomIntMat(19, 15), 15, 15, 15)
|| test_requantize(RandomIntMat(19, 15), 15, 15, 1)
|| test_requantize(RandomIntMat(19, 15), 15, 15, 0)
|| test_requantize(RandomIntMat(19, 15), 1, 15, 15)
|| test_requantize(RandomIntMat(19, 15), 1, 15, 1)
|| test_requantize(RandomIntMat(19, 15), 1, 15, 0)
|| test_requantize(RandomIntMat(19, 15), 15, 1, 15)
|| test_requantize(RandomIntMat(19, 15), 15, 1, 1)
|| test_requantize(RandomIntMat(19, 15), 15, 1, 0);
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 1, 12)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 12, 12)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 12, 1)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 12, 0)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 12, 12)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 12, 1)
|| test_requantize_pack1(RandomIntMat(17, 12), 1, 12, 0)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 1, 12)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 1, 1)
|| test_requantize_pack1(RandomIntMat(17, 12), 12, 1, 0)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 1, 15)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 15, 15)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 15, 1)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 15, 0)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 15, 15)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 15, 1)
|| test_requantize_pack1(RandomIntMat(19, 15), 1, 15, 0)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 1, 15)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 1, 1)
|| test_requantize_pack1(RandomIntMat(19, 15), 15, 1, 0);
}

static int test_requantize_2()
{
return 0
|| test_requantize(RandomIntMat(128), 1, 1, 1)
|| test_requantize(RandomIntMat(128), 1, 1, 0)
|| test_requantize(RandomIntMat(124), 1, 1, 1)
|| test_requantize(RandomIntMat(124), 1, 1, 0)
|| test_requantize(RandomIntMat(127), 1, 1, 1)
|| test_requantize(RandomIntMat(127), 1, 1, 0);
|| test_requantize_pack1(RandomIntMat(124), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(124), 1, 1, 0)
|| test_requantize_pack1(RandomIntMat(127), 1, 1, 1)
|| test_requantize_pack1(RandomIntMat(127), 1, 1, 0);
}

static int test_requantize_3()


+ 139
- 0
tests/test_requantize_oom.cpp View File

@@ -0,0 +1,139 @@
// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "testutil.h"

static int test_requantize_pack1_oom(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta)
{
ncnn::ParamDict pd;
pd.set(0, scale_in_data_size);
pd.set(1, scale_out_data_size);
pd.set(2, bias_data_size);

ncnn::Mat activation_params(2);
activation_params[0] = alpha;
activation_params[1] = beta;
pd.set(3, activation_type);
pd.set(4, activation_params);

std::vector<ncnn::Mat> weights(bias_data_size ? 3 : 2);
weights[0] = RandomMat(scale_in_data_size);
weights[1] = RandomMat(scale_out_data_size);
if (bias_data_size)
weights[2] = RandomMat(bias_data_size);

Randomize(weights[0], 0.0001, 0.001);
Randomize(weights[1], 10, 100);

int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING | TEST_LAYER_DISABLE_AUTO_INPUT_PACKING;
int ret = test_layer_oom("Requantize", pd, weights, a, flag);
if (ret != 0)
{
fprintf(stderr, "test_requantize_pack1_oom failed a.dims=%d a=(%d %d %d) scale_in_data_size=%d scale_out_data_size=%d bias_data_size=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.c, scale_in_data_size, scale_out_data_size, bias_data_size, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_requantize_pack1_oom(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size)
{
return 0
|| test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 0, 0.f, 0.f)
|| test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 1, 0.f, 0.f)
|| test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 2, RandomFloat(0, 1), 0.f)
|| test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 3, RandomFloat(-1, 0), RandomFloat(0, 1))
|| test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 4, 0.f, 0.f)
|| test_requantize_pack1_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 5, 0.f, 0.f);
}

static int test_requantize_pack8_oom(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size, int activation_type, float alpha, float beta)
{
ncnn::ParamDict pd;
pd.set(0, scale_in_data_size);
pd.set(1, scale_out_data_size);
pd.set(2, bias_data_size);

ncnn::Mat activation_params(2);
activation_params[0] = alpha;
activation_params[1] = beta;
pd.set(3, activation_type);
pd.set(4, activation_params);

std::vector<ncnn::Mat> weights(bias_data_size ? 3 : 2);
weights[0] = RandomMat(scale_in_data_size);
weights[1] = RandomMat(scale_out_data_size);
if (bias_data_size)
weights[2] = RandomMat(bias_data_size);

Randomize(weights[0], 0.0001, 0.001);
Randomize(weights[1], 10, 100);

int flag = TEST_LAYER_DISABLE_AUTO_INPUT_CASTING | TEST_LAYER_ENABLE_FORCE_INPUT_PACK8;
int ret = test_layer_oom("Requantize", pd, weights, a, flag);
if (ret != 0)
{
fprintf(stderr, "test_requantize_pack8_oom failed a.dims=%d a=(%d %d %d) scale_in_data_size=%d scale_out_data_size=%d bias_data_size=%d act=%d actparams=[%f,%f]\n", a.dims, a.w, a.h, a.c, scale_in_data_size, scale_out_data_size, bias_data_size, activation_type, activation_params[0], activation_params[1]);
}

return ret;
}

static int test_requantize_pack8_oom(const ncnn::Mat& a, int scale_in_data_size, int scale_out_data_size, int bias_data_size)
{
return 0
|| test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 0, 0.f, 0.f)
|| test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 1, 0.f, 0.f)
|| test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 2, RandomFloat(0, 1), 0.f)
|| test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 3, RandomFloat(-1, 0), RandomFloat(0, 1))
|| test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 4, 0.f, 0.f)
|| test_requantize_pack8_oom(a, scale_in_data_size, scale_out_data_size, bias_data_size, 5, 0.f, 0.f);
}

static int test_requantize_0()
{
return 0
|| test_requantize_pack1_oom(RandomIntMat(7, 9, 12), 12, 12, 12)
|| test_requantize_pack1_oom(RandomIntMat(3, 5, 13), 13, 13, 13);
}

static int test_requantize_1()
{
return 0
|| test_requantize_pack1_oom(RandomIntMat(17, 12), 12, 12, 12)
|| test_requantize_pack1_oom(RandomIntMat(19, 15), 15, 15, 15);
}

static int test_requantize_2()
{
return test_requantize_pack1_oom(RandomIntMat(124), 1, 1, 1);
}

static int test_requantize_3()
{
return 0
|| test_requantize_pack8_oom(RandomIntMat(5, 7, 24), 24, 24, 24)
|| test_requantize_pack8_oom(RandomIntMat(15, 24), 24, 24, 24)
|| test_requantize_pack8_oom(RandomIntMat(128), 1, 1, 1);
}

int main()
{
SRAND(7767517);

return 0
|| test_requantize_0()
|| test_requantize_1()
|| test_requantize_2()
|| test_requantize_3();
}

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