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deepin-ocr/3rdparty/ncnn/tests/testutil.h
wangzhengyang 718c41634f feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake 
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试
2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程
3.重整权利声明文件，重整代码工程，确保最小化侵权风险

Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake
Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
2022-05-10 10:22:11 +08:00

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// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2019 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.
#ifndef TESTUTIL_H
#define TESTUTIL_H
#include "cpu.h"
#include "layer.h"
#include "mat.h"
#include "prng.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#if NCNN_VULKAN
#include "command.h"
#include "gpu.h"
#endif // NCNN_VULKAN
static struct prng_rand_t g_prng_rand_state;
#if NCNN_VULKAN
class GlobalGpuInstance
{
public:
GlobalGpuInstance()
{
ncnn::create_gpu_instance();
}
~GlobalGpuInstance()
{
ncnn::destroy_gpu_instance();
}
};
// HACK workaround nvidia driver crash on exit
#define SRAND(seed) \
GlobalGpuInstance __ncnn_gpu_instance_guard; \
prng_srand(seed, &g_prng_rand_state)
#define RAND() prng_rand(&g_prng_rand_state)
#else // NCNN_VULKAN
#define SRAND(seed) prng_srand(seed, &g_prng_rand_state)
#define RAND() prng_rand(&g_prng_rand_state)
#endif // NCNN_VULKAN
#define TEST_LAYER_DISABLE_AUTO_INPUT_PACKING (1 << 0)
#define TEST_LAYER_DISABLE_AUTO_INPUT_CASTING (1 << 1)
#define TEST_LAYER_DISABLE_GPU_TESTING (1 << 2)
#define TEST_LAYER_ENABLE_FORCE_INPUT_PACK8 (1 << 3)
static float RandomFloat(float a = -1.2f, float b = 1.2f)
{
float random = ((float)RAND()) / (float)uint64_t(-1); //RAND_MAX;
float diff = b - a;
float r = random * diff;
return a + r;
}
static int RandomInt(int a = -10000, int b = 10000)
{
float random = ((float)RAND()) / (float)uint64_t(-1); //RAND_MAX;
int diff = b - a;
float r = random * diff;
return a + (int)r;
}
static signed char RandomS8()
{
return (signed char)RandomInt(-127, 127);
}
static void Randomize(ncnn::Mat& m, float a = -1.2f, float b = 1.2f)
{
for (size_t i = 0; i < m.total(); i++)
{
m[i] = RandomFloat(a, b);
}
}
static void RandomizeInt(ncnn::Mat& m, int a = -10000, int b = 10000)
{
for (size_t i = 0; i < m.total(); i++)
{
((int*)m)[i] = RandomInt(a, b);
}
}
static void RandomizeS8(ncnn::Mat& m)
{
for (size_t i = 0; i < m.total(); i++)
{
((signed char*)m)[i] = RandomS8();
}
}
static ncnn::Mat RandomMat(int w)
{
ncnn::Mat m(w);
Randomize(m);
return m;
}
static ncnn::Mat RandomMat(int w, int h)
{
ncnn::Mat m(w, h);
Randomize(m);
return m;
}
static ncnn::Mat RandomMat(int w, int h, int c)
{
ncnn::Mat m(w, h, c);
Randomize(m);
return m;
}
static ncnn::Mat RandomMat(int w, int h, int d, int c)
{
ncnn::Mat m(w, h, d, c);
Randomize(m);
return m;
}
static ncnn::Mat RandomIntMat(int w)
{
ncnn::Mat m(w);
RandomizeInt(m);
return m;
}
static ncnn::Mat RandomIntMat(int w, int h)
{
ncnn::Mat m(w, h);
RandomizeInt(m);
return m;
}
static ncnn::Mat RandomIntMat(int w, int h, int c)
{
ncnn::Mat m(w, h, c);
RandomizeInt(m);
return m;
}
static ncnn::Mat RandomIntMat(int w, int h, int d, int c)
{
ncnn::Mat m(w, h, d, c);
RandomizeInt(m);
return m;
}
static ncnn::Mat RandomS8Mat(int w)
{
ncnn::Mat m(w, (size_t)1u);
RandomizeS8(m);
return m;
}
static ncnn::Mat RandomS8Mat(int w, int h)
{
ncnn::Mat m(w, h, (size_t)1u);
RandomizeS8(m);
return m;
}
static ncnn::Mat RandomS8Mat(int w, int h, int c)
{
ncnn::Mat m(w, h, c, (size_t)1u);
RandomizeS8(m);
return m;
}
static ncnn::Mat RandomS8Mat(int w, int h, int d, int c)
{
ncnn::Mat m(w, h, d, c, (size_t)1u);
RandomizeS8(m);
return m;
}
static ncnn::Mat scales_mat(const ncnn::Mat& mat, int m, int k, int ldx)
{
ncnn::Mat weight_scales(m);
for (int i = 0; i < m; ++i)
{
float min = mat[0], _max = mat[0];
const float* ptr = (const float*)(mat.data) + i * ldx;
for (int j = 0; j < k; ++j)
{
if (min > ptr[j])
{
min = ptr[j];
}
if (_max < ptr[j])
{
_max = ptr[j];
}
}
const float abs_min = abs(min), abs_max = abs(_max);
weight_scales[i] = 127.f / (abs_min > abs_max ? abs_min : abs_max);
}
return weight_scales;
}
static bool NearlyEqual(float a, float b, float epsilon)
{
if (a == b)
return true;
float diff = (float)fabs(a - b);
if (diff <= epsilon)
return true;
// relative error
return diff < epsilon * std::max(fabs(a), fabs(b));
}
static int Compare(const ncnn::Mat& a, const ncnn::Mat& b, float epsilon = 0.001)
{
#define CHECK_MEMBER(m) \
if (a.m != b.m) \
{ \
fprintf(stderr, #m " not match expect %d but got %d\n", (int)a.m, (int)b.m); \
return -1; \
}
CHECK_MEMBER(dims)
CHECK_MEMBER(w)
CHECK_MEMBER(h)
CHECK_MEMBER(d)
CHECK_MEMBER(c)
CHECK_MEMBER(elemsize)
CHECK_MEMBER(elempack)
#undef CHECK_MEMBER
for (int q = 0; q < a.c; q++)
{
const ncnn::Mat ma = a.channel(q);
const ncnn::Mat mb = b.channel(q);
for (int z = 0; z < a.d; z++)
{
const ncnn::Mat da = ma.depth(z);
const ncnn::Mat db = mb.depth(z);
for (int i = 0; i < a.h; i++)
{
const float* pa = da.row(i);
const float* pb = db.row(i);
for (int j = 0; j < a.w; j++)
{
if (!NearlyEqual(pa[j], pb[j], epsilon))
{
fprintf(stderr, "value not match at c:%d d:%d h:%d w:%d expect %f but got %f\n", q, z, i, j, pa[j], pb[j]);
return -1;
}
}
}
}
}
return 0;
}
static int CompareMat(const ncnn::Mat& a, const ncnn::Mat& b, float epsilon = 0.001)
{
ncnn::Option opt;
opt.num_threads = 1;
if (a.elempack != 1)
{
ncnn::Mat a1;
ncnn::convert_packing(a, a1, 1, opt);
return CompareMat(a1, b, epsilon);
}
if (b.elempack != 1)
{
ncnn::Mat b1;
ncnn::convert_packing(b, b1, 1, opt);
return CompareMat(a, b1, epsilon);
}
if (a.elemsize == 2u)
{
ncnn::Mat a32;
cast_float16_to_float32(a, a32, opt);
return CompareMat(a32, b, epsilon);
}
if (a.elemsize == 1u)
{
ncnn::Mat a32;
cast_int8_to_float32(a, a32, opt);
return CompareMat(a32, b, epsilon);
}
if (b.elemsize == 2u)
{
ncnn::Mat b32;
cast_float16_to_float32(b, b32, opt);
return CompareMat(a, b32, epsilon);
}
if (b.elemsize == 1u)
{
ncnn::Mat b32;
cast_int8_to_float32(b, b32, opt);
return CompareMat(a, b32, epsilon);
}
return Compare(a, b, epsilon);
}
static int CompareMat(const std::vector<ncnn::Mat>& a, const std::vector<ncnn::Mat>& b, float epsilon = 0.001)
{
if (a.size() != b.size())
{
fprintf(stderr, "output blob count not match %zu %zu\n", a.size(), b.size());
return -1;
}
for (size_t i = 0; i < a.size(); i++)
{
if (CompareMat(a[i], b[i], epsilon))
{
fprintf(stderr, "output blob %zu not match\n", i);
return -1;
}
}
return 0;
}
template<typename T>
int test_layer_naive(int typeindex, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const std::vector<ncnn::Mat>& a, int top_blob_count, std::vector<ncnn::Mat>& b, void (*func)(T*), int flag)
{
ncnn::Layer* op = ncnn::create_layer(typeindex);
if (func)
{
(*func)((T*)op);
}
op->load_param(pd);
if (op->one_blob_only && a.size() != 1)
{
fprintf(stderr, "layer with one_blob_only but consume multiple inputs\n");
delete op;
return -1;
}
ncnn::ModelBinFromMatArray mb(weights.data());
op->load_model(mb);
ncnn::Option opt;
opt.num_threads = 1;
opt.use_packing_layout = false;
opt.use_fp16_packed = false;
opt.use_fp16_storage = false;
opt.use_fp16_arithmetic = false;
opt.use_shader_pack8 = false;
opt.use_image_storage = false;
opt.use_bf16_storage = false;
opt.use_vulkan_compute = false;
op->create_pipeline(opt);
b.resize(top_blob_count);
if (op->support_inplace)
{
for (size_t i = 0; i < a.size(); i++)
{
b[i] = a[i].clone();
}
((T*)op)->T::forward_inplace(b, opt);
}
else
{
((T*)op)->T::forward(a, b, opt);
}
op->destroy_pipeline(opt);
delete op;
return 0;
}
template<typename T>
int test_layer_cpu(int typeindex, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& _opt, const std::vector<ncnn::Mat>& a, int top_blob_count, std::vector<ncnn::Mat>& c, const std::vector<ncnn::Mat>& top_shapes, void (*func)(T*), int flag)
{
ncnn::Layer* op = ncnn::create_layer(typeindex);
if (func)
{
(*func)((T*)op);
}
if (!top_shapes.empty())
{
op->bottom_shapes = a;
op->top_shapes = top_shapes;
}
op->load_param(pd);
if (op->one_blob_only && a.size() != 1)
{
fprintf(stderr, "layer with one_blob_only but consume multiple inputs\n");
delete op;
return -1;
}
ncnn::ModelBinFromMatArray mb(weights.data());
op->load_model(mb);
ncnn::Option opt = _opt;
opt.num_threads = 1;
opt.use_vulkan_compute = false;
op->create_pipeline(opt);
std::vector<ncnn::Mat> a4(a.size());
for (size_t i = 0; i < a4.size(); i++)
{
if (opt.use_fp16_storage && op->support_fp16_storage && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
ncnn::cast_float32_to_float16(a[i], a4[i], opt);
}
else if (opt.use_bf16_storage && op->support_bf16_storage && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
ncnn::cast_float32_to_bfloat16(a[i], a4[i], opt);
}
else
{
a4[i] = a[i];
}
if (opt.use_packing_layout && op->support_packing && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_PACKING))
{
// resolve dst_elempack
int dims = a4[i].dims;
int elemcount = 0;
if (dims == 1) elemcount = a4[i].elempack * a4[i].w;
if (dims == 2) elemcount = a4[i].elempack * a4[i].h;
if (dims == 3 || dims == 4) elemcount = a4[i].elempack * a4[i].c;
int elembits = a4[i].elembits();
int dst_elempack = 1;
if (elembits == 32)
{
#if NCNN_AVX512
if (elemcount % 16 == 0 && ncnn::cpu_support_x86_avx512())
dst_elempack = 16;
else if (elemcount % 8 == 0 && ncnn::cpu_support_x86_avx())
dst_elempack = 8;
else if (elemcount % 4 == 0)
dst_elempack = 4;
#elif NCNN_AVX
if (elemcount % 8 == 0 && ncnn::cpu_support_x86_avx())
dst_elempack = 8;
else if (elemcount % 4 == 0)
dst_elempack = 4;
#elif NCNN_RVV
const int packn = ncnn::cpu_riscv_vlenb() / (elembits / 8);
if (elemcount % packn == 0)
dst_elempack = packn;
#else
if (elemcount % 4 == 0)
dst_elempack = 4;
#endif
}
if (elembits == 16)
{
#if NCNN_ARM82
if (elemcount % 8 == 0 && opt.use_fp16_storage && opt.use_fp16_arithmetic && op->support_fp16_storage)
dst_elempack = 8;
else if (elemcount % 4 == 0)
dst_elempack = 4;
#elif NCNN_RVV
const int packn = ncnn::cpu_riscv_vlenb() / 2;
if (elemcount % packn == 0)
dst_elempack = packn;
#else
if (elemcount % 4 == 0)
dst_elempack = 4;
#endif
}
if (elembits == 8)
{
#if NCNN_RVV
const int packn = ncnn::cpu_riscv_vlenb() / 1;
if (elemcount % packn == 0)
dst_elempack = packn;
#else
if (elemcount % 8 == 0)
dst_elempack = 8;
#endif
}
if (flag & TEST_LAYER_ENABLE_FORCE_INPUT_PACK8)
dst_elempack = 8;
ncnn::Mat a4_packed;
ncnn::convert_packing(a4[i], a4_packed, dst_elempack, opt);
a4[i] = a4_packed;
}
}
c.resize(top_blob_count);
if (op->support_inplace)
{
for (size_t i = 0; i < a4.size(); i++)
{
c[i] = a4[i].clone();
}
op->forward_inplace(c, opt);
}
else
{
op->forward(a4, c, opt);
}
for (size_t i = 0; i < c.size(); i++)
{
if (opt.use_fp16_storage && op->support_fp16_storage && c[i].elembits() == 16)
{
ncnn::Mat c_fp32;
ncnn::cast_float16_to_float32(c[i], c_fp32, opt);
c[i] = c_fp32;
}
else if (opt.use_bf16_storage && op->support_bf16_storage && c[i].elembits() == 16)
{
ncnn::Mat c_fp32;
ncnn::cast_bfloat16_to_float32(c[i], c_fp32, opt);
c[i] = c_fp32;
}
}
op->destroy_pipeline(opt);
delete op;
return 0;
}
#if NCNN_VULKAN
template<typename T>
int test_layer_gpu(int typeindex, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& _opt, const std::vector<ncnn::Mat>& a, int top_blob_count, std::vector<ncnn::Mat>& d, const std::vector<ncnn::Mat>& top_shapes, void (*func)(T*), int flag)
{
ncnn::Layer* op = ncnn::create_layer(typeindex);
if (!op->support_vulkan)
{
delete op;
return 233;
}
ncnn::VulkanDevice* vkdev = ncnn::get_gpu_device();
op->vkdev = vkdev;
if (func)
{
(*func)((T*)op);
}
if (!top_shapes.empty())
{
op->bottom_shapes = a;
op->top_shapes = top_shapes;
}
op->load_param(pd);
if (op->one_blob_only && a.size() != 1)
{
fprintf(stderr, "layer with one_blob_only but consume multiple inputs\n");
delete op;
return -1;
}
ncnn::ModelBinFromMatArray mb(weights.data());
op->load_model(mb);
ncnn::VkWeightAllocator g_weight_vkallocator(vkdev);
ncnn::VkWeightStagingAllocator g_weight_staging_vkallocator(vkdev);
ncnn::VkAllocator* blob_vkallocator = vkdev->acquire_blob_allocator();
ncnn::VkAllocator* staging_vkallocator = vkdev->acquire_staging_allocator();
ncnn::Option opt = _opt;
opt.num_threads = 1;
opt.use_vulkan_compute = true;
#if __APPLE__
opt.use_image_storage = false;
#endif
opt.blob_vkallocator = blob_vkallocator;
opt.workspace_vkallocator = blob_vkallocator;
opt.staging_vkallocator = staging_vkallocator;
if (!vkdev->info.support_fp16_packed()) opt.use_fp16_packed = false;
if (!vkdev->info.support_fp16_storage()) opt.use_fp16_storage = false;
if (!vkdev->info.support_fp16_arithmetic()) opt.use_fp16_arithmetic = false;
// FIXME fp16a may produce large error
opt.use_fp16_arithmetic = false;
op->create_pipeline(opt);
if (!op->support_vulkan)
{
delete op;
return 233;
}
{
ncnn::VkTransfer cmd(vkdev);
ncnn::Option opt_upload = opt;
opt_upload.blob_vkallocator = &g_weight_vkallocator;
opt_upload.workspace_vkallocator = &g_weight_vkallocator;
opt_upload.staging_vkallocator = &g_weight_staging_vkallocator;
op->upload_model(cmd, opt_upload);
cmd.submit_and_wait();
}
d.resize(top_blob_count);
{
// forward
ncnn::VkCompute cmd(vkdev);
if (op->support_image_storage && opt.use_image_storage)
{
// upload
std::vector<ncnn::VkImageMat> a_gpu(a.size());
for (size_t i = 0; i < a_gpu.size(); i++)
{
cmd.record_upload(a[i], a_gpu[i], opt);
}
std::vector<ncnn::VkImageMat> d_gpu(top_blob_count);
if (op->support_inplace)
{
op->forward_inplace(a_gpu, cmd, opt);
d_gpu = a_gpu;
}
else
{
op->forward(a_gpu, d_gpu, cmd, opt);
}
// download
for (size_t i = 0; i < d_gpu.size(); i++)
{
cmd.record_download(d_gpu[i], d[i], opt);
}
}
else
{
// upload
std::vector<ncnn::VkMat> a_gpu(a.size());
for (size_t i = 0; i < a_gpu.size(); i++)
{
cmd.record_upload(a[i], a_gpu[i], opt);
}
std::vector<ncnn::VkMat> d_gpu(top_blob_count);
if (op->support_inplace)
{
op->forward_inplace(a_gpu, cmd, opt);
d_gpu = a_gpu;
}
else
{
op->forward(a_gpu, d_gpu, cmd, opt);
}
// download
for (size_t i = 0; i < d_gpu.size(); i++)
{
cmd.record_download(d_gpu[i], d[i], opt);
}
}
cmd.submit_and_wait();
}
op->destroy_pipeline(opt);
delete op;
vkdev->reclaim_blob_allocator(blob_vkallocator);
vkdev->reclaim_staging_allocator(staging_vkallocator);
g_weight_vkallocator.clear();
g_weight_staging_vkallocator.clear();
return 0;
}
#endif // NCNN_VULKAN
template<typename T>
int test_layer(int typeindex, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& _opt, const std::vector<ncnn::Mat>& a, int top_blob_count, const std::vector<ncnn::Mat>& top_shapes = std::vector<ncnn::Mat>(), float epsilon = 0.001, void (*func)(T*) = 0, int flag = 0)
{
// naive
std::vector<ncnn::Mat> b;
{
int ret = test_layer_naive(typeindex, pd, weights, a, top_blob_count, b, func, flag);
if (ret != 0)
{
fprintf(stderr, "test_layer_naive failed\n");
return -1;
}
}
// cpu
{
std::vector<ncnn::Mat> c;
int ret = test_layer_cpu(typeindex, pd, weights, _opt, a, top_blob_count, c, std::vector<ncnn::Mat>(), func, flag);
if (ret != 0 || CompareMat(b, c, epsilon) != 0)
{
fprintf(stderr, "test_layer_cpu failed\n");
return -1;
}
}
// cpu shape hint
{
std::vector<ncnn::Mat> c;
int ret = test_layer_cpu(typeindex, pd, weights, _opt, a, top_blob_count, c, b, func, flag);
if (ret != 0 || CompareMat(b, c, epsilon) != 0)
{
fprintf(stderr, "test_layer_cpu failed with shape hint\n");
return -1;
}
}
#if NCNN_VULKAN
// gpu
if (!(flag & TEST_LAYER_DISABLE_GPU_TESTING))
{
std::vector<ncnn::Mat> d;
int ret = test_layer_gpu(typeindex, pd, weights, _opt, a, top_blob_count, d, std::vector<ncnn::Mat>(), func, flag);
if (ret != 233 && (ret != 0 || CompareMat(b, d, epsilon) != 0))
{
fprintf(stderr, "test_layer_gpu failed\n");
return -1;
}
}
// gpu shape hint
if (!(flag & TEST_LAYER_DISABLE_GPU_TESTING))
{
std::vector<ncnn::Mat> d;
int ret = test_layer_gpu(typeindex, pd, weights, _opt, a, top_blob_count, d, b, func, flag);
if (ret != 233 && (ret != 0 || CompareMat(b, d, epsilon) != 0))
{
fprintf(stderr, "test_layer_gpu failed with shape hint\n");
return -1;
}
}
#endif // NCNN_VULKAN
return 0;
}
template<typename T>
int test_layer_naive(int typeindex, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Mat& a, ncnn::Mat& b, void (*func)(T*), int flag)
{
ncnn::Layer* op = ncnn::create_layer(typeindex);
if (func)
{
(*func)((T*)op);
}
op->load_param(pd);
ncnn::ModelBinFromMatArray mb(weights.data());
op->load_model(mb);
ncnn::Option opt;
opt.num_threads = 1;
opt.use_packing_layout = false;
opt.use_fp16_packed = false;
opt.use_fp16_storage = false;
opt.use_fp16_arithmetic = false;
opt.use_shader_pack8 = false;
opt.use_image_storage = false;
opt.use_bf16_storage = false;
opt.use_vulkan_compute = false;
op->create_pipeline(opt);
if (op->support_inplace)
{
b = a.clone();
((T*)op)->T::forward_inplace(b, opt);
}
else
{
((T*)op)->T::forward(a, b, opt);
}
op->destroy_pipeline(opt);
delete op;
return 0;
}
template<typename T>
int test_layer_cpu(int typeindex, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& _opt, const ncnn::Mat& a, ncnn::Mat& c, const ncnn::Mat& top_shape, void (*func)(T*), int flag)
{
ncnn::Layer* op = ncnn::create_layer(typeindex);
if (func)
{
(*func)((T*)op);
}
if (top_shape.dims)
{
op->bottom_shapes.resize(1);
op->top_shapes.resize(1);
op->bottom_shapes[0] = a;
op->top_shapes[0] = top_shape;
}
op->load_param(pd);
ncnn::ModelBinFromMatArray mb(weights.data());
op->load_model(mb);
ncnn::Option opt = _opt;
opt.num_threads = 1;
opt.use_vulkan_compute = false;
op->create_pipeline(opt);
ncnn::Mat a4;
if (opt.use_fp16_storage && op->support_fp16_storage && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
ncnn::cast_float32_to_float16(a, a4, opt);
}
else if (opt.use_bf16_storage && op->support_bf16_storage && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
ncnn::cast_float32_to_bfloat16(a, a4, opt);
}
else
{
a4 = a;
}
if (opt.use_packing_layout && op->support_packing && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_PACKING))
{
// resolve dst_elempack
int dims = a4.dims;
int elemcount = 0;
if (dims == 1) elemcount = a4.elempack * a4.w;
if (dims == 2) elemcount = a4.elempack * a4.h;
if (dims == 3 || dims == 4) elemcount = a4.elempack * a4.c;
int elembits = a4.elembits();
int dst_elempack = 1;
if (elembits == 32)
{
#if NCNN_AVX512
if (elemcount % 16 == 0 && ncnn::cpu_support_x86_avx512())
dst_elempack = 16;
else if (elemcount % 8 == 0 && ncnn::cpu_support_x86_avx())
dst_elempack = 8;
else if (elemcount % 4 == 0)
dst_elempack = 4;
#elif NCNN_AVX
if (elemcount % 8 == 0 && ncnn::cpu_support_x86_avx())
dst_elempack = 8;
else if (elemcount % 4 == 0)
dst_elempack = 4;
#elif NCNN_RVV
const int packn = ncnn::cpu_riscv_vlenb() / (elembits / 8);
if (elemcount % packn == 0)
dst_elempack = packn;
#else
if (elemcount % 4 == 0)
dst_elempack = 4;
#endif
}
if (elembits == 16)
{
#if NCNN_ARM82
if (elemcount % 8 == 0 && opt.use_fp16_storage && opt.use_fp16_arithmetic && op->support_fp16_storage)
dst_elempack = 8;
else if (elemcount % 4 == 0)
dst_elempack = 4;
#elif NCNN_RVV
const int packn = ncnn::cpu_riscv_vlenb() / 2;
if (elemcount % packn == 0)
dst_elempack = packn;
#else
if (elemcount % 4 == 0)
dst_elempack = 4;
#endif
}
if (elembits == 8)
{
#if NCNN_RVV
const int packn = ncnn::cpu_riscv_vlenb() / 1;
if (elemcount % packn == 0)
dst_elempack = packn;
#else
if (elemcount % 8 == 0)
dst_elempack = 8;
#endif
}
if (flag & TEST_LAYER_ENABLE_FORCE_INPUT_PACK8)
dst_elempack = 8;
ncnn::Mat a4_packed;
ncnn::convert_packing(a4, a4_packed, dst_elempack, opt);
a4 = a4_packed;
}
if (op->support_inplace)
{
c = a4.clone();
op->forward_inplace(c, opt);
}
else
{
op->forward(a4, c, opt);
}
if (opt.use_fp16_storage && op->support_fp16_storage && c.elembits() == 16)
{
ncnn::Mat c_fp32;
ncnn::cast_float16_to_float32(c, c_fp32, opt);
c = c_fp32;
}
else if (opt.use_bf16_storage && op->support_bf16_storage && c.elembits() == 16)
{
ncnn::Mat c_fp32;
ncnn::cast_bfloat16_to_float32(c, c_fp32, opt);
c = c_fp32;
}
op->destroy_pipeline(opt);
delete op;
return 0;
}
#if NCNN_VULKAN
template<typename T>
int test_layer_gpu(int typeindex, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& _opt, const ncnn::Mat& a, ncnn::Mat& d, const ncnn::Mat& top_shape, void (*func)(T*), int flag)
{
ncnn::Layer* op = ncnn::create_layer(typeindex);
if (!op->support_vulkan)
{
delete op;
return 233;
}
ncnn::VulkanDevice* vkdev = ncnn::get_gpu_device();
op->vkdev = vkdev;
if (func)
{
(*func)((T*)op);
}
if (top_shape.dims)
{
op->bottom_shapes.resize(1);
op->top_shapes.resize(1);
op->bottom_shapes[0] = a;
op->top_shapes[0] = top_shape;
}
op->load_param(pd);
ncnn::ModelBinFromMatArray mb(weights.data());
op->load_model(mb);
ncnn::VkWeightAllocator g_weight_vkallocator(vkdev);
ncnn::VkWeightStagingAllocator g_weight_staging_vkallocator(vkdev);
ncnn::VkAllocator* blob_vkallocator = vkdev->acquire_blob_allocator();
ncnn::VkAllocator* staging_vkallocator = vkdev->acquire_staging_allocator();
ncnn::Option opt = _opt;
opt.num_threads = 1;
opt.use_vulkan_compute = true;
#if __APPLE__
opt.use_image_storage = false;
#endif
opt.blob_vkallocator = blob_vkallocator;
opt.workspace_vkallocator = blob_vkallocator;
opt.staging_vkallocator = staging_vkallocator;
if (!vkdev->info.support_fp16_packed()) opt.use_fp16_packed = false;
if (!vkdev->info.support_fp16_storage()) opt.use_fp16_storage = false;
if (!vkdev->info.support_fp16_arithmetic()) opt.use_fp16_arithmetic = false;
// FIXME fp16a may produce large error
opt.use_fp16_arithmetic = false;
op->create_pipeline(opt);
if (!op->support_vulkan)
{
delete op;
return 233;
}
{
ncnn::VkTransfer cmd(vkdev);
ncnn::Option opt_upload = opt;
opt_upload.blob_vkallocator = &g_weight_vkallocator;
opt_upload.workspace_vkallocator = &g_weight_vkallocator;
opt_upload.staging_vkallocator = &g_weight_staging_vkallocator;
op->upload_model(cmd, opt_upload);
cmd.submit_and_wait();
}
{
// forward
ncnn::VkCompute cmd(vkdev);
if (op->support_image_storage && opt.use_image_storage)
{
// upload
ncnn::VkImageMat a_gpu;
cmd.record_upload(a, a_gpu, opt);
ncnn::VkImageMat d_gpu;
if (op->support_inplace)
{
op->forward_inplace(a_gpu, cmd, opt);
d_gpu = a_gpu;
}
else
{
op->forward(a_gpu, d_gpu, cmd, opt);
}
// download
cmd.record_download(d_gpu, d, opt);
}
else
{
// upload
ncnn::VkMat a_gpu;
cmd.record_upload(a, a_gpu, opt);
ncnn::VkMat d_gpu;
if (op->support_inplace)
{
op->forward_inplace(a_gpu, cmd, opt);
d_gpu = a_gpu;
}
else
{
op->forward(a_gpu, d_gpu, cmd, opt);
}
// download
cmd.record_download(d_gpu, d, opt);
}
cmd.submit_and_wait();
}
op->destroy_pipeline(opt);
delete op;
vkdev->reclaim_blob_allocator(blob_vkallocator);
vkdev->reclaim_staging_allocator(staging_vkallocator);
g_weight_vkallocator.clear();
g_weight_staging_vkallocator.clear();
return 0;
}
#endif // NCNN_VULKAN
template<typename T>
int test_layer(int typeindex, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& _opt, const ncnn::Mat& a, const ncnn::Mat& top_shape = ncnn::Mat(), float epsilon = 0.001, void (*func)(T*) = 0, int flag = 0)
{
// naive
ncnn::Mat b;
{
int ret = test_layer_naive(typeindex, pd, weights, a, b, func, flag);
if (ret != 0)
{
fprintf(stderr, "test_layer_naive failed\n");
return -1;
}
}
// cpu
{
ncnn::Mat c;
int ret = test_layer_cpu(typeindex, pd, weights, _opt, a, c, ncnn::Mat(), func, flag);
if (ret != 0 || CompareMat(b, c, epsilon) != 0)
{
fprintf(stderr, "test_layer_cpu failed\n");
return -1;
}
}
// cpu shape hint
{
ncnn::Mat c;
int ret = test_layer_cpu(typeindex, pd, weights, _opt, a, c, b, func, flag);
if (ret != 0 || CompareMat(b, c, epsilon) != 0)
{
fprintf(stderr, "test_layer_cpu failed with shape hint\n");
return -1;
}
}
#if NCNN_VULKAN
// gpu
if (!(flag & TEST_LAYER_DISABLE_GPU_TESTING))
{
ncnn::Mat d;
int ret = test_layer_gpu(typeindex, pd, weights, _opt, a, d, ncnn::Mat(), func, flag);
if (ret != 233 && (ret != 0 || CompareMat(b, d, epsilon) != 0))
{
fprintf(stderr, "test_layer_gpu failed\n");
return -1;
}
}
// gpu shape hint
if (!(flag & TEST_LAYER_DISABLE_GPU_TESTING))
{
ncnn::Mat d;
int ret = test_layer_gpu(typeindex, pd, weights, _opt, a, d, b, func, flag);
if (ret != 233 && (ret != 0 || CompareMat(b, d, epsilon) != 0))
{
fprintf(stderr, "test_layer_gpu failed with shape hint\n");
return -1;
}
}
#endif // NCNN_VULKAN
return 0;
}
template<typename T>
int test_layer(const char* layer_type, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const std::vector<ncnn::Mat>& a, int top_blob_count = 1, float epsilon = 0.001, void (*func)(T*) = 0, int flag = 0)
{
ncnn::Option opts[7];
opts[0].use_packing_layout = false;
opts[0].use_fp16_packed = false;
opts[0].use_fp16_storage = false;
opts[0].use_fp16_arithmetic = false;
opts[0].use_bf16_storage = false;
opts[0].use_shader_pack8 = false;
opts[0].use_image_storage = false;
opts[1].use_packing_layout = false;
opts[1].use_fp16_packed = true;
opts[1].use_fp16_storage = true;
opts[1].use_fp16_arithmetic = true;
opts[1].use_bf16_storage = true;
opts[1].use_shader_pack8 = false;
opts[1].use_image_storage = false;
opts[2].use_packing_layout = true;
opts[2].use_fp16_packed = true;
opts[2].use_fp16_storage = false;
opts[2].use_fp16_arithmetic = false;
opts[2].use_bf16_storage = false;
opts[2].use_shader_pack8 = true;
opts[2].use_image_storage = false;
opts[3].use_packing_layout = true;
opts[3].use_fp16_packed = true;
opts[3].use_fp16_storage = true;
opts[3].use_fp16_arithmetic = false;
opts[3].use_bf16_storage = true;
opts[3].use_shader_pack8 = true;
opts[3].use_image_storage = true;
opts[4].use_packing_layout = true;
opts[4].use_fp16_packed = true;
opts[4].use_fp16_storage = true;
opts[4].use_fp16_arithmetic = true;
opts[4].use_bf16_storage = true;
opts[4].use_shader_pack8 = true;
opts[4].use_image_storage = true;
opts[5].use_packing_layout = true;
opts[5].use_fp16_packed = false;
opts[5].use_fp16_storage = false;
opts[5].use_fp16_arithmetic = false;
opts[5].use_bf16_storage = false;
opts[5].use_shader_pack8 = false;
opts[5].use_image_storage = false;
opts[5].use_sgemm_convolution = false;
opts[5].use_winograd_convolution = false;
opts[6].use_packing_layout = true;
opts[6].use_fp16_packed = true;
opts[6].use_fp16_storage = true;
opts[6].use_fp16_arithmetic = true;
opts[6].use_bf16_storage = true;
opts[6].use_shader_pack8 = true;
opts[6].use_image_storage = true;
opts[6].use_sgemm_convolution = false;
opts[6].use_winograd_convolution = false;
for (int i = 0; i < 7; i++)
{
opts[i].num_threads = 1;
}
for (int i = 0; i < 7; i++)
{
const ncnn::Option& opt = opts[i];
// fp16 representation
std::vector<ncnn::Mat> a_fp16;
if (opt.use_bf16_storage && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
a_fp16.resize(a.size());
for (size_t j = 0; j < a.size(); j++)
{
ncnn::Mat tmp;
ncnn::cast_float32_to_bfloat16(a[j], tmp, opt);
ncnn::cast_bfloat16_to_float32(tmp, a_fp16[j], opt);
}
}
else if ((opt.use_fp16_packed || opt.use_fp16_storage) && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
a_fp16.resize(a.size());
for (size_t j = 0; j < a.size(); j++)
{
ncnn::Mat tmp;
ncnn::cast_float32_to_float16(a[j], tmp, opt);
ncnn::cast_float16_to_float32(tmp, a_fp16[j], opt);
}
}
else
{
a_fp16 = a;
}
std::vector<ncnn::Mat> weights_fp16;
float epsilon_fp16;
if (opt.use_bf16_storage)
{
weights_fp16.resize(weights.size());
for (size_t j = 0; j < weights.size(); j++)
{
ncnn::Mat tmp;
ncnn::cast_float32_to_bfloat16(weights[j], tmp, opt);
ncnn::cast_bfloat16_to_float32(tmp, weights_fp16[j], opt);
}
epsilon_fp16 = epsilon * 100; // 0.1
}
else if (opt.use_fp16_packed || opt.use_fp16_storage)
{
weights_fp16.resize(weights.size());
for (size_t j = 0; j < weights.size(); j++)
{
ncnn::Mat tmp;
ncnn::cast_float32_to_float16(weights[j], tmp, opt);
ncnn::cast_float16_to_float32(tmp, weights_fp16[j], opt);
}
epsilon_fp16 = epsilon * 100; // 0.1
}
else
{
weights_fp16 = weights;
epsilon_fp16 = epsilon;
}
if (opt.use_fp16_arithmetic)
{
epsilon_fp16 = epsilon * 1000; // 1.0
}
std::vector<ncnn::Mat> top_shapes;
int ret = test_layer<T>(ncnn::layer_to_index(layer_type), pd, weights_fp16, opt, a_fp16, top_blob_count, top_shapes, epsilon_fp16, func, flag);
if (ret != 0)
{
fprintf(stderr, "test_layer %s failed use_packing_layout=%d use_fp16_packed=%d use_fp16_storage=%d use_fp16_arithmetic=%d use_shader_pack8=%d use_bf16_storage=%d use_image_storage=%d use_sgemm_convolution=%d use_winograd_convolution=%d\n", layer_type, opt.use_packing_layout, opt.use_fp16_packed, opt.use_fp16_storage, opt.use_fp16_arithmetic, opt.use_shader_pack8, opt.use_bf16_storage, opt.use_image_storage, opt.use_sgemm_convolution, opt.use_winograd_convolution);
return ret;
}
}
return 0;
}
template<typename T>
int test_layer(const char* layer_type, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Mat& a, float epsilon = 0.001, void (*func)(T*) = 0, int flag = 0)
{
ncnn::Option opts[7];
opts[0].use_packing_layout = false;
opts[0].use_fp16_packed = false;
opts[0].use_fp16_storage = false;
opts[0].use_fp16_arithmetic = false;
opts[0].use_bf16_storage = false;
opts[0].use_shader_pack8 = false;
opts[0].use_image_storage = false;
opts[1].use_packing_layout = false;
opts[1].use_fp16_packed = true;
opts[1].use_fp16_storage = true;
opts[1].use_fp16_arithmetic = true;
opts[1].use_bf16_storage = true;
opts[1].use_shader_pack8 = false;
opts[1].use_image_storage = false;
opts[2].use_packing_layout = true;
opts[2].use_fp16_packed = true;
opts[2].use_fp16_storage = false;
opts[2].use_fp16_arithmetic = false;
opts[2].use_bf16_storage = false;
opts[2].use_shader_pack8 = true;
opts[2].use_image_storage = false;
opts[3].use_packing_layout = true;
opts[3].use_fp16_packed = true;
opts[3].use_fp16_storage = true;
opts[3].use_fp16_arithmetic = false;
opts[3].use_bf16_storage = true;
opts[3].use_shader_pack8 = true;
opts[3].use_image_storage = true;
opts[4].use_packing_layout = true;
opts[4].use_fp16_packed = true;
opts[4].use_fp16_storage = true;
opts[4].use_fp16_arithmetic = true;
opts[4].use_bf16_storage = true;
opts[4].use_shader_pack8 = true;
opts[4].use_image_storage = true;
opts[5].use_packing_layout = true;
opts[5].use_fp16_packed = false;
opts[5].use_fp16_storage = false;
opts[5].use_fp16_arithmetic = false;
opts[5].use_bf16_storage = false;
opts[5].use_shader_pack8 = false;
opts[5].use_image_storage = false;
opts[5].use_sgemm_convolution = false;
opts[5].use_winograd_convolution = false;
opts[6].use_packing_layout = true;
opts[6].use_fp16_packed = true;
opts[6].use_fp16_storage = true;
opts[6].use_fp16_arithmetic = true;
opts[6].use_bf16_storage = true;
opts[6].use_shader_pack8 = true;
opts[6].use_image_storage = true;
opts[6].use_sgemm_convolution = false;
opts[6].use_winograd_convolution = false;
for (int i = 0; i < 7; i++)
{
opts[i].num_threads = 1;
}
for (int i = 0; i < 7; i++)
{
const ncnn::Option& opt = opts[i];
// fp16 representation
ncnn::Mat a_fp16;
if (opt.use_bf16_storage && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
ncnn::Mat tmp;
ncnn::cast_float32_to_bfloat16(a, tmp, opt);
ncnn::cast_bfloat16_to_float32(tmp, a_fp16, opt);
}
else if ((opt.use_fp16_packed || opt.use_fp16_storage) && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
ncnn::Mat tmp;
ncnn::cast_float32_to_float16(a, tmp, opt);
ncnn::cast_float16_to_float32(tmp, a_fp16, opt);
}
else
{
a_fp16 = a;
}
std::vector<ncnn::Mat> weights_fp16;
float epsilon_fp16;
if (opt.use_bf16_storage)
{
weights_fp16.resize(weights.size());
for (size_t j = 0; j < weights.size(); j++)
{
ncnn::Mat tmp;
ncnn::cast_float32_to_bfloat16(weights[j], tmp, opt);
ncnn::cast_bfloat16_to_float32(tmp, weights_fp16[j], opt);
}
epsilon_fp16 = epsilon * 100; // 0.1
}
else if (opt.use_fp16_packed || opt.use_fp16_storage)
{
weights_fp16.resize(weights.size());
for (size_t j = 0; j < weights.size(); j++)
{
ncnn::Mat tmp;
ncnn::cast_float32_to_float16(weights[j], tmp, opt);
ncnn::cast_float16_to_float32(tmp, weights_fp16[j], opt);
}
epsilon_fp16 = epsilon * 100; // 0.1
}
else
{
weights_fp16 = weights;
epsilon_fp16 = epsilon;
}
if (opt.use_fp16_arithmetic)
{
epsilon_fp16 = epsilon * 1000; // 1.0
}
ncnn::Mat top_shape;
int ret = test_layer<T>(ncnn::layer_to_index(layer_type), pd, weights_fp16, opt, a_fp16, top_shape, epsilon_fp16, func, flag);
if (ret != 0)
{
fprintf(stderr, "test_layer %s failed use_packing_layout=%d use_fp16_packed=%d use_fp16_storage=%d use_fp16_arithmetic=%d use_shader_pack8=%d use_bf16_storage=%d use_image_storage=%d use_sgemm_convolution=%d use_winograd_convolution=%d\n", layer_type, opt.use_packing_layout, opt.use_fp16_packed, opt.use_fp16_storage, opt.use_fp16_arithmetic, opt.use_shader_pack8, opt.use_bf16_storage, opt.use_image_storage, opt.use_sgemm_convolution, opt.use_winograd_convolution);
return ret;
}
}
return 0;
}
#endif // TESTUTIL_H
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