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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.
#include "testutil.h"
#include "cpu.h"
#include "layer.h"
#include "mat.h"
#include "prng.h"
#include <limits.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;
void SRAND(int seed)
{
prng_srand(seed, &g_prng_rand_state);
}
uint64_t RAND()
{
return prng_rand(&g_prng_rand_state);
}
float RandomFloat(float a, float b)
{
float random = ((float)RAND()) / (float)uint64_t(-1); //RAND_MAX;
float diff = b - a;
float r = random * diff;
float v = a + r;
// generate denormal as zero
if (v < 0.0001 && v > -0.0001)
v = 0.f;
return v;
}
int RandomInt(int a, int b)
{
float random = ((float)RAND()) / (float)uint64_t(-1); //RAND_MAX;
int diff = b - a;
float r = random * diff;
return a + (int)r;
}
signed char RandomS8()
{
return (signed char)RandomInt(-127, 127);
}
void Randomize(ncnn::Mat& m, float a, float b)
{
for (size_t i = 0; i < m.total(); i++)
{
m[i] = RandomFloat(a, b);
}
}
void RandomizeInt(ncnn::Mat& m, int a, int b)
{
for (size_t i = 0; i < m.total(); i++)
{
((int*)m)[i] = RandomInt(a, b);
}
}
void RandomizeS8(ncnn::Mat& m)
{
for (size_t i = 0; i < m.total(); i++)
{
((signed char*)m)[i] = RandomS8();
}
}
ncnn::Mat RandomMat(int w, float a, float b)
{
ncnn::Mat m(w);
Randomize(m, a, b);
return m;
}
ncnn::Mat RandomMat(int w, int h, float a, float b)
{
ncnn::Mat m(w, h);
Randomize(m, a, b);
return m;
}
ncnn::Mat RandomMat(int w, int h, int c, float a, float b)
{
ncnn::Mat m(w, h, c);
Randomize(m, a, b);
return m;
}
ncnn::Mat RandomMat(int w, int h, int d, int c, float a, float b)
{
ncnn::Mat m(w, h, d, c);
Randomize(m, a, b);
return m;
}
ncnn::Mat RandomIntMat(int w)
{
ncnn::Mat m(w);
RandomizeInt(m);
return m;
}
ncnn::Mat RandomIntMat(int w, int h)
{
ncnn::Mat m(w, h);
RandomizeInt(m);
return m;
}
ncnn::Mat RandomIntMat(int w, int h, int c)
{
ncnn::Mat m(w, h, c);
RandomizeInt(m);
return m;
}
ncnn::Mat RandomIntMat(int w, int h, int d, int c)
{
ncnn::Mat m(w, h, d, c);
RandomizeInt(m);
return m;
}
ncnn::Mat RandomS8Mat(int w)
{
ncnn::Mat m(w, (size_t)1u);
RandomizeS8(m);
return m;
}
ncnn::Mat RandomS8Mat(int w, int h)
{
ncnn::Mat m(w, h, (size_t)1u);
RandomizeS8(m);
return m;
}
ncnn::Mat RandomS8Mat(int w, int h, int c)
{
ncnn::Mat m(w, h, c, (size_t)1u);
RandomizeS8(m);
return m;
}
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;
}
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;
}
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));
}
int Compare(const ncnn::Mat& a, const ncnn::Mat& b, float epsilon)
{
#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;
}
int CompareMat(const ncnn::Mat& a, const ncnn::Mat& b, float epsilon)
{
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);
}
int CompareMat(const std::vector<ncnn::Mat>& a, const std::vector<ncnn::Mat>& b, float epsilon)
{
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;
}
static int convert_to_optimal_layout(const ncnn::Mat& a, ncnn::Mat& a4, const ncnn::Option& opt, const ncnn::Layer* op, int flag)
{
// clang-format off
// *INDENT-OFF*
#if NCNN_ARM82
if (opt.use_fp16_storage && ncnn::cpu_support_arm_asimdhp() && op->support_fp16_storage && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
ncnn::cast_float32_to_float16(a, a4, opt);
}
else
#endif // NCNN_ARM82
#if NCNN_VFPV4
if (opt.use_fp16_storage && !opt.use_bf16_storage && ncnn::cpu_support_arm_vfpv4() && op->support_fp16_storage && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
ncnn::cast_float32_to_float16(a, a4, opt);
}
else
#endif // NCNN_VFPV4
#if NCNN_RVV
if (opt.use_fp16_storage && ncnn::cpu_support_riscv_v() && ncnn::cpu_support_riscv_zfh() && op->support_fp16_storage && !(flag & TEST_LAYER_DISABLE_AUTO_INPUT_CASTING))
{
ncnn::cast_float32_to_float16(a, a4, opt);
}
else
#endif // NCNN_RVV
#if NCNN_BF16
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
#endif // NCNN_BF16
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
{
a4 = a;
}
// *INDENT-ON*
// clang-format on
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 && ncnn::cpu_support_arm_asimdhp() && 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;
}
return 0;
}
static int convert_to_vanilla_layout(const ncnn::Mat& c4, ncnn::Mat& c, const ncnn::Option& opt, const ncnn::Layer* op, int flag)
{
ncnn::Mat c4_unpacked;
if (c4.elempack != 1)
{
ncnn::convert_packing(c4, c4_unpacked, 1, opt);
}
else
{
c4_unpacked = c4;
}
// clang-format off
// *INDENT-OFF*
#if NCNN_ARM82
if (opt.use_fp16_storage && ncnn::cpu_support_arm_asimdhp() && op->support_fp16_storage && c4_unpacked.elembits() == 16)
{
ncnn::cast_float16_to_float32(c4_unpacked, c, opt);
}
else
#endif // NCNN_ARM82
#if NCNN_VFPV4
if (opt.use_fp16_storage && !opt.use_bf16_storage && ncnn::cpu_support_arm_vfpv4() && op->support_fp16_storage && c4_unpacked.elembits() == 16)
{
ncnn::cast_float16_to_float32(c4_unpacked, c, opt);
}
else
#endif // NCNN_VFPV4
#if NCNN_RVV
if (opt.use_fp16_storage && ncnn::cpu_support_riscv_v() && ncnn::cpu_support_riscv_zfh() && op->support_fp16_storage && c4_unpacked.elembits() == 16)
{
ncnn::cast_float16_to_float32(c4_unpacked, c, opt);
}
else
#endif // NCNN_RVV
#if NCNN_BF16
if (opt.use_bf16_storage && op->support_bf16_storage && c4_unpacked.elembits() == 16)
{
ncnn::cast_bfloat16_to_float32(c4_unpacked, c, opt);
}
else
#endif // NCNN_BF16
if (opt.use_fp16_storage && op->support_fp16_storage && c4_unpacked.elembits() == 16)
{
ncnn::cast_float16_to_float32(c4_unpacked, c, opt);
}
else
{
c = c4_unpacked;
}
// *INDENT-ON*
// clang-format on
return 0;
}
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)(ncnn::Layer*), int flag)
{
ncnn::Layer* op = ncnn::create_layer_naive(typeindex);
if (func)
{
(*func)((ncnn::Layer*)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.lightmode = false;
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();
}
op->forward_inplace(b, opt);
}
else
{
op->forward(a, b, opt);
}
op->destroy_pipeline(opt);
delete op;
return 0;
}
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)(ncnn::Layer*), int flag)
{
ncnn::Layer* op = ncnn::create_layer_cpu(typeindex);
if (!op->support_packing && _opt.use_packing_layout)
{
delete op;
return 233;
}
if (!op->support_bf16_storage && !op->support_fp16_storage && (_opt.use_bf16_storage || _opt.use_fp16_arithmetic))
{
delete op;
return 233;
}
if (func)
{
(*func)((ncnn::Layer*)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);
if (!op->support_packing && _opt.use_packing_layout)
{
op->destroy_pipeline(opt);
delete op;
return 233;
}
if (!op->support_bf16_storage && !op->support_fp16_storage && (_opt.use_bf16_storage || _opt.use_fp16_arithmetic))
{
op->destroy_pipeline(opt);
delete op;
return 233;
}
std::vector<ncnn::Mat> a4(a.size());
for (size_t i = 0; i < a4.size(); i++)
{
convert_to_optimal_layout(a[i], a4[i], opt, op, flag);
}
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++)
{
convert_to_vanilla_layout(c[i], c[i], opt, op, flag);
}
op->destroy_pipeline(opt);
delete op;
return 0;
}
#if NCNN_VULKAN
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)(ncnn::Layer*), int flag)
{
if (!_opt.use_packing_layout)
{
// pack1 test is useless for gpu
return 233;
}
ncnn::Layer* op = ncnn::create_layer_vulkan(typeindex);
if (!op)
{
return 233;
}
op->load_param(pd);
if (!op->support_vulkan)
{
delete op;
return 233;
}
ncnn::VulkanDevice* vkdev = ncnn::get_gpu_device();
op->vkdev = vkdev;
if (func)
{
(*func)((ncnn::Layer*)op);
}
if (!top_shapes.empty())
{
op->bottom_shapes = a;
op->top_shapes = top_shapes;
}
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_uniform()) opt.use_fp16_uniform = false;
if (!vkdev->info.support_fp16_arithmetic()) opt.use_fp16_arithmetic = false;
if (!vkdev->info.support_int8_packed()) opt.use_int8_packed = false;
if (!vkdev->info.support_int8_storage()) opt.use_int8_storage = false;
if (!vkdev->info.support_int8_uniform()) opt.use_int8_uniform = false;
if (!vkdev->info.support_int8_arithmetic()) opt.use_int8_arithmetic = false;
if (!vkdev->info.support_cooperative_matrix()) opt.use_cooperative_matrix = false;
// FIXME fp16a may produce large error
opt.use_fp16_arithmetic = false;
op->create_pipeline(opt);
if (!op->support_vulkan)
{
op->destroy_pipeline(opt);
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
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, float epsilon, void (*func)(ncnn::Layer*), int flag)
{
// naive
std::vector<ncnn::Mat> b;
{
int ret = test_layer_naive(typeindex, pd, weights, a, top_blob_count, b, func, flag);
if (ret != 233 && 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 != 233 && (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 != 233 && (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;
}
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)(ncnn::Layer*), int flag)
{
ncnn::Layer* op = ncnn::create_layer_naive(typeindex);
if (func)
{
(*func)((ncnn::Layer*)op);
}
op->load_param(pd);
ncnn::ModelBinFromMatArray mb(weights.data());
op->load_model(mb);
ncnn::Option opt;
opt.num_threads = 1;
opt.lightmode = false;
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();
op->forward_inplace(b, opt);
}
else
{
op->forward(a, b, opt);
}
op->destroy_pipeline(opt);
delete op;
return 0;
}
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)(ncnn::Layer*), int flag)
{
ncnn::Layer* op = ncnn::create_layer_cpu(typeindex);
if (!op->support_packing && _opt.use_packing_layout)
{
delete op;
return 233;
}
if (!op->support_bf16_storage && !op->support_fp16_storage && (_opt.use_bf16_storage || _opt.use_fp16_arithmetic))
{
delete op;
return 233;
}
if (func)
{
(*func)((ncnn::Layer*)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);
if (!op->support_packing && _opt.use_packing_layout)
{
op->destroy_pipeline(opt);
delete op;
return 233;
}
if (!op->support_bf16_storage && !op->support_fp16_storage && (_opt.use_bf16_storage || _opt.use_fp16_arithmetic))
{
op->destroy_pipeline(opt);
delete op;
return 233;
}
ncnn::Mat a4;
convert_to_optimal_layout(a, a4, opt, op, flag);
if (op->support_inplace)
{
c = a4.clone();
op->forward_inplace(c, opt);
}
else
{
op->forward(a4, c, opt);
}
convert_to_vanilla_layout(c, c, opt, op, flag);
op->destroy_pipeline(opt);
delete op;
return 0;
}
#if NCNN_VULKAN
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)(ncnn::Layer*), int flag)
{
if (!_opt.use_packing_layout)
{
// pack1 test is useless for gpu
return 233;
}
ncnn::Layer* op = ncnn::create_layer_vulkan(typeindex);
if (!op)
{
return 233;
}
op->load_param(pd);
if (!op->support_vulkan)
{
delete op;
return 233;
}
ncnn::VulkanDevice* vkdev = ncnn::get_gpu_device();
op->vkdev = vkdev;
if (func)
{
(*func)((ncnn::Layer*)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;
}
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_uniform()) opt.use_fp16_uniform = false;
if (!vkdev->info.support_fp16_arithmetic()) opt.use_fp16_arithmetic = false;
if (!vkdev->info.support_int8_packed()) opt.use_int8_packed = false;
if (!vkdev->info.support_int8_storage()) opt.use_int8_storage = false;
if (!vkdev->info.support_int8_uniform()) opt.use_int8_uniform = false;
if (!vkdev->info.support_int8_arithmetic()) opt.use_int8_arithmetic = false;
if (!vkdev->info.support_cooperative_matrix()) opt.use_cooperative_matrix = false;
// FIXME fp16a may produce large error
opt.use_fp16_arithmetic = false;
op->create_pipeline(opt);
if (!op->support_vulkan)
{
op->destroy_pipeline(opt);
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
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, float epsilon, void (*func)(ncnn::Layer*), int flag)
{
// naive
ncnn::Mat b;
{
int ret = test_layer_naive(typeindex, pd, weights, a, b, func, flag);
if (ret != 233 && 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 != 233 && (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 != 233 && (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;
}
int test_layer_opt(const char* layer_type, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& opt, const std::vector<ncnn::Mat>& a, int top_blob_count, float epsilon, void (*func)(ncnn::Layer*), int flag)
{
// 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++)
{
if (weights[j].elembits() != 32)
{
weights_fp16[j] = weights[j];
continue;
}
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++)
{
if (weights[j].elembits() != 32)
{
weights_fp16[j] = weights[j];
continue;
}
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(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;
}
int test_layer_opt(const char* layer_type, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& opt, const ncnn::Mat& a, float epsilon, void (*func)(ncnn::Layer*), int flag)
{
// 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++)
{
if (weights[j].elembits() != 32)
{
weights_fp16[j] = weights[j];
continue;
}
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++)
{
if (weights[j].elembits() != 32)
{
weights_fp16[j] = weights[j];
continue;
}
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(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;
}
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, float epsilon, void (*func)(ncnn::Layer*), int flag)
{
// pack fp16p fp16s fp16a bf16s shader8 image
const int options[][7] = {
{0, 0, 0, 0, 0, 0, 0},
{0, 0, 1, 0, 0, 0, 0},
{0, 0, 1, 1, 1, 0, 0},
{1, 0, 0, 0, 0, 0, 0},
{1, 1, 0, 0, 1, 0, 0},
{1, 0, 1, 0, 0, 1, 0},
{1, 1, 1, 1, 0, 0, 0},
{1, 1, 1, 1, 1, 1, 1},
};
const int opt_count = sizeof(options) / sizeof(options[0]);
for (int i = 0; i < opt_count; i++)
{
ncnn::Option opt;
opt.num_threads = 1;
opt.use_packing_layout = options[i][0];
opt.use_fp16_packed = options[i][1];
opt.use_fp16_storage = options[i][2];
opt.use_fp16_arithmetic = options[i][3];
opt.use_bf16_storage = options[i][4];
opt.use_shader_pack8 = options[i][5];
opt.use_image_storage = options[i][6];
int ret = test_layer_opt(layer_type, pd, weights, opt, a, top_blob_count, epsilon, func, flag);
if (ret != 0)
return ret;
}
return 0;
}
int test_layer(const char* layer_type, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Mat& a, float epsilon, void (*func)(ncnn::Layer*), int flag)
{
// pack fp16p fp16s fp16a bf16s shader8 image
const int options[][7] = {
{0, 0, 0, 0, 0, 0, 0},
{0, 0, 1, 0, 0, 0, 0},
{0, 0, 1, 1, 1, 0, 0},
{1, 0, 0, 0, 0, 0, 0},
{1, 1, 0, 0, 1, 0, 0},
{1, 0, 1, 0, 0, 1, 0},
{1, 1, 1, 1, 0, 0, 0},
{1, 1, 1, 1, 1, 1, 1},
};
const int opt_count = sizeof(options) / sizeof(options[0]);
for (int i = 0; i < opt_count; i++)
{
ncnn::Option opt;
opt.num_threads = 1;
opt.use_packing_layout = options[i][0];
opt.use_fp16_packed = options[i][1];
opt.use_fp16_storage = options[i][2];
opt.use_fp16_arithmetic = options[i][3];
opt.use_bf16_storage = options[i][4];
opt.use_shader_pack8 = options[i][5];
opt.use_image_storage = options[i][6];
int ret = test_layer_opt(layer_type, pd, weights, opt, a, epsilon, func, flag);
if (ret != 0)
return ret;
}
return 0;
}
class TestOOMAllocator : public ncnn::UnlockedPoolAllocator
{
public:
TestOOMAllocator();
virtual void* fastMalloc(size_t size);
virtual void fastFree(void* ptr);
ncnn::Mutex lock;
int counter;
int failid;
};
TestOOMAllocator::TestOOMAllocator()
{
counter = 0;
failid = INT_MAX;
}
void* TestOOMAllocator::fastMalloc(size_t size)
{
lock.lock();
void* ptr;
if (counter == failid)
{
ptr = 0;
}
else
{
ptr = ncnn::UnlockedPoolAllocator::fastMalloc(size);
}
counter++;
lock.unlock();
return ptr;
}
void TestOOMAllocator::fastFree(void* ptr)
{
lock.lock();
ncnn::UnlockedPoolAllocator::fastFree(ptr);
lock.unlock();
}
int test_layer_oom_opt(const char* layer_type, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& _opt, const std::vector<ncnn::Mat>& a, int top_blob_count, int flag)
{
int typeindex = ncnn::layer_to_index(layer_type);
if (typeindex == -1)
return -1;
ncnn::Layer* op = ncnn::create_layer_cpu(typeindex);
if (!op->support_packing && _opt.use_packing_layout)
{
delete op;
return 233;
}
if (!op->support_bf16_storage && !op->support_fp16_storage && (_opt.use_bf16_storage || _opt.use_fp16_arithmetic))
{
delete op;
return 233;
}
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);
if (!op->support_packing && _opt.use_packing_layout)
{
op->destroy_pipeline(opt);
delete op;
return 233;
}
if (!op->support_bf16_storage && !op->support_fp16_storage && (_opt.use_bf16_storage || _opt.use_fp16_arithmetic))
{
op->destroy_pipeline(opt);
delete op;
return 233;
}
std::vector<ncnn::Mat> a4(a.size());
for (size_t i = 0; i < a4.size(); i++)
{
convert_to_optimal_layout(a[i], a4[i], opt, op, flag);
}
TestOOMAllocator test_oom_allocator;
opt.blob_allocator = &test_oom_allocator;
opt.workspace_allocator = &test_oom_allocator;
std::vector<ncnn::Mat> c;
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 (int i = 0; i < top_blob_count; i++)
{
c[i].release();
}
const int alloc_count = test_oom_allocator.counter;
for (int i = 0; i < alloc_count; i++)
{
test_oom_allocator.counter = 0;
test_oom_allocator.failid = i;
int ret = 0;
if (op->support_inplace)
{
for (size_t i = 0; i < a4.size(); i++)
{
c[i] = a4[i].clone();
}
ret = op->forward_inplace(c, opt);
}
else
{
ret = op->forward(a4, c, opt);
}
for (int i = 0; i < top_blob_count; i++)
{
c[i].release();
}
if (ret != -100)
{
fprintf(stderr, "oom not catched %d/%d\n", i, alloc_count);
op->destroy_pipeline(opt);
delete op;
return -1;
}
}
op->destroy_pipeline(opt);
delete op;
return 0;
}
int test_layer_oom_opt(const char* layer_type, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Option& _opt, const ncnn::Mat& a, int flag)
{
int typeindex = ncnn::layer_to_index(layer_type);
if (typeindex == -1)
return -1;
ncnn::Layer* op = ncnn::create_layer_cpu(typeindex);
if (!op->support_packing && _opt.use_packing_layout)
{
delete op;
return 233;
}
if (!op->support_bf16_storage && !op->support_fp16_storage && (_opt.use_bf16_storage || _opt.use_fp16_arithmetic))
{
delete op;
return 233;
}
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);
if (!op->support_packing && _opt.use_packing_layout)
{
op->destroy_pipeline(opt);
delete op;
return 233;
}
if (!op->support_bf16_storage && !op->support_fp16_storage && (_opt.use_bf16_storage || _opt.use_fp16_arithmetic))
{
op->destroy_pipeline(opt);
delete op;
return 233;
}
ncnn::Mat a4;
convert_to_optimal_layout(a, a4, opt, op, flag);
TestOOMAllocator test_oom_allocator;
opt.blob_allocator = &test_oom_allocator;
opt.workspace_allocator = &test_oom_allocator;
ncnn::Mat c;
if (op->support_inplace)
{
c = a4.clone();
op->forward_inplace(c, opt);
}
else
{
op->forward(a4, c, opt);
}
c.release();
const int alloc_count = test_oom_allocator.counter;
for (int i = 0; i < alloc_count; i++)
{
test_oom_allocator.counter = 0;
test_oom_allocator.failid = i;
int ret = 0;
if (op->support_inplace)
{
c = a4.clone();
ret = op->forward_inplace(c, opt);
}
else
{
ret = op->forward(a4, c, opt);
}
c.release();
if (ret != -100)
{
fprintf(stderr, "oom not catched %d/%d\n", i, alloc_count);
op->destroy_pipeline(opt);
delete op;
return -1;
}
}
op->destroy_pipeline(opt);
delete op;
return 0;
}
int test_layer_oom(const char* layer_type, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const std::vector<ncnn::Mat>& a, int top_blob_count, int flag)
{
// pack fp16p fp16s fp16a bf16s shader8 image
const int options[][7] = {
{0, 0, 0, 0, 0, 0, 0},
{0, 0, 1, 0, 0, 0, 0},
{0, 0, 1, 1, 1, 0, 0},
{1, 0, 0, 0, 0, 0, 0},
{1, 1, 0, 0, 1, 0, 0},
{1, 0, 1, 0, 0, 1, 0},
{1, 1, 1, 1, 0, 0, 0},
{1, 1, 1, 1, 1, 1, 1},
};
const int opt_count = sizeof(options) / sizeof(options[0]);
for (int i = 0; i < opt_count; i++)
{
ncnn::Option opt;
opt.num_threads = 1;
opt.use_packing_layout = options[i][0];
opt.use_fp16_packed = options[i][1];
opt.use_fp16_storage = options[i][2];
opt.use_fp16_arithmetic = options[i][3];
opt.use_bf16_storage = options[i][4];
opt.use_shader_pack8 = options[i][5];
opt.use_image_storage = options[i][6];
int ret = test_layer_oom_opt(layer_type, pd, weights, opt, a, top_blob_count, flag);
if (ret != 233 && ret != 0)
return ret;
}
return 0;
}
int test_layer_oom(const char* layer_type, const ncnn::ParamDict& pd, const std::vector<ncnn::Mat>& weights, const ncnn::Mat& a, int flag)
{
// pack fp16p fp16s fp16a bf16s shader8 image
const int options[][7] = {
{0, 0, 0, 0, 0, 0, 0},
{0, 0, 1, 0, 0, 0, 0},
{0, 0, 1, 1, 1, 0, 0},
{1, 0, 0, 0, 0, 0, 0},
{1, 1, 0, 0, 1, 0, 0},
{1, 0, 1, 0, 0, 1, 0},
{1, 1, 1, 1, 0, 0, 0},
{1, 1, 1, 1, 1, 1, 1},
};
const int opt_count = sizeof(options) / sizeof(options[0]);
for (int i = 0; i < opt_count; i++)
{
ncnn::Option opt;
opt.num_threads = 1;
opt.use_packing_layout = options[i][0];
opt.use_fp16_packed = options[i][1];
opt.use_fp16_storage = options[i][2];
opt.use_fp16_arithmetic = options[i][3];
opt.use_bf16_storage = options[i][4];
opt.use_shader_pack8 = options[i][5];
opt.use_image_storage = options[i][6];
int ret = test_layer_oom_opt(layer_type, pd, weights, opt, a, flag);
if (ret != 233 && ret != 0)
return ret;
}
return 0;
}
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