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Memory Pool Improvement For Variadic Sized Inputs #4190

Merged
merged 34 commits into from
Oct 9, 2022
Merged

Memory Pool Improvement For Variadic Sized Inputs #4190

merged 34 commits into from
Oct 9, 2022

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LinHeLurking
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A Better Memory Pool With Simple Greedy Strategy

This pull request contains several modifications on current ncnn pool allocators.

Basic Assumption

The program runs in multiple stages. In each stage, memory sizes the program is asking for are lied in an (relatively small) interval. When program goes into a new stage, the size interval will change. The intervals may or may not overlap.

Double Ended Greedy Strategy

If all cached chunks in the pool cannot satisfy current allocation, the allocator tries to remove an outdated chunk.

  • If this allocation size is larger than any previous chunk, remove the smallest chunk (see below).
                NEW
           ^============^
        ___^_____       ^
       |   ^     |      ^
----------------------------------------
         OLD        ^    
                    |
                    |
                ALLOCATION
  • If this allocation size is smaller than any previous chunk, remove the largest chunk.
  • If this allocation size is between the largest and smallest, do not remove.

Simulated Results

Following codes can be used to simulate a multiple stage running scenario.

int thrd_share_allocator_test(int repeat, ncnn::Allocator* allocator)
{
    // Fix seed for better reproducibility.
    std::mt19937 rng(0xbadf00d);
    std::uniform_int_distribution<size_t> dist;

    std::deque<ncnn::Mat> occupied;
    int batch_size = 30;
    int batch_num = repeat;
    for (int batch_id = 0; batch_id < batch_num; ++batch_id)
    {
        // Centroid and radius absolute size does not matter.
        // You can scale them arbitrarily.
        size_t centroid = std::max(1UL, dist(rng)) % 100000000;              // max centroid is 100 MB
        size_t radius = std::min(centroid, std::min(dist(rng), 30000000UL)); // max radius is 30 MB

        std::uniform_int_distribution<size_t> size_dist(centroid - radius, centroid + radius);
        std::vector<void*> allocated;
        for (int i = 0; i < batch_size; ++i)
        {
            size_t size = size_dist(rng) & ~0x13FFF; // minimal step is 5 KB
            void* ptr = allocator->fastMalloc(size);
            allocated.push_back(ptr);
        }
        std::this_thread::sleep_for(std::chrono::milliseconds(500));
        for (void* ptr : allocated)
        {
            allocator->fastFree(ptr);
        }
    }
    return 0;
}

int test_allocator(int thrd_num = 4, int repeat = int(1e1))
{
    auto allocator = new ncnn::PoolAllocator;
    std::vector<std::future<int> > futures;
    for (int i = 0; i < thrd_num; ++i)
    {
        futures.emplace_back(std::async(thrd_share_allocator_test, repeat, allocator));
    }
    int flag = 0;
    for (auto& future : futures)
    {
        flag |= future.get();
    }
    return flag;
}

int main()
{
    return 0 || test_allocator();
}

Memory usage:
(Memory usage is gathered with valgrind: valgrind --tool=massif --time-unit=ms <executable>)

image

@LinHeLurking
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I'm not sure why old commits occur here. Actually only cee9d22 is the thing I did the magic.
😆

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codecov-commenter commented Sep 7, 2022

Codecov Report

Merging #4190 (4066294) into master (479a73a) will decrease coverage by 0.18%.
The diff coverage is 48.57%.

@@            Coverage Diff             @@
##           master    #4190      +/-   ##
==========================================
- Coverage   94.43%   94.25%   -0.19%     
==========================================
  Files         749      750       +1     
  Lines      179049   179405     +356     
==========================================
+ Hits       169091   169094       +3     
- Misses       9958    10311     +353     
Impacted Files Coverage Δ
src/allocator.h 87.50% <ø> (ø)
src/allocator.cpp 75.11% <47.05%> (-1.94%) ⬇️
src/net.cpp 65.37% <100.00%> (ø)
src/command.cpp 72.70% <0.00%> (-14.94%) ⬇️
src/pipeline.cpp 58.69% <0.00%> (-2.18%) ⬇️
src/layer/vulkan/reshape_vulkan.cpp 92.01% <0.00%> (-2.14%) ⬇️
src/layer/vulkan/packing_vulkan.cpp 81.70% <0.00%> (-1.88%) ⬇️
src/layer/vulkan/permute_vulkan.cpp 96.99% <0.00%> (-1.60%) ⬇️
src/layer/vulkan/reorg_vulkan.cpp 96.35% <0.00%> (-1.57%) ⬇️
src/layer/vulkan/pixelshuffle_vulkan.cpp 96.35% <0.00%> (-1.57%) ⬇️
... and 24 more

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@nihui
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nihui commented Sep 13, 2022

please investigate sanitizer error

@LinHeLurking
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I've tried but cannot reproduce the error of test_squeezenet. Is there any way to run CTest with extra checks on?

@nihui
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nihui commented Sep 23, 2022

cmake -DCMAKE_BUILD_TYPE=debug -DNCNN_ASAN=ON -DNCNN_BUILD_TESTS=ON -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF ..

@LRY89757
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LRY89757 commented Sep 24, 2022

I've tried but cannot reproduce the error of test_squeezenet. Is there any way to run CTest with extra checks on?

May be you can refer to this line about this file linux-x64-cpu-gcc-san.yml :)

@LinHeLurking
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(First of all, sorry for late response.)

It turns out that ASAN switch of CMAKE is ignored if it is set in VS Code setting.json file. (But why ???)
After all, I've fixed the sanitizer error. 😆

@@ -33,6 +33,7 @@ class PoolAllocatorPrivate
Mutex budgets_lock;
Mutex payouts_lock;
unsigned int size_compare_ratio; // 0~256
static const size_t size_threshold = 10;
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size_threshold does not seem expressive enough
budget_count_threshold or budget_drop_threshold might be better?

How about adding a setter api that allows the user to control whether a more aggressive or conservative budget recycling strategy is required?

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Renamed & Added a setter.

@@ -104,11 +105,20 @@ void* PoolAllocator::fastMalloc(size_t size)
d->budgets_lock.lock();

// find free budget
std::list<std::pair<size_t, void*> >::iterator it = d->budgets.begin();
std::list<std::pair<size_t, void*> >::iterator it = d->budgets.begin(), it_max = d->budgets.end(), it_min = d->budgets.end();
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Why are these max/min iterators initialized to end() instead of begin() ?
If the budgets are empty, if (d->budgets.size() >= d->size_threshold) will always be false.

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Yes it doesn't matter.

@nihui nihui merged commit 9426e21 into Tencent:master Oct 9, 2022
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nihui commented Oct 9, 2022

Thanks for your contribution !

csukuangfj added a commit to csukuangfj/ncnn that referenced this pull request Dec 1, 2022
* remove duplicated newline (Tencent#4187)

* remove duplicated newline (Tencent#4188)

* optmize softmax arm neon (Tencent#4171)

* [docs] Fix typo (Tencent#4201)

* [Prelu x86] Finish intrinsic with elempack merged (Tencent#4177)

* changed size of images for pretty formatting of page (Tencent#4193)

* [Gelu x86] Finish intrinsic with elempack merged(fast version) (Tencent#4144)

* Finish the gelu x86 intrinsics
* Finish the fast tanh x86 simd impl

* Ignore .xmake directory (Tencent#4212)

* Bump pypa/cibuildwheel from 2.9.0 to 2.10.1 (Tencent#4207)

Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.9.0 to 2.10.1.
- [Release notes](https://github.com/pypa/cibuildwheel/releases)
- [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md)
- [Commits](pypa/cibuildwheel@v2.9.0...v2.10.1)

---
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- dependency-name: pypa/cibuildwheel
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

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* style: space alignment (Tencent#4217)

* Ignore CMakeSettings.json, the Visual Studio CMake schema file (Tencent#4228)

* RVV: use new interface for segment load/store & change word_type to size_t&add clang ci (part Tencent#4100) (Tencent#4118)

* RVV: use size_t for vl

* RVV: replace vsseg.v tuple type by using regex

-----

search:
vsseg([1-9])e(8|16|32)_v_(f|i|u)\2m(1|2|4|8)x\1\(([ -~]+), vcreate_\3\2m\4x\1\(([ -~]+)\), vl\);

substitute by:
vsseg$1e$2_v_$3$2m$4($5, $6, vl);

* RVV: replace vssseg.v tuple types by using regex

---

search:
vssseg([1-9])e(8|16|32)_v_f\2m1x\1\(([ -~]+), vcreate_f\2m1x\1\(([ -~]+)\), vl\);

substitute by:
vssseg$1e$2_v_f$2m1($3, $4, vl);

* RVV: replace vlseg.v tuple types in load/store

* RVV: replace vloxseg2ei32.v tuple types

* RVV: add a wrapper for old compilers

* RVV: add segment load/store wrapper in pakcing

* RVV: fix cmake test

* RVV: make clang happy by dropping VLAs in sgemm

* RVV: add clang cmake toolchain configure

* RVV: add clang ci, riscv64-unknown-linux-gnu

Co-authored-by: thelastlin <thelastlin@users.noreply.github.com>
Co-authored-by: nihui <shuizhuyuanluo@126.com>

* Bump pypa/cibuildwheel from 2.10.1 to 2.10.2 (Tencent#4220)

Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.10.1 to 2.10.2.
- [Release notes](https://github.com/pypa/cibuildwheel/releases)
- [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md)
- [Commits](pypa/cibuildwheel@v2.10.1...v2.10.2)

---
updated-dependencies:
- dependency-name: pypa/cibuildwheel
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

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* add c906 build ci (Tencent#4232)

* Add benchmark result of T-Head TH1520 (Tencent#4240)

`cpuinfo`: 

```
isa             : rv64imafdcvsu
mmu             : sv39
cpu-freq                : 1.848Ghz
cpu-icache              : 64KB
cpu-dcache              : 64KB
cpu-l2cache             : 1MB
cpu-tlb         : 1024 4-ways
cpu-cacheline           : 64Bytes
cpu-vector              : 0.7.1
```

Compiled with `-DCMAKE_TOOLCHAIN_FILE=../toolchains/c910-v240.toolchain.cmake -DCMAKE_BUILD_TYPE=release -DNCNN_OPENMP=OFF -DNCNN_THREADS=OFF -DNCNN_RUNTIME_CPU=OFF -DNCNN_RVV=ON -DNCNN_SIMPLEOCV=ON -DNCNN_BUILD_EXAMPLES=ON` 

Seems much worse than expected 🤔

* fix param parsing issue when layer/blob name exceeds 255 (Tencent#4236)

* fix param parsing issue when layer/blob name exceeds 255

* apply code-format changes

Co-authored-by: ZhangGe6 <ZhangGe6@users.noreply.github.com>

* Memory Pool Improvement For Variadic Sized Inputs (Tencent#4190)

* Simple miss count for better space efficiency

* Simple double ended greedy;

* Add size drop threshold setter;

* set workspace allocator cr to zero as we had some sort of recylcing capability :P

Co-authored-by: LinHeLurking <LinHeLurking@users.noreply.github.com>
Co-authored-by: nihuini <nihuini@tencent.com>

* docs: disable fp16 when wrong results encountered caused by overflow (Tencent#4248)

* pnnx math operation (Tencent#4251)

* more stricter armv7 fp16 and armv84 bf16 compiler check, fix Tencent#4147 fix Tencent#4222 (Tencent#4247)

* modified the param axes of expanddims in modelwriter (Tencent#4259)

* Add TH1520 (4*C910V) toolchain support.  (Tencent#4267)

* implement lstm proj_size (Tencent#4263)

* Optimize x86 DeformableConv2D (Tencent#4128)

* fix compile warning with gcc 9.1.0 including simplestl.h file (Tencent#4274)

* fix compile warning with gcc 9.1.0 including simplestl.h file

* apply code-format changes

Co-authored-by: veahow <veahow@users.noreply.github.com>

* add benchmark for rk3588 on rock5b (Tencent#4275)

* linux-x64-cpu-gcc on tencent ci

* implement layer feature disabled bit (Tencent#4278)

* add elu vulkan operator (Tencent#4280)

* fix tencent ci (Tencent#4277)

* implement GLU and pnnx conversion (Tencent#4283)

* Bump pypa/cibuildwheel from 2.10.2 to 2.11.1 (Tencent#4271)

Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.10.2 to 2.11.1.
- [Release notes](https://github.com/pypa/cibuildwheel/releases)
- [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md)
- [Commits](pypa/cibuildwheel@v2.10.2...v2.11.1)

---
updated-dependencies:
- dependency-name: pypa/cibuildwheel
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

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* fix pnnx softmax/normalize/slice negative axis conversion to ncnn (Tencent#4284)

* pnnx glu batchindex aware conversion (Tencent#4285)

* 1. Fix typo in readme (Tencent#4287)

* x86 sse2/avx2 optimization for convolution sgemm/winograd int8 family (Tencent#4286)

* pnnx skip dynamic size evaluation (Tencent#4291)

* Fix linux build error(Tencent#4265) (Tencent#4294)

Co-authored-by: wangyu <786794414@qq.com>

* general cpu feature detection on macos/ios, enable bf16 and i8mm on a15 a16 and m2 (Tencent#4300)

* x86 unified fc fp32/fp16s (Tencent#4303)

* more fma
* more transpose utility function

* Bump pypa/cibuildwheel from 2.11.1 to 2.11.2 (Tencent#4308)

Bumps [pypa/cibuildwheel](https://github.com/pypa/cibuildwheel) from 2.11.1 to 2.11.2.
- [Release notes](https://github.com/pypa/cibuildwheel/releases)
- [Changelog](https://github.com/pypa/cibuildwheel/blob/main/docs/changelog.md)
- [Commits](pypa/cibuildwheel@v2.11.1...v2.11.2)

---
updated-dependencies:
- dependency-name: pypa/cibuildwheel
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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* pnnx pytorch 1.13 (Tencent#4314)

* fix Tencent#4315 (Tencent#4316)

* get_physical_cpu_count api family (Tencent#4302)

* get_physical_cpu_count api family

* set default to physical big cpu

* always treat smt core as big core

* is_smt_cpu

* get max freq mhz on windows

* windows thread affinity

* groupnorm 1d/2d/4d (Tencent#4312)

* fix slice end index, fix fp16 model weight alignment (Tencent#4317)

* tencent ci test-coverage pnnx (Tencent#4305)

* RVV: BatchNorm with fp16s(a) support (Tencent#4075)

* RVV: InstanceNorm with fp16s(a) support (Tencent#4078)

* fix ci pnnx build

* fold new_full and full_like (Tencent#4323)

* pnnx convert nn.Softmax2d (Tencent#4324)

* pnnx convert fold unfold (Tencent#4325)

* support yolov5 6.2 (Tencent#4328)

* implement ncnn fold and unfold (Tencent#4326)

* pnnx load gpu torchscript and reset device (Tencent#4330)

* fix:pnnx-softmax (Tencent#4333)

* pnnx save onnx zero (Tencent#4077)

* save foldable constants in file for reducing memory usage (Tencent#4337)

* match inplace slice copy pattern, rewrite copy uses (Tencent#4338)

* add vector optimization for loongarch64 (Tencent#4242)

* ci loongarch64 lsx (Tencent#4344)

* gridsample op support (Tencent#4288)



Co-authored-by: LRY89757 <LRY89757@users.noreply.github.com>
Co-authored-by: nihuini <nihuini@tencent.com>
Co-authored-by: nihui <shuizhuyuanluo@126.com>

* squeeze and expanddims 4d (Tencent#4346)

* implement MultiheadAttention kdim vdim (Tencent#4347)

* pnnx convert torch bitwise left_shift right_shift (Tencent#4349)

* pnnx fp16 option for ncnn and onnx weight type (Tencent#4350)

* pnnx fuse more function to module (Tencent#4351)

* pnnx fuse more function to module

* rename some pass name

* fuse adjacent reshape, fuse pad conv2d

* fuse pad conv1d

* split tests (Tencent#4354)

* Support mat.numpy() in Python (Tencent#4356)

* Fix typo in stb_image.h (Tencent#4358)

exitting -> exiting

* Fix windows-arm64 build for non-neon case (Tencent#4227)

* update release ci (Tencent#4359)

* update release ci

* find modern glslang

* parallel jobs on windows

* Fix c api allocator (Tencent#4360)

* add some c_api interfaces related to allocator setup.

* fix errors in allocator parameters in c_api.

* test c api allocator

Co-authored-by: zhangtongshe <yuyuyezi@vip.qq.com>

* update glslang (Tencent#4361)

* disable out-of-line atomics since ndk23+ for resolving linking issue with old ndk (Tencent#4362)

* I added one more project to the list of examples. (Tencent#4205)

* Dedicated to coloring black and white photographs.

* add example project link (Tencent#4365)

* fix(pybind11): build error (Tencent#4368)

* fix openmp affinity abort when cpu goes offline (Tencent#4370)

* Update release-python.yml

* small fixes

* unpack list input

* Remove LSTM2

* fix LSTM

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