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Memory Pool Improvement For Variadic Sized Inputs #4190
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…something strange in packing layout;
I'm not sure why old commits occur here. Actually only cee9d22 is the thing I did the magic. |
Codecov Report
@@ 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
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please investigate sanitizer error |
I've tried but cannot reproduce the error of |
cmake -DCMAKE_BUILD_TYPE=debug -DNCNN_ASAN=ON -DNCNN_BUILD_TESTS=ON -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF .. |
May be you can refer to this line about this file linux-x64-cpu-gcc-san.yml :) |
(First of all, sorry for late response.) It turns out that ASAN switch of CMAKE is ignored if it is set in VS Code |
src/allocator.cpp
Outdated
@@ -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.
src/allocator.cpp
Outdated
@@ -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.
Thanks for your contribution ! |
* 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) --- updated-dependencies: - dependency-name: pypa/cibuildwheel dependency-type: direct:production update-type: version-update:semver-minor ... 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(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 ... Signed-off-by: dependabot[bot] <support@github.com> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * fix pnnx softmax/normalize/slice negative axis conversion to ncnn (Tencent#4284) * pnnx glu batchindex aware conversion (Tencent#4285) * 1. 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Signed-off-by: dependabot[bot] <support@github.com> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * 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. 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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.
Simulated Results
Following codes can be used to simulate a multiple stage running scenario.
Memory usage:
(Memory usage is gathered with valgrind:
valgrind --tool=massif --time-unit=ms <executable>
)