Cannot run training for LSTM or GRU imported from ONNX

[alex19999]

Try to train some RNN model from ONNX. PyTorch code:

import os
import numpy as np
import torch
import torch.nn as nn

class TEST_LSTM(nn.Module):
    def __init__(self):
        super(TEST_LSTM, self).__init__()
        self.lstm = nn.LSTM(34, 6, 1, batch_first=True)

    def forward(self, x):
        return self.lstm(x.view((-1, 48, 34)))[0]

# pytorch
model = TEST_LSTM()
model.eval()
dummy_input = np.zeros((256, 48, 34), dtype=np.float32)

# onnx
torch.onnx.export(model, torch.tensor(dummy_input), "lstm.onnx", verbose=False)

Then I convert it using converter into .mnn like this: /MNNConvert -f ONNX —modelFile lstm.onnx —MNNModel lstm.mnn —bizCode biz —forTraining

And write dummy training code. The problem is that size of trainable params is zero and nothing happens on backward. Could you help me please to run training for LSTM?

using namespace MNN;
using namespace MNN::Express;
using namespace MNN::Train;

class LSTMTrain : public DemoUnit {
public:
virtual int run(int argc, const char* argv[]) override {

std::cout « "Started LSTM training..." « std::endl;
if (argc < 2) {
std::cout « "usage: ./runTrainDemo.out LSTM /path/to/model" « std::endl;
return 0;
}

const size_t batchSize = 256;
const size_t inputLength = 48;
const size_t inputSize = 34;
const size_t hiddenSize = 6;

std::cout « "Configure training framework params..." « std::endl;
auto exe = Executor::getGlobalExecutor();
BackendConfig config;
exe->setGlobalExecutorConfig(MNN_FORWARD_CPU, config, 1);

std::cout « "Creating model..." « std::endl;
auto varMap = Variable::loadMap(argv[1]);
auto inputOutputs = Variable::getInputAndOutput(varMap);
auto input = inputOutputs.first["input"];
auto output = inputOutputs.second.begin()->second;
std::shared_ptr<Module> model;
model.reset(NN::extract({input}, {output}, true));
model->setIsTraining(true);

auto parameters = model->parameters();
std::cout « "Trainable params size: " « parameters.size() « std::endl;
for (size_t i = 0; i < parameters.size(); ++i)
{
auto size = parameters[i].get()->getInfo()->size;
std::cout « i « " " « parameters[i].get()->name() « " " « size « std::endl;
}
auto x = _Input({batchSize, inputLength, inputSize});
auto labels = _Input({batchSize, inputLength, hiddenSize});
auto labelsPtr = labels->writeMap<float>();
auto xPtr = x->writeMap<float>();
for (int v = 0; v < batchSize * inputLength * inputSize; ++v) {
xPtr[v] = 1.0f;
if (v < batchSize * hiddenSize)
{
hPtr[v] = 0.0f;
}

if (v < batchSize * inputLength * hiddenSize)
{
labelsPtr[v] = 1.0f;
}
}

std::cout « "Configure optimizer..." « std::endl;
std::shared_ptr<SGD> sgd(new SGD(model));
sgd->setLearningRate(0.1f);

std::cout « "Training model..." « std::endl;
for (size_t i = 0; i < 1; ++i)
{
auto y = model->forward(x);
auto yPtr = y->readMap<float>();
for (int v = 0; v < 5; ++v) {
std::cout « v « " " « yPtr[v] « std::endl;
}
auto loss = _CrossEntropy(y, labels) / _Const(10.0f);
auto lossPtr = loss->readMap<float>();
std::cout « "Loss: " « lossPtr[0] « std::endl;
sgd->step(loss);
}
\
return 0;
}
};

平台(如果交叉编译请再附上交叉编译目标平台):

Platform(Include target platform as well if cross-compiling):

Ubuntu 18.04

Github版本:

Github Version:

直接下载ZIP包请提供下载日期以及压缩包注释里的git版本(可通过7z l zip包路径命令并在输出信息中搜索Comment 获得，形如Comment = bc80b11110cd440aacdabbf59658d630527a7f2b)。 git clone请提供 git commit 第一行的commit id

Provide date (or better yet, git revision from the comment section of the zip. Obtainable using 7z l PATH/TO/ZIP and search for Comment in the output) if downloading source as zip,otherwise provide the first commit id from the output of git commit

commit 82630820c04402e6282e7c717c4fc531c23808e2 (HEAD -> master, origin/master, origin/HEAD)

编译方式:

Compiling Method

请在这里粘贴cmake参数或使用的cmake脚本路径以及完整输出
Paste cmake arguments or path of the build script used here as well as the full log of the cmake proess here or pastebin

cmake .. -DMNN_BUILD_TRAIN=ON -DMNN_BUILD_TRAIN_MINI=OFF -DMNN_USE_OPENCV=OFF -DMNN_BUILD_CONVERTER=true
-- The C compiler identification is GNU 10.3.0
-- The CXX compiler identification is GNU 9.3.0
-- The ASM compiler identification is GNU
-- Found assembler: /usr/bin/cc
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found PythonInterp: /usr/bin/python3.8 (found version "3.8.10")
--
-- 3.19.0.0
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Failed
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Found Threads: TRUE
-- Performing Test protobuf_HAVE_BUILTIN_ATOMICS
-- Performing Test protobuf_HAVE_BUILTIN_ATOMICS - Success
-- Use Threadpool, forbid openmp
-- >>>>>>>>>>>>>
-- MNN BUILD INFO:
--      System: Linux
--      Processor: x86_64
--      Metal: OFF
--      OpenCL: OFF
--      OpenGL: OFF
--      Vulkan: OFF
--      ARM82: OFF
--      oneDNN: OFF
--      TensorRT: OFF
--      CoreML: OFF
--      CUDA: OFF
--      OpenMP: OFF
--      BF16:
--      ThreadPool: ON
--      Hidden: TRUE
--      Model Auth: OFF
--      Build Path: /home/am/MNN/build
-- x86_64: Open SSE
-- MNN_AVX512:OFF
-- Onnx:
-- Configuring done
-- Generating done

编译日志:

Build Log:

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In file included from /home/am/MNN/3rd_party/protobuf/src/google/protobuf/generated_message_tctable_lite.cc:36:
/home/am/MNN/3rd_party/protobuf/src/google/protobuf/generated_message_tctable_impl.h: In function ‘void google::protobuf::internal::AlignFail(uintptr_t) [with long unsigned int align = 4]’:
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  103 | }
      | ^
/home/am/MNN/3rd_party/protobuf/src/google/protobuf/generated_message_tctable_impl.h: In function ‘void google::protobuf::internal::AlignFail(uintptr_t) [with long unsigned int align = 8]’:
/home/am/MNN/3rd_party/protobuf/src/google/protobuf/generated_message_tctable_impl.h:103:1: warning: ‘noreturn’ function does return
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In file included from /home/am/MNN/3rd_party/protobuf/src/google/protobuf/generated_message_tctable_lite.cc:36:
/home/am/MNN/3rd_party/protobuf/src/google/protobuf/generated_message_tctable_impl.h: In function ‘void google::protobuf::internal::AlignFail(uintptr_t) [with long unsigned int align = 4]’:
/home/am/MNN/3rd_party/protobuf/src/google/protobuf/generated_message_tctable_impl.h:103:1: warning: ‘noreturn’ function does return
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/home/am/MNN/3rd_party/protobuf/src/google/protobuf/generated_message_tctable_impl.h:103:1: warning: ‘noreturn’ function does return
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[ 82%] Building CXX object tools/converter/CMakeFiles/MNNConverterOpt.dir/source/optimizer/torchextra/TorchAddmm.cpp.o
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In file included from /home/am/MNN/include/MNN/expr/Expr.hpp:18,
                 from /home/am/MNN/express/Expr.cpp:10:
/home/am/MNN/express/Expr.cpp: In static member function ‘static std::vector<MNN::Express::VARP> MNN::Express::Variable::load(const uint8_t*, size_t)’:
/home/am/MNN/express/Expr.cpp:786:38: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘std::vector<std::unique_ptr<MNN::OpT> >::size_type’ {aka ‘long unsigned int’} [-Wformat=]
  786 |     FUNC_PRINT(source->oplists.size());
      |                ~~~~~~~~~~~~~~~~~~~~~~
      |                                    |
      |                                    std::vector<std::unique_ptr<MNN::OpT> >::size_type {aka long unsigned int}
/home/am/MNN/include/MNN/MNNDefine.h:31:39: note: in definition of macro ‘MNN_PRINT’
   31 | #define MNN_PRINT(format, ...) printf(format, ##__VA_ARGS__)
      |                                       ^~~~~~
/home/am/MNN/express/Expr.cpp:786:5: note: in expansion of macro ‘FUNC_PRINT’
  786 |     FUNC_PRINT(source->oplists.size());
      |     ^~~~~~~~~~
[ 84%] Built target MNNConverterTF
[ 84%] Linking CXX executable ../../MNNDump2Json
[ 84%] Built target MNNDump2Json
[ 84%] Built target MNNConverterOpt
[ 84%] Linking CXX executable ../../OnnxClip
[ 84%] Built target MNNConverterTFL
[ 84%] Built target OnnxClip
/home/am/MNN/express/Expr.cpp: In static member function ‘static void MNN::Express::Variable::save(const std::vector<MNN::Express::VARP>&, MNN::NetT*)’:
/home/am/MNN/express/Expr.cpp:26:24: warning: ‘__builtin___snprintf_chk’ output may be truncated before the last format character [-Wformat-truncation=]
   26 |     snprintf(s, 10, "%d", index);
      |                        ^
In file included from /usr/include/stdio.h:867,
                 from /usr/include/c++/9/cstdio:42,
                 from /usr/include/c++/9/ext/string_conversions.h:43,
                 from /usr/include/c++/9/bits/basic_string.h:6493,
                 from /usr/include/c++/9/string:55,
                 from /usr/include/c++/9/stdexcept:39,
                 from /usr/include/c++/9/array:39,
                 from /usr/include/c++/9/tuple:39,
                 from /usr/include/c++/9/functional:54,
                 from /home/am/MNN/include/MNN/expr/Expr.hpp:12,
                 from /home/am/MNN/express/Expr.cpp:10:
/usr/include/x86_64-linux-gnu/bits/stdio2.h:67:35: note: ‘__builtin___snprintf_chk’ output between 2 and 11 bytes into a destination of size 10
   67 |   return __builtin___snprintf_chk (__s, __n, __USE_FORTIFY_LEVEL - 1,
      |          ~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
   68 |        __bos (__s), __fmt, __va_arg_pack ());
      |        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
[ 84%] Linking CXX shared library libMNN_Express.so
[ 84%] Built target MNN_Express
[ 84%] Building CXX object tools/train/CMakeFiles/MNNTrain.dir/source/grad/BinaryGrad.cpp.o
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/home/am/MNN/tools/cpp/getPerformance.cpp: In function ‘void getFreqKhz(int, std::vector<int>&)’:
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/home/am/MNN/tools/cpp/getPerformance.cpp:84:15: warning: ignoring return value of ‘int fscanf(FILE*, const char*, ...)’, declared with attribute warn_unused_result [-Wunused-result]
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[xiao-keeplearning]

Hi, I also encountered the same problem, may I ask you to solve it later?

[github-actions[bot]]

Marking as stale. No activity in 60 days.