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PyTorch bindings for Warp-ctc built with VS2017

This is an extension onto the original repo found here. This is a branch for Visual Studio 2017 building, with the errors fixed while installing from pytorch_bindings branch. The installation has been successfully performed in the following environments:

Win10 cuda10.1 anaconda3 python3.7 pytorch1.0 vs2017

Win7 cuda9.0 anaconda3 pytorch3.7 pytorch1.0 vs2015

Most of our modifications follow amberblade's (It is a Windows compatible version of SeanNaren's work, but cannot work with pytorch1.0). In case you should modify it yourself, all the necessary modifications can be viewed in the commit fix errors in vs2017 building. You can also refer to "Specific Modifications" for all the modifications and the error messages that would have be encountered during the installation without the modifications.

Installation

Install PyTorch v1.0 or higher.

From within a new warp-ctc clone you could build WarpCTC like this:

git clone -b vs2017 https://github.com/hzli-ucas/warp-ctc.git
cd warp-ctc
mkdir build
cd build
cmake -G "Visual Studio 15 2017 Win64" ..

If using vs2015 instead of vs2017 for building, replace the cmake -G "Visual Studio 15 2017 Win64" .. with cmake -G "Visual Studio 14 2015 Win64" ...

Open the warp-ctc/build/ctc_release.sln with vs2017, and build the project ALL_BUILD (Release x64). Then install the bindings:

cd ../pytorch_binding
python setup.py install

At last, copy warp-ctc/build/Release/warpctc.dll to the path where you install the warpctc. For example, for the installation performed with Anaconda3 within an environment named "pytorch", the path oughts to be: Anaconda3/envs/pytorch/Lib/site-packages/warpctc_pytorch-0.1-py3.7-win-amd64.egg/warpctc_pytorch/

You could use the following commands to check that warpctc_pytorch has been successfully installed:

python tests/test_cpu.py
python tests/test_gpu.py

Example to use the bindings below.

import torch
from warpctc_pytorch import CTCLoss
ctc_loss = CTCLoss()
# expected shape of seqLength x batchSize x alphabet_size
probs = torch.FloatTensor([[[0.1, 0.6, 0.1, 0.1, 0.1], [0.1, 0.1, 0.6, 0.1, 0.1]]]).transpose(0, 1).contiguous()
labels = torch.IntTensor([1, 2])
label_sizes = torch.IntTensor([2])
probs_sizes = torch.IntTensor([2])
probs.requires_grad_(True)  # tells autograd to compute gradients for probs
cost = ctc_loss(probs, labels, probs_sizes, label_sizes)
cost.backward()

Documentation

CTCLoss(size_average=False, length_average=False)
    # size_average (bool): normalize the loss by the batch size (default: False)
    # length_average (bool): normalize the loss by the total number of frames in the batch. If True, supersedes size_average (default: False)

forward(acts, labels, act_lens, label_lens)
    # acts: Tensor of (seqLength x batch x outputDim) containing output activations from network (before softmax)
    # labels: 1 dimensional Tensor containing all the targets of the batch in one large sequence
    # act_lens: Tensor of size (batch) containing size of each output sequence from the network
    # label_lens: Tensor of (batch) containing label length of each example

Specific Modifications

When build the project with vs2017, there might be

2>F:/warp-ctc/src/ctc_entrypoint.cu(1): error : this declaration has no storage class or type specifier
2>F:/warp-ctc/src/ctc_entrypoint.cu(1): error : expected a ";"

warp-ctc/src/ctc_entrypoint.cu: change ctc_entrypoint.cpp to #include "ctc_entrypoint.cpp"

2>f:\warp-ctc\include\contrib\moderngpu\include\device\intrinsics.cuh(115): error : identifier "__shfl_up" is undefined
2>f:\warp-ctc\include\contrib\moderngpu\include\device\intrinsics.cuh(125): error : identifier "__shfl_up" is undefined

warp-ctc/CMakeLists.txt: comment out set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -gencode arch=compute_70,code=sm_70")

3>f:\warp-ctc\tests\test.h(57): error : namespace "std" has no member "max"

warp-ctc\tests\test.h: add #include <algorithm>

3>test_gpu_generated_test_gpu.cu.obj : error LNK2019: 无法解析的外部符号 get_warpctc_version，该符号在函数 main 中被引用
3>test_gpu_generated_test_gpu.cu.obj : error LNK2019: 无法解析的外部符号 ctcGetStatusString，该符号在函数 "float __cdecl grad_check(int,int,class std::vector<float,class std::allocator<float> > &,class std::vector<class std::vector<int,class std::allocator<int> >,class std::allocator<class std::vector<int,class std::allocator<int> > > > const &,class std::vector<int,class std::allocator<int> > const &)" (?grad_check@@YAMHHAEAV?$vector@MV?$allocator@M@std@@@std@@AEBV?$vector@V?$vector@HV?$allocator@H@std@@@std@@V?$allocator@V?$vector@HV?$allocator@H@std@@@std@@@2@@2@AEBV?$vector@HV?$allocator@H@std@@@2@@Z) 中被引用
3>test_gpu_generated_test_gpu.cu.obj : error LNK2019: 无法解析的外部符号 compute_ctc_loss，该符号在函数 "float __cdecl grad_check(int,int,class std::vector<float,class std::allocator<float> > &,class std::vector<class std::vector<int,class std::allocator<int> >,class std::allocator<class std::vector<int,class std::allocator<int> > > > const &,class std::vector<int,class std::allocator<int> > const &)" (?grad_check@@YAMHHAEAV?$vector@MV?$allocator@M@std@@@std@@AEBV?$vector@V?$vector@HV?$allocator@H@std@@@std@@V?$allocator@V?$vector@HV?$allocator@H@std@@@std@@@2@@2@AEBV?$vector@HV?$allocator@H@std@@@2@@Z) 中被引用
3>test_gpu_generated_test_gpu.cu.obj : error LNK2019: 无法解析的外部符号 get_workspace_size，该符号在函数 "float __cdecl grad_check(int,int,class std::vector<float,class std::allocator<float> > &,class std::vector<class std::vector<int,class std::allocator<int> >,class std::allocator<class std::vector<int,class std::allocator<int> > > > const &,class std::vector<int,class std::allocator<int> > const &)" (?grad_check@@YAMHHAEAV?$vector@MV?$allocator@M@std@@@std@@AEBV?$vector@V?$vector@HV?$allocator@H@std@@@std@@V?$allocator@V?$vector@HV?$allocator@H@std@@@std@@@2@@2@AEBV?$vector@HV?$allocator@H@std@@@2@@Z) 中被引用
3>F:\warp-ctc\build\Release\test_gpu.exe : fatal error LNK1120: 4 个无法解析的外部命令

warp-ctc\include\ctc.h: put __declspec(dllexport) in front of the four variables/functions

When run the following command,

cd ../pytorch_binding
python setup.py install

there might be

(pytorch) F:\warp-ctc\pytorch_binding>python setup.py install
Could not find libwarpctc.so in ../build.
Build warp-ctc and set WARP_CTC_PATH to the location of libwarpctc.so (default is '../build')

warp-ctc/pytorch_binding/steup.py: change .so to .dll change ../build to ../build/Release change libwarpctc to warpctc

binding.obj : error LNK2001: 无法解析的外部符号 "struct THCState * state" (?state@@3PEAUTHCState@@EA)
build\lib.win-amd64-3.7\warpctc_pytorch\_warp_ctc.cp37-win_amd64.pyd : fatal error LNK1120: 1 个无法解析的外部命令
error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio\\2017\\Community\\VC\\Tools\\MSVC\\14.16.27023\\bin\\HostX86\\x64\\link.exe' failed with exit status 1120

warp-ctc/pyrotch_binding/src/binding.cpp: change extern THCState* state; to THCState* state = at::globalContext().lazyInitCUDA();

The error might occur using the bindings

>>> import warpctc_pytorch
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\dell\Anaconda3\envs\pytorch\lib\site-packages\warpctc_pytorch-0.1-py3.7-win-amd64.egg\warpctc_pytorch\__init__.py", line 6, in <module>
    from ._warp_ctc import *
ImportError: DLL load failed: 找不到指定的模块。

copy warp-ctc/build/Release/warpctc.dll to where/you/install/the/warpctc_pytorch/