Vs2017

CMakeLists.txt

@@ -48,7 +48,7 @@ IF (CUDA_VERSION GREATER 7.6)
 ENDIF()
 
 IF (CUDA_VERSION GREATER 8.9)
-    set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -gencode arch=compute_70,code=sm_70")
+    #set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -gencode arch=compute_70,code=sm_70")
 ENDIF()
 
 if (NOT APPLE)

README.md

@@ -1,39 +1,40 @@
-# PyTorch bindings for Warp-ctc
-
-[![Build Status](https://travis-ci.org/SeanNaren/warp-ctc.svg?branch=pytorch_bindings)](https://travis-ci.org/SeanNaren/warp-ctc)
+# PyTorch bindings for Warp-ctc built with VS2017
 
 This is an extension onto the original repo found [here](https://github.com/baidu-research/warp-ctc).
+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
 
-## Installation
+>Win7 cuda9.0 anaconda3 pytorch3.7 pytorch1.0 vs2015
 
-Install [PyTorch](https://github.com/pytorch/pytorch#installation) v0.4.
+Most of our modifications follow [amberblade's](https://github.com/amberblade/warp-ctc) (It is a Windows compatible version of [SeanNaren's work](https://github.com/SeanNaren/warp-ctc), 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](https://github.com/hzli-ucas/warp-ctc/commit/a19202c399b8e40adc96739df946d3bdb26eefac). 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.
 
-`WARP_CTC_PATH` should be set to the location of a built WarpCTC
-(i.e. `libwarpctc.so`).  This defaults to `../build`, so from within a
-new warp-ctc clone you could build WarpCTC like this:
+## Installation
 
-```bash
-git clone https://github.com/SeanNaren/warp-ctc.git
+Install [PyTorch](https://github.com/pytorch/pytorch#installation) v1.0 or higher.
+
+From within a new warp-ctc clone you could build WarpCTC like this:
+```cmd
+git clone -b vs2017 https://github.com/hzli-ucas/warp-ctc.git
 cd warp-ctc
-mkdir build; cd build
-cmake ..
-make
+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" ..`.
 
-Now install the bindings:
-```bash
-cd pytorch_binding
+Open the `warp-ctc/build/ctc_release.sln` with vs2017, and build the project `ALL_BUILD` (Release x64). Then install the bindings:
+```cmd
+cd ../pytorch_binding
 python setup.py install
 ```
 
-If you try the above and get a dlopen error on OSX with anaconda3 (as recommended by pytorch):
-```bash
-cd ../pytorch_binding
-python setup.py install
-cd ../build
-cp libwarpctc.dylib /Users/$WHOAMI/anaconda3/lib
+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:
+```cmd
+python tests/test_cpu.py
+python tests/test_gpu.py
 ```
-This will resolve the library not loaded error. This can be easily modified to work with other python installs if needed.
 
 Example to use the bindings below.
 
@@ -63,4 +64,84 @@ forward(acts, labels, act_lens, label_lens)
     # 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,***
+```cmd
+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/`

include/ctc.h

@@ -22,13 +22,13 @@ typedef enum {
 } ctcStatus_t;
 
 /** Returns a single integer which specifies the API version of the warpctc library */
-int get_warpctc_version();
+__declspec(dllexport) int get_warpctc_version();
 
 /** Returns a string containing a description of status that was passed in
  *  \param[in] status identifies which string should be returned
  *  \return C style string containing the text description
  *  */
-const char* ctcGetStatusString(ctcStatus_t status);
+__declspec(dllexport) const char* ctcGetStatusString(ctcStatus_t status);
 
 typedef enum {
     CTC_CPU = 0,
@@ -91,7 +91,7 @@ struct ctcOptions {
  *  \return Status information
  *
  * */
-ctcStatus_t compute_ctc_loss(const float* const activations,
+__declspec(dllexport) ctcStatus_t compute_ctc_loss(const float* const activations,
                              float* gradients,
                              const int* const flat_labels,
                              const int* const label_lengths,
@@ -120,7 +120,7 @@ ctcStatus_t compute_ctc_loss(const float* const activations,
  *
  *  \return Status information
  **/
-ctcStatus_t get_workspace_size(const int* const label_lengths,
+__declspec(dllexport) ctcStatus_t get_workspace_size(const int* const label_lengths,
                                const int* const input_lengths,
                                int alphabet_size, int minibatch,
                                ctcOptions info,

pytorch_binding/setup.py

@@ -7,19 +7,16 @@
 import torch
 
 extra_compile_args = ['-std=c++11', '-fPIC']
-warp_ctc_path = "../build"
+warp_ctc_path = "../build/Release"
 
-if platform.system() == 'Darwin':
-    lib_ext = ".dylib"
-else:
-    lib_ext = ".so"
+lib_ext = ".dll"
 
 if "WARP_CTC_PATH" in os.environ:
     warp_ctc_path = os.environ["WARP_CTC_PATH"]
-if not os.path.exists(os.path.join(warp_ctc_path, "libwarpctc" + lib_ext)):
-    print(("Could not find libwarpctc.so in {}.\n"
+if not os.path.exists(os.path.join(warp_ctc_path, "warpctc" + lib_ext)):
+    print(("Could not find warpctc.dll in {}.\n"
            "Build warp-ctc and set WARP_CTC_PATH to the location of"
-           " libwarpctc.so (default is '../build')").format(warp_ctc_path))
+           " warpctc.dll (default is '%s')").format(warp_ctc_path))
     sys.exit(1)
 
 include_dirs = [os.path.realpath('../include')]

pytorch_binding/src/binding.cpp

@@ -7,11 +7,10 @@
 
 #ifdef WARPCTC_ENABLE_GPU
 	#include "ATen/cuda/CUDAContext.h"
-	#include "ATen/cuda/CUDAGuard.h"
 	#include "ATen/cuda/CUDAEvent.h"
 
     #include "THC.h"
-    extern THCState* state;
+    THCState* state = at::globalContext().lazyInitCUDA();
 #endif
 
 #include "ctc.h"

src/ctc_entrypoint.cu

@@ -1 +1 @@
-ctc_entrypoint.cpp
+#include "ctc_entrypoint.cpp"

tests/test.h

@@ -4,7 +4,7 @@
 #include <vector>
 #include <limits>
 #include <numeric>
-
+#include <algorithm>
 
 #include <ctc.h>