From b68f141481052dd53ebd5a2675b053740d745239 Mon Sep 17 00:00:00 2001
From: jackbenn <jack@bennetto.com>
Date: Mon, 26 Feb 2018 16:04:14 -0800
Subject: [PATCH] changed default values of alpha/beta to 1,1 in prior

---
 pymc3/introduction.ipynb                   | 5565 +-------------------
 slides/probabilistic-programming-intro.pdf |  Bin 1309996 -> 1324042 bytes
 slides/probabilistic-programming-intro.tex |    4 +-
 3 files changed, 39 insertions(+), 5530 deletions(-)

diff --git a/pymc3/introduction.ipynb b/pymc3/introduction.ipynb
index fea7ae4..a9bf995 100644
--- a/pymc3/introduction.ipynb
+++ b/pymc3/introduction.ipynb
@@ -14,5502 +14,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "1 #define _CUDA_NDARRAY_C\n",
-      "2 \n",
-      "3 #include <Python.h>\n",
-      "4 #include <structmember.h>\n",
-      "5 #include \"theano_mod_helper.h\"\n",
-      "6 \n",
-      "7 #include <numpy/arrayobject.h>\n",
-      "8 #include <iostream>\n",
-      "9 \n",
-      "10 #include \"cuda_ndarray.cuh\"\n",
-      "11 \n",
-      "12 #ifndef CNMEM_DLLEXPORT\n",
-      "13 #define CNMEM_DLLEXPORT\n",
-      "14 #endif\n",
-      "15 \n",
-      "16 #include \"cnmem.h\"\n",
-      "17 #include \"cnmem.cpp\"\n",
-      "18 \n",
-      "19 //If true, when there is a gpu malloc or free error, we print the size of allocated memory on the device.\n",
-      "20 #define COMPUTE_GPU_MEM_USED 0\n",
-      "21 \n",
-      "22 //If true, we fill with NAN allocated device memory.\n",
-      "23 #define ALLOC_MEMSET 0\n",
-      "24 \n",
-      "25 //If true, we print out when we free a device pointer, uninitialize a\n",
-      "26 //CudaNdarray, or allocate a device pointer\n",
-      "27 #define PRINT_FREE_MALLOC 0\n",
-      "28 \n",
-      "29 //If true, we do error checking at the start of functions, to make sure there\n",
-      "30 //is not a pre-existing error when the function is called.\n",
-      "31 //You probably need to set the environment variable\n",
-      "32 //CUDA_LAUNCH_BLOCKING=1, and/or modify the CNDA_THREAD_SYNC\n",
-      "33 //preprocessor macro in cuda_ndarray.cuh\n",
-      "34 //if you want this to work.\n",
-      "35 #define PRECHECK_ERROR 0\n",
-      "36 \n",
-      "37 cublasHandle_t handle = NULL;\n",
-      "38 int* err_var = NULL;\n",
-      "39 \n",
-      "40 /////////////////////////\n",
-      "41 // Alloc and Free\n",
-      "42 /////////////////////////\n",
-      "43 \n",
-      "44 static int g_gpu_context_active = 0;\n",
-      "45 \n",
-      "46 \n",
-      "47 PyObject *\n",
-      "48 CudaNdarray_Dimshuffle(PyObject* _unused, PyObject* args);\n",
-      "49 static PyObject *CudaNdarray_get_shape(CudaNdarray *self, void *closure);\n",
-      "50 \n",
-      "51 \n",
-      "52 /**\n",
-      "53  *\n",
-      "54  * In the test program I'm using, the _outstanding_mallocs decreases with every call.\n",
-      "55  * This suggests there are more free() calls being made than alloc(), but I can't figure out why.\n",
-      "56  *\n",
-      "57  */\n",
-      "58 int _outstanding_mallocs[] = {0,0};\n",
-      "59 \n",
-      "60 #if COMPUTE_GPU_MEM_USED\n",
-      "61 size_t _allocated_size = 0;\n",
-      "62 size_t _max_allocated_size = 0;\n",
-      "63 \n",
-      "64 const int TABLE_SIZE = 10000;\n",
-      "65 struct table_struct{\n",
-      "66     void* ptr;\n",
-      "67     size_t size;\n",
-      "68 };\n",
-      "69 table_struct _alloc_size_table[TABLE_SIZE];\n",
-      "70 #endif\n",
-      "71 \n",
-      "72 void * device_malloc(size_t size)\n",
-      "73 {\n",
-      "74     return device_malloc(size, VERBOSE_DEVICE_MALLOC);\n",
-      "75 }\n",
-      "76 \n",
-      "77 static bool g_use_cnmem = false;\n",
-      "78 static const int g_max_devices = 8;\n",
-      "79 int initCnmem(int card_number_provided, int card_nb, size_t mem) {\n",
-      "80     static bool cnmemInitialized = false;\n",
-      "81     if(cnmemInitialized) {\n",
-      "82         return 0;\n",
-      "83     }\n",
-      "84     // On stderr to be at the same place as \"Using gpu device...\"\n",
-      "85     int numDevices = 0;\n",
-      "86     cnmemDevice_t devices[g_max_devices];\n",
-      "87     if(cudaGetDeviceCount(&numDevices) != cudaSuccess) {\n",
-      "88         PyErr_Format(PyExc_RuntimeError,\n",
-      "89                      \"initCnmem: 'cudaGetDeviceCount' failed! Reason=%s\\n\",\n",
-      "90                      cudaGetErrorString(cudaGetLastError()));\n",
-      "91         return -1;\n",
-      "92     }\n",
-      "93     if(card_number_provided){\n",
-      "94         numDevices = 1;\n",
-      "95         int i = 0;\n",
-      "96         devices[i].device = card_nb;\n",
-      "97         devices[i].size = mem;\n",
-      "98         ///@TODO: thejaswi: add support for multiple streams\n",
-      "99         devices[i].numStreams = 0;\n",
-      "100         devices[i].streams = NULL;\n",
-      "101         devices[i].streamSizes = NULL;\n",
-      "102     }else{\n",
-      "103         for(int i=0;i<numDevices;++i) {\n",
-      "104             devices[i].device = i;\n",
-      "105             devices[i].size = mem;\n",
-      "106             ///@TODO: thejaswi: add support for multiple streams\n",
-      "107             devices[i].numStreams = 0;\n",
-      "108             devices[i].streams = NULL;\n",
-      "109         }\n",
-      "110     }\n",
-      "111 \n",
-      "112     ///@TODO: thejaswi: passing custom cnmem flags?\n",
-      "113     cnmemStatus_t status = cnmemInit(numDevices, devices, CNMEM_FLAGS_DEFAULT);\n",
-      "114     if(status != CNMEM_STATUS_SUCCESS) {\n",
-      "115         PyErr_Format(PyExc_RuntimeError,\n",
-      "116                      \"initCnmem: cnmemInit call failed! Reason=%s. numdev=%d\\n\",\n",
-      "117                      cnmemGetErrorString(status), numDevices);\n",
-      "118         return -1;\n",
-      "119     }\n",
-      "120     cnmemInitialized = true;\n",
-      "121     return 0;\n",
-      "122 }\n",
-      "123 \n",
-      "124 void * device_malloc(size_t size, int verbose)\n",
-      "125 {\n",
-      "126     #if PRECHECK_ERROR\n",
-      "127         cudaThreadSynchronize();\n",
-      "128         cudaError_t prevError = cudaGetLastError();\n",
-      "129         if (cudaSuccess != prevError)\n",
-      "130         {\n",
-      "131             fprintf(stderr,\n",
-      "132                     \"Error existed before calling device_malloc. %s\\n\",\n",
-      "133                     cudaGetErrorString(prevError)\n",
-      "134                     );\n",
-      "135         }\n",
-      "136     #endif\n",
-      "137     void * rval=NULL;\n",
-      "138     ///@TODO: thejaswi: support for multiple-streams?\n",
-      "139     if(g_use_cnmem) {\n",
-      "140         cnmemStatus_t status = CNMEM_STATUS_SUCCESS;\n",
-      "141         status = cnmemMalloc(&rval, size, NULL);\n",
-      "142         if(status != CNMEM_STATUS_SUCCESS) {\n",
-      "143             PyErr_Format(PyExc_MemoryError,\n",
-      "144                          \"Error allocating %llu bytes of device memory (%s).\",\n",
-      "145                          (unsigned long long)size, cnmemGetErrorString(status));\n",
-      "146             return NULL;\n",
-      "147         }\n",
-      "148     }\n",
-      "149     else {\n",
-      "150         cudaError_t err = cudaMalloc(&rval, size);\n",
-      "151         if (cudaSuccess != err)\n",
-      "152         {\n",
-      "153             // Clear the error flag, cudaMalloc doesn't do it.\n",
-      "154             // Currently this returns the same thing as err, but if in future\n",
-      "155             // it returns something else I still don't see why we should ignore\n",
-      "156             // it.  All we want to do here is reset the flag.\n",
-      "157             cudaGetLastError();\n",
-      "158             if (verbose)\n",
-      "159             {\n",
-      "160                 size_t free = 0, total = 0;\n",
-      "161                 cudaError_t err2 = cudaMemGetInfo(&free, &total);\n",
-      "162                 if (err2 != cudaSuccess){\n",
-      "163                     cudaGetLastError();\n",
-      "164                     fprintf(stderr,\n",
-      "165                             \"Error when trying to find the memory information\"\n",
-      "166                             \" on the GPU: %s\\n\", cudaGetErrorString(err2));\n",
-      "167                 }\n",
-      "168                 #if COMPUTE_GPU_MEM_USED\n",
-      "169                     fprintf(stderr,\n",
-      "170                             \"Error allocating %llu bytes of device memory (%s).\"\n",
-      "171                             \" new total bytes allocated: %llu.\"\n",
-      "172                             \" Driver report %llu bytes free and %llu bytes total \\n\",\n",
-      "173                             (unsigned long long)size, cudaGetErrorString(err), (unsigned long long)_allocated_size,\n",
-      "174                             (unsigned long long)free, (unsigned long long)total);\n",
-      "175                 #else\n",
-      "176                     fprintf(stderr,\n",
-      "177                             \"Error allocating %llu bytes of device memory (%s).\"\n",
-      "178                             \" Driver report %llu bytes free and %llu bytes total \\n\",\n",
-      "179                             (unsigned long long)size, cudaGetErrorString(err), (unsigned long long)free, (unsigned long long)total);\n",
-      "180                 #endif\n",
-      "181             }\n",
-      "182             PyErr_Format(PyExc_MemoryError,\n",
-      "183                          \"Error allocating %llu bytes of device memory (%s).\",\n",
-      "184                          (unsigned long long)size, cudaGetErrorString(err));\n",
-      "185             return NULL;\n",
-      "186         }\n",
-      "187     }\n",
-      "188     if (rval != NULL){\n",
-      "189         // Can it happen that cudaMalloc return cudaSuccess, but return a NULL ptr?\n",
-      "190         // Could this be what happen if size is 0?\n",
-      "191         _outstanding_mallocs[0] += 1;\n",
-      "192 \n",
-      "193 #if COMPUTE_GPU_MEM_USED\n",
-      "194         _allocated_size += size;\n",
-      "195         _max_allocated_size = std::max(_max_allocated_size, _allocated_size);\n",
-      "196         int i = 0;\n",
-      "197         for(;i<TABLE_SIZE;i++){\n",
-      "198             if(NULL==_alloc_size_table[i].ptr){\n",
-      "199                 _alloc_size_table[i].ptr=rval;\n",
-      "200                 _alloc_size_table[i].size=size;\n",
-      "201                 break;\n",
-      "202             }\n",
-      "203         }\n",
-      "204         if (i == TABLE_SIZE){\n",
-      "205             fprintf(stderr,\n",
-      "206                     \"When tracking GPU malloc, our table size wasn't big enough.\"\n",
-      "207                     \" So we loose some tracking. Raise the value of TABLE_SIZE in the file cuda_ndarra.cu\");\n",
-      "208         }\n",
-      "209 #endif\n",
-      "210     }\n",
-      "211     //fprintf(stderr,\n",
-      "212     //\"allocated %li bytes of device memory (%s). new total bytes allocated: %d. ptr: %p\\n\",\n",
-      "213     //(long)size, cudaGetErrorString(err),_allocated_size,rval);\n",
-      "214 \n",
-      "215     if(ALLOC_MEMSET){\n",
-      "216         //We init them to nan to make sure we catch more debug case.\n",
-      "217         cudaMemset(rval, 0xFF, size);\n",
-      "218         //printf(\"MEMSET\\n\");\n",
-      "219     }\n",
-      "220     #if PRINT_FREE_MALLOC\n",
-      "221         fprintf(stderr, \"device malloc %p of size %d\\n\", rval, size);\n",
-      "222     #endif\n",
-      "223     return rval;\n",
-      "224 }\n",
-      "225 \n",
-      "226 int device_free(void *ptr)\n",
-      "227 {\n",
-      "228     #if PRECHECK_ERROR\n",
-      "229         cudaThreadSynchronize();\n",
-      "230         cudaError_t prevError = cudaGetLastError();\n",
-      "231         if (cudaSuccess != prevError)\n",
-      "232         {\n",
-      "233             fprintf(stderr,\n",
-      "234                     \"Error existed before calling device_free. %s\\n\",\n",
-      "235                     cudaGetErrorString(prevError)\n",
-      "236                     );\n",
-      "237         }\n",
-      "238     #endif\n",
-      "239     #if PRINT_FREE_MALLOC\n",
-      "240         size_t free = 0, total = 0;\n",
-      "241         cudaError_t err2 = cudaMemGetInfo(&free, &total);\n",
-      "242         if (err2 != cudaSuccess){\n",
-      "243             cudaGetLastError();\n",
-      "244             fprintf(stderr,\n",
-      "245                     \"Error when tring to find the memory information\"\n",
-      "246                     \" on the GPU: %s\\n\", cudaGetErrorString(err2));\n",
-      "247         }\n",
-      "248         #if COMPUTE_GPU_MEM_USED\n",
-      "249         {\n",
-      "250             int i = 0;\n",
-      "251             for(;i<TABLE_SIZE;i++)\n",
-      "252                 if(_alloc_size_table[i].ptr==ptr){\n",
-      "253                     break;\n",
-      "254                 }\n",
-      "255             assert(i<TABLE_SIZE);\n",
-      "256             fprintf(stderr, \"device_free %p of size %d.\"\n",
-      "257                     \" Driver report %d bytes free and %d bytes total \\n\",\n",
-      "258                     ptr, _alloc_size_table[i].size, free, total);\n",
-      "259         }\n",
-      "260         #else\n",
-      "261             fprintf(stderr, \"device_free %p.\"\n",
-      "262                     \" Driver report %d bytes free and %d bytes total \\n\",\n",
-      "263                     ptr, free, total);\n",
-      "264         #endif\n",
-      "265     #endif\n",
-      "266 \n",
-      "267     // if there is no gpu context, the call to cudaFree will fail; skip it entirely\n",
-      "268     if(!g_gpu_context_active) {\n",
-      "269         return 0;\n",
-      "270     }\n",
-      "271 \n",
-      "272     ///@TODO: thejaswi: multi-stream support\n",
-      "273     if(g_use_cnmem) {\n",
-      "274         cnmemStatus_t status = cnmemFree(ptr, NULL);\n",
-      "275         if(status != CNMEM_STATUS_SUCCESS) {\n",
-      "276             fprintf(stderr, \"device_free: cnmemFree call failed! Reason=%s\\n\",\n",
-      "277                     cnmemGetErrorString(status));\n",
-      "278         }\n",
-      "279     }\n",
-      "280     else {\n",
-      "281         // We need sync as the Theano's GC could remove intermediate variable that\n",
-      "282         // are still needed as the gpu kernel are running or in the queue.\n",
-      "283         CNDA_BEGIN_ALLOW_THREADS\n",
-      "284         cudaThreadSynchronize();\n",
-      "285         CNDA_END_ALLOW_THREADS\n",
-      "286 \n",
-      "287         cudaError_t err =  cudaFree(ptr);\n",
-      "288         if (cudaSuccess != err)\n",
-      "289         {\n",
-      "290             // Clear the error flag, cudaFree doesn't do it.\n",
-      "291             // Currently this returns the same thing as err, but if in future\n",
-      "292             // it returns something else I still don't see why we should ignore\n",
-      "293             // it.  All we want to do here is reset the flag.\n",
-      "294             cudaGetLastError();\n",
-      "295             size_t free = 0, total = 0;\n",
-      "296             cudaError_t err2 = cudaMemGetInfo(&free, &total);\n",
-      "297             if (err2 != cudaSuccess){\n",
-      "298                 cudaGetLastError();\n",
-      "299                 fprintf(stderr,\n",
-      "300                         \"Error when tring to find the memory information\"\n",
-      "301                         \" on the GPU: %s\\n\", cudaGetErrorString(err2));\n",
-      "302             }\n",
-      "303             #if COMPUTE_GPU_MEM_USED\n",
-      "304             {\n",
-      "305                 int i = 0;\n",
-      "306                 for(;i<TABLE_SIZE;i++)\n",
-      "307                     if(_alloc_size_table[i].ptr==ptr){\n",
-      "308                         break;\n",
-      "309                     }\n",
-      "310                 assert(i<TABLE_SIZE);\n",
-      "311                 fprintf(stderr,\n",
-      "312                         \"Error freeing device pointer %p (%s) of size %llu. %llu byte already allocated.\"\n",
-      "313                         \" Driver report %llu bytes free and %llu bytes total \\n\",\n",
-      "314                         ptr, cudaGetErrorString(err),\n",
-      "315                         (unsigned long long)_alloc_size_table[i].size, (unsigned long long)_allocated_size, (unsigned long long)free, (unsigned long long)total);\n",
-      "316             }\n",
-      "317             #else\n",
-      "318                 fprintf(stderr,\n",
-      "319                         \"Error freeing device pointer %p (%s).\"\n",
-      "320                         \" Driver report %llu bytes free and %llu bytes total \\n\",\n",
-      "321                         ptr,\n",
-      "322                         cudaGetErrorString(err), (unsigned long long)free, (unsigned long long)total);\n",
-      "323             #endif\n",
-      "324             if (NULL != PyErr_Occurred()){\n",
-      "325                 fprintf(stderr,\n",
-      "326                         \"device_free: cudaFree() returned an error, but there is already an\"\n",
-      "327                         \" Python error set. This happen during the clean up when there is a\"\n",
-      "328                         \" first error and the CUDA driver is in a so bad state that it don't\"\n",
-      "329                         \" work anymore. We keep the previous error set to help debugging it.\");\n",
-      "330                 return -1;\n",
-      "331             }\n",
-      "332             PyErr_Format(PyExc_MemoryError,\n",
-      "333                     \"error freeing device pointer %p (%s)\",\n",
-      "334                     ptr,\n",
-      "335                     cudaGetErrorString(err));\n",
-      "336             return -1;\n",
-      "337         }\n",
-      "338     }\n",
-      "339     _outstanding_mallocs[0] -= (ptr != NULL);\n",
-      "340     #if COMPUTE_GPU_MEM_USED\n",
-      "341         int i=0;\n",
-      "342         size_t total_freed = 0;\n",
-      "343         for(;i<TABLE_SIZE;i++)\n",
-      "344             if(_alloc_size_table[i].ptr==ptr){\n",
-      "345                 _allocated_size -= _alloc_size_table[i].size;\n",
-      "346                 total_freed += _alloc_size_table[i].size;\n",
-      "347                 _alloc_size_table[i].ptr=0;\n",
-      "348                 _alloc_size_table[i].size=0;\n",
-      "349 \n",
-      "350                 break;\n",
-      "351             }\n",
-      "352         //if(i==TABLE_SIZE)\n",
-      "353         //    printf(\"Unallocated unknow size!\\n\");\n",
-      "354         //fprintf(stderr, \"freed %li bytes of device memory (%s). %d already allocated, ptr=%p\\n\", (long)total_freed, cudaGetErrorString(err),_allocated_size,ptr);\n",
-      "355     #endif\n",
-      "356     return 0;\n",
-      "357 }\n",
-      "358 \n",
-      "359 static PyObject *\n",
-      "360 outstanding_mallocs(PyObject* self, PyObject * args)\n",
-      "361 {\n",
-      "362     return PyInt_FromLong(_outstanding_mallocs[0]);\n",
-      "363 }\n",
-      "364 \n",
-      "365 \n",
-      "366 static void *work_mem = NULL;\n",
-      "367 static size_t work_size = 0;\n",
-      "368 \n",
-      "369 /*\n",
-      "370  * Returns a chunk of memory for temporary work inside of an op. You can only\n",
-      "371  * request a single chunk of memory at a time since it is reused.\n",
-      "372  */\n",
-      "373 void *get_work_mem(size_t sz) {\n",
-      "374     if (sz <= work_size)\n",
-      "375         return work_mem;\n",
-      "376     device_free(work_mem);\n",
-      "377     work_mem = device_malloc(sz);\n",
-      "378     work_size = sz;\n",
-      "379     if (work_mem == NULL)\n",
-      "380         work_size = 0;\n",
-      "381     return work_mem;\n",
-      "382 }\n",
-      "383 \n",
-      "384 /////////////////////////\n",
-      "385 // Static helper methods\n",
-      "386 /////////////////////////\n",
-      "387 \n",
-      "388 static void\n",
-      "389 CudaNdarray_null_init(CudaNdarray*self)\n",
-      "390 {\n",
-      "391     self->base = NULL;\n",
-      "392     self->nd = -1;\n",
-      "393     self->host_structure = NULL;\n",
-      "394     self->data_allocated = 0;\n",
-      "395     self->dev_structure_fresh = 1;\n",
-      "396     self->dev_structure = NULL;\n",
-      "397     self->devdata = NULL;\n",
-      "398 }\n",
-      "399 \n",
-      "400 static int\n",
-      "401 CudaNdarray_uninit(CudaNdarray*self)\n",
-      "402 {\n",
-      "403     #if PRINT_FREE_MALLOC\n",
-      "404         fprintf(stderr, \"CudaNdarray_uninit %p\\n\", self);\n",
-      "405     #endif\n",
-      "406     int rval = 0;\n",
-      "407     if (self->data_allocated) {\n",
-      "408         assert(self->devdata);\n",
-      "409         if (device_free(self->devdata))\n",
-      "410         {\n",
-      "411             fprintf(stderr,\n",
-      "412                     \"CudaNdarray_uninit: error freeing self->devdata. (self=%p, self->devata=%p)\\n\",\n",
-      "413                     self, self->devdata);\n",
-      "414             rval = -1;\n",
-      "415         }\n",
-      "416         self->devdata = NULL;\n",
-      "417         self->data_allocated = 0;\n",
-      "418     }\n",
-      "419     if (self->dev_structure)\n",
-      "420     {\n",
-      "421         if (device_free(self->dev_structure))\n",
-      "422         {\n",
-      "423             fprintf(stderr,\n",
-      "424                     \"CudaNdarray_uninit: error freeing dev_structure memory %p (self=%p)\\n\",\n",
-      "425                     self->dev_structure, self);\n",
-      "426             rval = -1;\n",
-      "427         }\n",
-      "428         self->dev_structure = NULL;\n",
-      "429     }\n",
-      "430     if (self->host_structure)\n",
-      "431     {\n",
-      "432         free(self->host_structure);\n",
-      "433         self->host_structure = NULL;\n",
-      "434     }\n",
-      "435     self->nd = -1;\n",
-      "436     Py_XDECREF(self->base);\n",
-      "437     self->base = NULL;\n",
-      "438     return rval;\n",
-      "439 }\n",
-      "440 \n",
-      "441 \n",
-      "442 //make the rightmost coords change fastest\n",
-      "443 //TODO: why does a downward for-loop not work????\n",
-      "444 //TODO: use the log2_dims and driver code to remove / and %\n",
-      "445 //TODO: skip the last division (when d == 0)\n",
-      "446 #define decl_k_elemwise_unary_rowmajor(name, F) \\\n",
-      "447 __global__ void name (unsigned int numEls,  \\\n",
-      "448         unsigned int nd, \\\n",
-      "449         const int * dim,  \\\n",
-      "450         const float * a_data, const int * a_str, \\\n",
-      "451         float * z_data, const int * z_str) \\\n",
-      "452 { \\\n",
-      "453     const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; \\\n",
-      "454     const unsigned int numThreads = blockDim.x * gridDim.x; \\\n",
-      "455  \\\n",
-      "456     for (unsigned int i = idx; i < numEls; i += numThreads) \\\n",
-      "457     { \\\n",
-      "458         unsigned int ii = i; \\\n",
-      "459         const float * a_i = a_data; \\\n",
-      "460         float * z_i = z_data; \\\n",
-      "461         for (unsigned int _d = 0; _d < nd; ++_d) \\\n",
-      "462         { \\\n",
-      "463             unsigned int d = nd - _d-1;  \\\n",
-      "464             int i_d = ii % dim[d]; /* i_d is our position in the d'th dimension   */ \\\n",
-      "465             ii = ii / dim[d]; \\\n",
-      "466             a_i += i_d * a_str[d]; /* increment our a and z pointers by i_d elements */ \\\n",
-      "467             z_i += i_d * z_str[d]; \\\n",
-      "468         } \\\n",
-      "469         z_i[0] = F(a_i[0]); \\\n",
-      "470     } \\\n",
-      "471 }\n",
-      "472 \n",
-      "473 template<typename T> __device__ T unary_copy(T a) { return a; }\n",
-      "474 decl_k_elemwise_unary_rowmajor(k_elemwise_unary_rowmajor_copy, unary_copy<float>)\n",
-      "475 \n",
-      "476 template<typename T> __device__ T unary_exp(T a) { return exp(a); }\n",
-      "477 decl_k_elemwise_unary_rowmajor(k_elemwise_unary_rowmajor_exp, unary_exp<float>)\n",
-      "478 \n",
-      "479 /////////////////////////////\n",
-      "480 // Satisfying reqs to be Type\n",
-      "481 /////////////////////////////\n",
-      "482 \n",
-      "483 //DON'T use directly(if their is other CudaNdarray that point to it, it will cause problem)! use Py_DECREF() instead\n",
-      "484 static void\n",
-      "485 CudaNdarray_dealloc(CudaNdarray* self)\n",
-      "486 {\n",
-      "487     if (0) std::cerr << \"CudaNdarray dealloc \" << self << \" \" << self->devdata << '\\n';\n",
-      "488     if(Py_REFCNT(self) > 1)\n",
-      "489       printf(\"WARNING:CudaNdarray_dealloc called when there is still active reference to it.\\n\");\n",
-      "490     CudaNdarray_uninit(self);\n",
-      "491     Py_TYPE(self)->tp_free((PyObject*)self);\n",
-      "492     --_outstanding_mallocs[1];\n",
-      "493     if (0)\n",
-      "494     {\n",
-      "495         fprintf(stderr, \"device_malloc_counts: (device) %i (obj) %i\\n\",\n",
-      "496                 _outstanding_mallocs[0],\n",
-      "497                 _outstanding_mallocs[1]);\n",
-      "498     }\n",
-      "499 }\n",
-      "500 \n",
-      "501 static PyObject *\n",
-      "502 CudaNdarray_new(PyTypeObject *type, PyObject *args, PyObject *kwds)\n",
-      "503 {\n",
-      "504     CudaNdarray *self;\n",
-      "505 \n",
-      "506     self = (CudaNdarray *)type->tp_alloc(type, 0);\n",
-      "507     if (self != NULL)\n",
-      "508     {\n",
-      "509         CudaNdarray_null_init(self);\n",
-      "510         ++_outstanding_mallocs[1];\n",
-      "511     }\n",
-      "512     return (PyObject *)self;\n",
-      "513 }\n",
-      "514 static int\n",
-      "515 CudaNdarray_init(CudaNdarray *self, PyObject *args, PyObject *kwds)\n",
-      "516 {\n",
-      "517     PyObject *arr=NULL;\n",
-      "518 \n",
-      "519     if (! PyArg_ParseTuple(args, \"O\", &arr))\n",
-      "520         return -1;\n",
-      "521     if (! PyArray_Check(arr))\n",
-      "522     {\n",
-      "523         PyErr_SetString(PyExc_TypeError, \"PyArray arg required\");\n",
-      "524         return -1;\n",
-      "525     }\n",
-      "526     int rval = CudaNdarray_CopyFromArray(self, (PyArrayObject*)arr);\n",
-      "527     return rval;\n",
-      "528 }\n",
-      "529 static PyMemberDef CudaNdarray_members[] =\n",
-      "530 {\n",
-      "531     /*\n",
-      "532     {\"first\", T_OBJECT_EX, offsetof(CudaNdarray, first), 0,\n",
-      "533      \"first name\"},\n",
-      "534     {\"last\", T_OBJECT_EX, offsetof(CudaNdarray, last), 0,\n",
-      "535      \"last name\"},\n",
-      "536     {\"number\", T_INT, offsetof(CudaNdarray, number), 0,\n",
-      "537      \"noddy number\"},\n",
-      "538      */\n",
-      "539     {NULL}  /* Sentinel */\n",
-      "540 };\n",
-      "541 \n",
-      "542 PyObject * CudaNdarray_CreateArrayObj(CudaNdarray * self, PyObject *args)\n",
-      "543 {\n",
-      "544     PyObject * dtype = NULL;\n",
-      "545     if (args && !PyArg_ParseTuple(args, \"|O\", &dtype))\n",
-      "546         return NULL;\n",
-      "547     if (dtype) {\n",
-      "548         PyArray_Descr* dtype2;\n",
-      "549         // PyArray_DescrConverter try to convert anything to a PyArray_Descr.\n",
-      "550         if(!PyArray_DescrConverter(dtype, &dtype2))\n",
-      "551         {\n",
-      "552             PyObject * str = PyObject_Repr(dtype);\n",
-      "553             PyErr_Format(PyExc_TypeError,\n",
-      "554                          \"CudaNdarray dtype parameter not understood: %s\",\n",
-      "555                          PyString_AsString(str)\n",
-      "556                          );\n",
-      "557             Py_CLEAR(str);\n",
-      "558             return NULL;\n",
-      "559         }\n",
-      "560         int typeNum = dtype2->type_num;\n",
-      "561         Py_DECREF(dtype2);\n",
-      "562         if (typeNum != NPY_FLOAT32)\n",
-      "563         {\n",
-      "564             PyObject * str = PyObject_Repr(dtype);\n",
-      "565             PyErr_Format(PyExc_TypeError,\n",
-      "566                          \"CudaNdarray support only support float32 dtype, provided: %d\",\n",
-      "567                          typeNum\n",
-      "568                          );\n",
-      "569             Py_CLEAR(str);\n",
-      "570             return NULL;\n",
-      "571         }\n",
-      "572     }\n",
-      "573 \n",
-      "574     int verbose = 0;\n",
-      "575     if(self->nd>=0 && CudaNdarray_SIZE(self)==0){\n",
-      "576         npy_intp * npydims = (npy_intp*)malloc(self->nd * sizeof(npy_intp));\n",
-      "577         assert (npydims);\n",
-      "578         for (int i = 0; i < self->nd; ++i) npydims[i] = (npy_intp)(CudaNdarray_HOST_DIMS(self)[i]);\n",
-      "579         PyObject * rval = PyArray_SimpleNew(self->nd, npydims, REAL_TYPENUM);\n",
-      "580         free(npydims);\n",
-      "581         if (!rval){\n",
-      "582             return NULL;\n",
-      "583         }\n",
-      "584         assert (PyArray_ITEMSIZE((PyArrayObject *)rval) == sizeof(real));\n",
-      "585         return rval;\n",
-      "586     }\n",
-      "587     if ((self->nd < 0) || (self->devdata == 0))\n",
-      "588     {\n",
-      "589         PyErr_SetString(PyExc_ValueError, \"can't copy from un-initialized CudaNdarray\");\n",
-      "590         return NULL;\n",
-      "591     }\n",
-      "592     CudaNdarray * contiguous_self = NULL;\n",
-      "593     if (CudaNdarray_is_c_contiguous(self))\n",
-      "594     {\n",
-      "595         contiguous_self = self;\n",
-      "596         Py_INCREF(contiguous_self);\n",
-      "597         if (verbose) std::cerr << \"CreateArrayObj already contiguous\" << contiguous_self << '\\n';\n",
-      "598     }\n",
-      "599     else\n",
-      "600     {\n",
-      "601         contiguous_self = (CudaNdarray*)CudaNdarray_Copy(self);\n",
-      "602         if (verbose) std::cerr << \"CreateArrayObj created contiguous\" << contiguous_self << '\\n';\n",
-      "603     }\n",
-      "604     if (!contiguous_self)\n",
-      "605     {\n",
-      "606         return NULL;\n",
-      "607     }\n",
-      "608 \n",
-      "609     npy_intp * npydims = (npy_intp*)malloc(self->nd * sizeof(npy_intp));\n",
-      "610     assert (npydims);\n",
-      "611     for (int i = 0; i < self->nd; ++i)\n",
-      "612         npydims[i] = (npy_intp)(CudaNdarray_HOST_DIMS(self)[i]);\n",
-      "613     PyArrayObject * rval = (PyArrayObject *) PyArray_SimpleNew(self->nd,\n",
-      "614                                                                npydims,\n",
-      "615                                                                REAL_TYPENUM);\n",
-      "616     free(npydims);\n",
-      "617     if (!rval)\n",
-      "618     {\n",
-      "619         Py_DECREF(contiguous_self);\n",
-      "620         return NULL;\n",
-      "621     }\n",
-      "622 \n",
-      "623     assert (PyArray_ITEMSIZE(rval) == sizeof(real));\n",
-      "624 \n",
-      "625     npy_intp rval_size = PyArray_SIZE(rval);\n",
-      "626     void *rval_data = PyArray_DATA(rval);\n",
-      "627     cudaError_t err;\n",
-      "628     CNDA_BEGIN_ALLOW_THREADS;\n",
-      "629 \n",
-      "630     err = cudaMemcpy(rval_data, contiguous_self->devdata,\n",
-      "631                      rval_size * sizeof(real),\n",
-      "632                      cudaMemcpyDeviceToHost\n",
-      "633                      );\n",
-      "634     //CNDA_THREAD_SYNC;  // unneeded because cudaMemcpy is blocking anyway\n",
-      "635     CNDA_END_ALLOW_THREADS;\n",
-      "636 \n",
-      "637     if (cudaSuccess != err)\n",
-      "638     {\n",
-      "639         PyErr_Format(PyExc_RuntimeError, \"error (%s)copying data to host\",\n",
-      "640                      cudaGetErrorString(err));\n",
-      "641         Py_DECREF(rval);\n",
-      "642         rval = NULL;\n",
-      "643     }\n",
-      "644 \n",
-      "645     Py_DECREF(contiguous_self);\n",
-      "646     return (PyObject *)rval;\n",
-      "647 }\n",
-      "648 \n",
-      "649 // TODO-- we have two functions here, ZEROS and Zeros.\n",
-      "650 // ZEROS is meant to be called just from C code (you don't need to pass it PyObject * s)\n",
-      "651 // but this naming is very weird, makes it look like a macro\n",
-      "652 // we should figure out the correct convention and change to that\n",
-      "653 PyObject* CudaNdarray_ZEROS(int n, int * dims)\n",
-      "654 {\n",
-      "655 \n",
-      "656     size_t total_elements = 1;\n",
-      "657 \n",
-      "658     for(size_t i=0;i<n;i++){\n",
-      "659         // Detect overflow on unsigned integer\n",
-      "660         if (dims[i] != 0 && total_elements > (SIZE_MAX / dims[i])) {\n",
-      "661             PyErr_Format(PyExc_RuntimeError,\n",
-      "662                          \"Can't store in size_t for the bytes requested %llu * %llu\",\n",
-      "663                          (unsigned long long)total_elements,\n",
-      "664                          (unsigned long long)dims[i]);\n",
-      "665             return NULL;\n",
-      "666         }\n",
-      "667         total_elements*=dims[i];\n",
-      "668     }\n",
-      "669 \n",
-      "670     // total_elements now contains the size of the array, in reals\n",
-      "671     if (total_elements > (SIZE_MAX / sizeof(real))){\n",
-      "672         PyErr_Format(PyExc_RuntimeError,\n",
-      "673                      \"Can't store in size_t for the bytes requested %llu * 4\",\n",
-      "674                      (unsigned long long)total_elements);\n",
-      "675         return NULL;\n",
-      "676     }\n",
-      "677     size_t total_size = total_elements * sizeof(real);\n",
-      "678 \n",
-      "679     CudaNdarray* rval = (CudaNdarray*)CudaNdarray_New();\n",
-      "680     if (!rval)\n",
-      "681     {\n",
-      "682         PyErr_SetString(PyExc_RuntimeError, \"CudaNdarray_ZEROS: call to New failed\");\n",
-      "683         return NULL;\n",
-      "684     }\n",
-      "685 \n",
-      "686     if (CudaNdarray_alloc_contiguous(rval, n, dims))\n",
-      "687     {\n",
-      "688         PyErr_SetString(PyExc_RuntimeError, \"CudaNdarray_ZEROS: allocation failed.\");\n",
-      "689         Py_DECREF(rval);\n",
-      "690         return NULL;\n",
-      "691     }\n",
-      "692 \n",
-      "693     // Fill with zeros\n",
-      "694     //fprintf(stdout, \"Sizeof: %d\\n\", total_size);\n",
-      "695     if (cudaSuccess != cudaMemset(rval->devdata, 0, total_size))\n",
-      "696     {\n",
-      "697         PyErr_Format(PyExc_MemoryError,\n",
-      "698                      \"CudaNdarray_ZEROS: Error memsetting %llu bytes of device memory.\",\n",
-      "699                      (unsigned long long)total_size);\n",
-      "700         Py_DECREF(rval);\n",
-      "701         return NULL;\n",
-      "702     }\n",
-      "703 \n",
-      "704     if (cnda_copy_structure_to_device(rval))\n",
-      "705     {\n",
-      "706         PyErr_SetString(PyExc_RuntimeError, \"CudaNdarray_ZEROS: syncing structure to device failed\");\n",
-      "707         Py_DECREF(rval);\n",
-      "708         return NULL;\n",
-      "709     }\n",
-      "710     return (PyObject*) rval;\n",
-      "711 }\n",
-      "712 \n",
-      "713 // declared as a static method (hence 1st parameter is not used)\n",
-      "714 // Based on _Copy and _dimshuffle\n",
-      "715 PyObject* CudaNdarray_Zeros(PyObject* _unused, PyObject* shape)\n",
-      "716 {\n",
-      "717     if(!shape)\n",
-      "718     {\n",
-      "719         PyErr_SetString(PyExc_TypeError, \"CudaNdarray_Zeros: function takes at least 1 argument (0 given)\");\n",
-      "720         return NULL;\n",
-      "721     }\n",
-      "722     if(!PySequence_Check(shape))\n",
-      "723     {\n",
-      "724         PyErr_SetString(PyExc_TypeError, \"shape argument must be a sequence\");\n",
-      "725         return NULL;\n",
-      "726     }\n",
-      "727 \n",
-      "728     int shplen = PySequence_Length(shape);\n",
-      "729 \n",
-      "730     if (shplen == 0)\n",
-      "731     {\n",
-      "732         return CudaNdarray_ZEROS(0, NULL);\n",
-      "733     }\n",
-      "734 \n",
-      "735     int* newdims = (int *)malloc(sizeof(int) * shplen);\n",
-      "736 \n",
-      "737     if (!newdims)\n",
-      "738     {\n",
-      "739         PyErr_SetString(PyExc_MemoryError,\n",
-      "740             \"CudaNdarray_Zeros: Failed to allocate temporary space\");\n",
-      "741         return NULL;\n",
-      "742     }\n",
-      "743 \n",
-      "744     // start from the end to compute strides\n",
-      "745     for (int i = shplen-1; i >= 0; --i)\n",
-      "746     {\n",
-      "747         PyObject* shp_el_obj = PySequence_GetItem(shape, i);\n",
-      "748         if(shp_el_obj == NULL)\n",
-      "749         {\n",
-      "750             // shouldn't happen since we checked length before...\n",
-      "751             PyErr_SetString(PyExc_RuntimeError, \"CudaNdarray_Zeros: Index out of bound in sequence\");\n",
-      "752             free(newdims);\n",
-      "753             return NULL;\n",
-      "754         }\n",
-      "755 \n",
-      "756         int shp_el = PyInt_AsLong(shp_el_obj);\n",
-      "757         Py_DECREF(shp_el_obj);\n",
-      "758 \n",
-      "759         if (shp_el < 0)\n",
-      "760         {\n",
-      "761             PyErr_SetString(PyExc_ValueError, \"CudaNdarray_Zeros: shape must contain only non-negative values for size of a dimension\");\n",
-      "762             free(newdims);\n",
-      "763             return NULL;\n",
-      "764         }\n",
-      "765 \n",
-      "766         newdims[i] = shp_el;\n",
-      "767     }\n",
-      "768 \n",
-      "769     PyObject* rval = CudaNdarray_ZEROS(shplen,newdims);\n",
-      "770 \n",
-      "771     free(newdims);\n",
-      "772 \n",
-      "773     return (PyObject*)rval;\n",
-      "774 }\n",
-      "775 \n",
-      "776 \n",
-      "777 \n",
-      "778 \n",
-      "779 \n",
-      "780 PyObject * CudaNdarray_Copy(const CudaNdarray * self)\n",
-      "781 {\n",
-      "782     PyObject * rval = CudaNdarray_New();\n",
-      "783     if ((!rval) || (-1 == self->nd))\n",
-      "784     {\n",
-      "785         return rval;\n",
-      "786     }\n",
-      "787     if (CudaNdarray_alloc_contiguous((CudaNdarray*)rval, self->nd, CudaNdarray_HOST_DIMS(self)))\n",
-      "788     {\n",
-      "789         Py_DECREF(rval);\n",
-      "790         return NULL;\n",
-      "791     }\n",
-      "792     if (CudaNdarray_CopyFromCudaNdarray((CudaNdarray*)rval, self))\n",
-      "793     {\n",
-      "794         Py_DECREF(rval);\n",
-      "795         return NULL;\n",
-      "796     }\n",
-      "797     return rval;\n",
-      "798 }\n",
-      "799 PyObject * CudaNdarray_DeepCopy(CudaNdarray * self, PyObject * memo)\n",
-      "800 {\n",
-      "801     assert(PyDict_Check(memo));\n",
-      "802     PyObject * selfkey = PyInt_FromLong((long)self);\n",
-      "803     assert(selfkey);\n",
-      "804     if (PyDict_Contains(memo, selfkey))\n",
-      "805     {\n",
-      "806         PyObject * rval = PyDict_GetItem(memo, selfkey);\n",
-      "807         Py_DECREF(selfkey);\n",
-      "808         Py_XINCREF(rval);\n",
-      "809         return rval;\n",
-      "810     }\n",
-      "811     else\n",
-      "812     {\n",
-      "813         PyObject * rval = CudaNdarray_Copy(self);\n",
-      "814         if (0) std::cerr << \"DeepCopy created \" << rval << \" devdata \" << ((CudaNdarray*)rval)->devdata << \"\\n\";\n",
-      "815         if (NULL == rval)\n",
-      "816         {\n",
-      "817             Py_DECREF(selfkey);\n",
-      "818             return NULL;\n",
-      "819         }\n",
-      "820         if (PyDict_SetItem(memo, selfkey, rval))\n",
-      "821         {\n",
-      "822             Py_DECREF(rval);\n",
-      "823             Py_DECREF(selfkey);\n",
-      "824             return NULL;\n",
-      "825         }\n",
-      "826         Py_DECREF(selfkey);\n",
-      "827         return rval;\n",
-      "828     }\n",
-      "829 }\n",
-      "830 PyObject * CudaNdarray_ReduceSum(CudaNdarray * self, PyObject * py_reduce_mask)\n",
-      "831 {\n",
-      "832     if (!PySequence_Check(py_reduce_mask))\n",
-      "833     {\n",
-      "834         PyErr_SetString(PyExc_TypeError, \"reduce_mask must be sequence of ints\");\n",
-      "835         return NULL;\n",
-      "836     }\n",
-      "837     int len = PySequence_Length(py_reduce_mask);\n",
-      "838     if (len != self->nd)\n",
-      "839     {\n",
-      "840         PyErr_SetString(PyExc_TypeError, \"length of reduce_mask must match self->nd\");\n",
-      "841         return NULL;\n",
-      "842     }\n",
-      "843     CudaNdarray * self_sum = (CudaNdarray*)CudaNdarray_New();\n",
-      "844     if (!self_sum)\n",
-      "845     {\n",
-      "846         return NULL;\n",
-      "847     }\n",
-      "848     //TODO: allocate a fixed size dimshuffle_pattern_cache on the stack,\n",
-      "849     //      and use it if it is big enough.\n",
-      "850     int * dimshuffle_pattern = (int*)malloc(len * 2 * sizeof(int));\n",
-      "851     int * sum_dims = dimshuffle_pattern + len;\n",
-      "852     int n_remaining_dims = 0;\n",
-      "853     if (!dimshuffle_pattern)\n",
-      "854     {\n",
-      "855         Py_DECREF(self_sum);\n",
-      "856         PyErr_SetString(PyExc_MemoryError, \"failed to alloc internal storage\");\n",
-      "857         return NULL;\n",
-      "858     }\n",
-      "859     for (int i = 0; i < len; ++i)\n",
-      "860     {\n",
-      "861         PyObject *o_i = PySequence_GetItem(py_reduce_mask, i);\n",
-      "862         int o_i_int = PyInt_AsLong(o_i);\n",
-      "863         Py_XDECREF(o_i);\n",
-      "864         if (PyErr_Occurred())\n",
-      "865         {\n",
-      "866             Py_DECREF(self_sum);\n",
-      "867             free(dimshuffle_pattern);\n",
-      "868             return NULL;\n",
-      "869         }\n",
-      "870         if (o_i_int) // this is a dimension over which we are reducing\n",
-      "871         {\n",
-      "872             sum_dims[i] = 1;\n",
-      "873         }\n",
-      "874         else\n",
-      "875         {\n",
-      "876             sum_dims[i] = CudaNdarray_HOST_DIMS(self)[i];\n",
-      "877             dimshuffle_pattern[n_remaining_dims++] = i;\n",
-      "878         }\n",
-      "879     }\n",
-      "880     if (0   || CudaNdarray_alloc_contiguous(self_sum, len, sum_dims)\n",
-      "881             || CudaNdarray_reduce_sum(self_sum, self)\n",
-      "882             || CudaNdarray_dimshuffle(self_sum, n_remaining_dims, dimshuffle_pattern))\n",
-      "883     {\n",
-      "884         Py_DECREF(self_sum);\n",
-      "885         free(dimshuffle_pattern);\n",
-      "886         return NULL;\n",
-      "887     }\n",
-      "888     free(dimshuffle_pattern);\n",
-      "889     return (PyObject*)self_sum;\n",
-      "890 }\n",
-      "891 \n",
-      "892 // Reshape self to the new shape gived by the tuple shape.\n",
-      "893 //\n",
-      "894 // If self is c contiguous, it return a view. Otherwise it always do a copy.\n",
-      "895 // TODO: make it return a view when the strides allow it even if it is not\n",
-      "896 //       c contiguous\n",
-      "897 PyObject * CudaNdarray_Reshape(CudaNdarray * self, PyObject * shape)\n",
-      "898 {\n",
-      "899     if(!CudaNdarray_is_c_contiguous(self))\n",
-      "900     {\n",
-      "901         // allocate new space\n",
-      "902         //TODO: test to see if we can re-use old one and take a new param to\n",
-      "903         //  use this\n",
-      "904         CudaNdarray* rval = (CudaNdarray*) CudaNdarray_Copy(self);\n",
-      "905         if (!rval)\n",
-      "906         {\n",
-      "907             return NULL;\n",
-      "908         }\n",
-      "909 \n",
-      "910         CudaNdarray* ret = (CudaNdarray*) CudaNdarray_Reshape(rval, shape);\n",
-      "911         Py_XDECREF(rval);\n",
-      "912         return (PyObject*)ret;\n",
-      "913     }\n",
-      "914 \n",
-      "915     // check shape tuple\n",
-      "916     unsigned int rval_nd;\n",
-      "917     unsigned int * rval_dims;\n",
-      "918     size_t rval_size = 1;\n",
-      "919 \n",
-      "920     if (PyTuple_Check(shape)){\n",
-      "921         // copy shape to integer array\n",
-      "922         rval_nd = PyTuple_Size(shape);\n",
-      "923     }else if (PyInt_Check(shape)){\n",
-      "924         rval_nd = 1;\n",
-      "925     }else{\n",
-      "926         PyErr_SetString(PyExc_TypeError, \"shape must be tuple of integers or an integer\");\n",
-      "927         return NULL;\n",
-      "928     }\n",
-      "929     rval_dims = (unsigned int*)malloc(rval_nd * sizeof(int));\n",
-      "930 \n",
-      "931     if(PyTuple_Check(shape)){\n",
-      "932         for (int i = 0; i < rval_nd; ++i)\n",
-      "933         {\n",
-      "934             rval_dims[i] = PyInt_AsLong(PyTuple_GetItem(shape, i)); //GetItem returns borrowed reference\n",
-      "935             if (PyErr_Occurred()) //error in AsLong\n",
-      "936             {\n",
-      "937                 free(rval_dims);\n",
-      "938                 return NULL;\n",
-      "939             }\n",
-      "940             if(rval_dims[i]<0){\n",
-      "941                 PyErr_Format(PyExc_ValueError, \"Reshape has invalid dimension %i (must be >=0)\",rval_dims[i]);\n",
-      "942                 free(rval_dims);\n",
-      "943                 return NULL;\n",
-      "944             }\n",
-      "945             rval_size = rval_size * rval_dims[i];\n",
-      "946         }\n",
-      "947     }else{\n",
-      "948         rval_size = PyInt_AsLong(shape);\n",
-      "949         rval_dims[0] = rval_size;\n",
-      "950     }\n",
-      "951     // calculate new size, assert same as old size\n",
-      "952     if (rval_size != CudaNdarray_SIZE(self))\n",
-      "953     {\n",
-      "954         PyErr_Format(PyExc_ValueError, \"size must remain unchanged, changed from %lld to %lld\", CudaNdarray_SIZE(self), rval_size);\n",
-      "955         free(rval_dims);\n",
-      "956         return NULL;\n",
-      "957     }\n",
-      "958     if (rval_size==0)\n",
-      "959     {\n",
-      "960         PyObject * rval = CudaNdarray_NewDims(rval_nd, rval_dims);\n",
-      "961         free(rval_dims);\n",
-      "962         return rval;\n",
-      "963     }\n",
-      "964 \n",
-      "965     //return a view, not a copy\n",
-      "966     //we can do this as we checked self is c_contiguous\n",
-      "967     CudaNdarray * rval = (CudaNdarray * )CudaNdarray_New(rval_nd);\n",
-      "968 \n",
-      "969     if (!rval || 0 != rval->data_allocated\n",
-      "970         ||CudaNdarray_set_device_data(rval, CudaNdarray_DEV_DATA(self), self))\n",
-      "971     {\n",
-      "972         Py_XDECREF(rval);\n",
-      "973         free(rval_dims);\n",
-      "974         return NULL;\n",
-      "975     }\n",
-      "976     //set dim and stride\n",
-      "977     int size = 1;\n",
-      "978     for (int i = rval_nd-1; i >= 0; --i)\n",
-      "979     {\n",
-      "980         CudaNdarray_set_stride(rval, i, (rval_dims[i] == 1) ? 0 : size);\n",
-      "981         CudaNdarray_set_dim(rval, i, rval_dims[i]);\n",
-      "982         size = size * rval_dims[i];\n",
-      "983     }\n",
-      "984     free(rval_dims);\n",
-      "985     return (PyObject*)rval;\n",
-      "986 }\n",
-      "987 \n",
-      "988 PyObject * CudaNdarray_View(const CudaNdarray * self)\n",
-      "989 {\n",
-      "990     CudaNdarray * rval = (CudaNdarray*)CudaNdarray_New(self->nd);\n",
-      "991     if (!rval || CudaNdarray_set_device_data(rval, CudaNdarray_DEV_DATA(self), self))\n",
-      "992     {\n",
-      "993         Py_XDECREF(rval);\n",
-      "994         rval = NULL;\n",
-      "995     }\n",
-      "996     else\n",
-      "997     {\n",
-      "998         for (int i = 0; i < self->nd; ++i)\n",
-      "999         {\n",
-      "1000             CudaNdarray_set_dim(rval, i, CudaNdarray_HOST_DIMS(self)[i]);\n",
-      "1001             CudaNdarray_set_stride(rval, i, CudaNdarray_HOST_STRIDES(self)[i]);\n",
-      "1002         }\n",
-      "1003     }\n",
-      "1004     return (PyObject*)rval;\n",
-      "1005 }\n",
-      "1006 \n",
-      "1007 /*\n",
-      "1008  * d0,... are the output dims\n",
-      "1009  * indices are a list of index to operate on\n",
-      "1010  *         They are int32 viewed as float32.\n",
-      "1011  * a is the output\n",
-      "1012  * b is the input\n",
-      "1013  * dB0, the source leading dimensions size\n",
-      "1014  */\n",
-      "1015 template <int operator_num>\n",
-      "1016 __global__ void k_take_3(const int d0, const int d1, const int d2,\n",
-      "1017                          const npy_int64* indices,\n",
-      "1018                          float* a,\n",
-      "1019                          const int sA0, const int sA1, const int sA2,\n",
-      "1020                          const float* b, const int dB0,\n",
-      "1021                          const int sB0, const int sB1, const int sB2,\n",
-      "1022                          int* err){\n",
-      "1023     for (int i0 = blockIdx.x; i0 < d0; i0 += gridDim.x){\n",
-      "1024         npy_int64 idx = indices[i0];\n",
-      "1025         if (idx<0)\n",
-      "1026             idx += dB0; // To allow negative indexing.\n",
-      "1027         if ((idx < 0) || (idx >= dB0)){\n",
-      "1028             // Any value other the 0 probably work. But to be more safe, I want\n",
-      "1029             // to change all bits to prevent problem with concurrent write that\n",
-      "1030             // could cross cache line. But this should not happen with the\n",
-      "1031             // current code and driver.\n",
-      "1032             *err = 0xFFFF;\n",
-      "1033             continue;\n",
-      "1034         }\n",
-      "1035         for (int i1 = threadIdx.x; i1 < d1; i1 += blockDim.x){\n",
-      "1036             for (int i2 = threadIdx.y; i2 < d2; i2 += blockDim.y){\n",
-      "1037                 int a_idx = i0*sA0 + i1*sA1 + i2*sA2;\n",
-      "1038                 int b_idx = idx*sB0 + i1*sB1 + i2*sB2;\n",
-      "1039                 a[a_idx] = b[b_idx];\n",
-      "1040             }\n",
-      "1041         }\n",
-      "1042     }\n",
-      "1043 }\n",
-      "1044 \n",
-      "1045 // We try to be similar to the PyArray_TakeFrom function\n",
-      "1046 //http://docs.scipy.org/doc/numpy/reference/c-api.array.html\n",
-      "1047 //TODO: support other clip mode then raise(clip, wrap)\n",
-      "1048 //self is the input that we copy data from.\n",
-      "1049 //The indices that we receive MUST be an CudaNdarray(float32)\n",
-      "1050 //    that is in fact a view to int64 indices\n",
-      "1051 PyObject*\n",
-      "1052 CudaNdarray_TakeFrom(CudaNdarray * self, PyObject *args){\n",
-      "1053     int verbose = 0;\n",
-      "1054     PyObject * indices_obj = NULL;\n",
-      "1055     //int axis; Default None, that mean the flattened array.\n",
-      "1056     PyObject * axis_obj = Py_None;\n",
-      "1057     PyObject * out_obj = Py_None;\n",
-      "1058     PyObject * clipmode_obj = NULL;\n",
-      "1059     int max_threads = 1; // max threads per blocks\n",
-      "1060 \n",
-      "1061     if (! PyArg_ParseTuple(args, \"O|OOOi\", &indices_obj, &axis_obj,\n",
-      "1062                            &out_obj, &clipmode_obj, &max_threads))\n",
-      "1063         return NULL;\n",
-      "1064 \n",
-      "1065     //Check argument indices\n",
-      "1066     //TODO: if not a numpy.ndarray, convert to numpy.ndarray\n",
-      "1067     //TODO: If a CudaNdarray, accept it and suppose the data is int32? is float32 number of int?\n",
-      "1068     //TODO: Support ndarray of other dtype then int32\n",
-      "1069     //TODO: support list of indices that are not c_contiguous\n",
-      "1070     CudaNdarray * indices = NULL;\n",
-      "1071     if (CudaNdarray_Check(indices_obj)) {\n",
-      "1072         if (verbose) printf(\"cudandarray indices\\n\");\n",
-      "1073         indices = (CudaNdarray*) indices_obj;\n",
-      "1074         Py_INCREF(indices);\n",
-      "1075     } else if (PyArray_Check(indices_obj)) {\n",
-      "1076         if (verbose) printf(\"ndarray indices\\n\");\n",
-      "1077         if (PyArray_TYPE((PyArrayObject *)indices_obj) != NPY_INT64) {\n",
-      "1078             PyErr_SetString(PyExc_TypeError,\n",
-      "1079                             \"CudaNdarray_TakeFrom: need a ndarray for indices\"\n",
-      "1080                             \" with dtype int64\");\n",
-      "1081             return NULL;\n",
-      "1082         }\n",
-      "1083         if (PyArray_NDIM(((PyArrayObject*)indices_obj)) != 1) {\n",
-      "1084             PyErr_SetString(PyExc_TypeError,\n",
-      "1085                             \"CudaNdarray_TakeFrom: need a CudaNdarray of\"\n",
-      "1086                             \" indices with only 1 dimensions\");\n",
-      "1087             return NULL;\n",
-      "1088         }\n",
-      "1089         // We need indices_obj to be contiguous, in order to take a view\n",
-      "1090         // with a different dtype.\n",
-      "1091         if (!PyArray_IS_C_CONTIGUOUS((PyArrayObject*) indices_obj)) {\n",
-      "1092             PyObject* indices_obj_contig = PyArray_NewCopy((PyArrayObject*) indices_obj, NPY_CORDER);\n",
-      "1093             if (!indices_obj_contig)\n",
-      "1094                 return NULL;\n",
-      "1095             indices_obj = indices_obj_contig;\n",
-      "1096         } else {\n",
-      "1097             // Keep the refcount consistent\n",
-      "1098             Py_INCREF(indices_obj);\n",
-      "1099         }\n",
-      "1100         PyArray_Descr* float32_descr = PyArray_DescrFromType(NPY_FLOAT32);\n",
-      "1101         PyObject * indices_float32 = NULL;\n",
-      "1102         indices_float32 = PyArray_View((PyArrayObject*)indices_obj,\n",
-      "1103                                                   float32_descr, NULL);\n",
-      "1104         if (verbose) printf(\"ndarray indices\\n\");\n",
-      "1105         if (!indices_float32) {\n",
-      "1106             Py_DECREF(indices_obj);\n",
-      "1107             return NULL;\n",
-      "1108         }\n",
-      "1109 \n",
-      "1110         indices = (CudaNdarray*) CudaNdarray_New();\n",
-      "1111         if (verbose) printf(\"\\nndarray after new\\n\");\n",
-      "1112         if (! indices){\n",
-      "1113             Py_DECREF(indices_obj);\n",
-      "1114             Py_DECREF(indices_float32);\n",
-      "1115             return NULL;\n",
-      "1116         }\n",
-      "1117         if (CudaNdarray_CopyFromArray(indices,\n",
-      "1118                                       (PyArrayObject *)indices_float32)){\n",
-      "1119             Py_DECREF(indices_obj);\n",
-      "1120             Py_DECREF(indices_float32);\n",
-      "1121             return NULL;\n",
-      "1122         }\n",
-      "1123         Py_DECREF(indices_obj);\n",
-      "1124         Py_DECREF(indices_float32);\n",
-      "1125     } else {\n",
-      "1126         PyObject* py_s = PyObject_Str(indices_obj);\n",
-      "1127         const char* s = PyString_AsString(py_s);\n",
-      "1128         Py_DECREF(py_s);\n",
-      "1129         PyErr_Format(PyExc_TypeError,\n",
-      "1130                      \"CudaNdarray_TakeFrom: need an ndarray of int64 or a\"\n",
-      "1131                      \" CudaNdarray(float32) that is a view from int64 data\"\n",
-      "1132                      \" for indices. Got %s\", s);\n",
-      "1133         return NULL;\n",
-      "1134     }\n",
-      "1135 \n",
-      "1136     if (verbose) {\n",
-      "1137         printf(\"indices used on the gpu\\n\");\n",
-      "1138         fprint_CudaNdarray(stdout, indices);\n",
-      "1139         PyObject * used_indices = CudaNdarray_CreateArrayObj(indices);\n",
-      "1140         PyObject_Print(used_indices, stdout, 0);\n",
-      "1141         Py_DECREF(used_indices);\n",
-      "1142     }\n",
-      "1143     if (verbose) printf(\"after print of object\\n\");\n",
-      "1144     if(!CudaNdarray_is_c_contiguous(indices) != 0) {\n",
-      "1145         PyErr_SetString(PyExc_NotImplementedError,\n",
-      "1146                         \"CudaNdarray_TakeFrom: The indices must be contiguous in memory.\");\n",
-      "1147         Py_DECREF(indices);\n",
-      "1148         return NULL;\n",
-      "1149     }\n",
-      "1150     int nb_indices = CudaNdarray_SIZE((CudaNdarray *)indices) / 2;// int64 are 8 bytes, float32 are 4 bytes\n",
-      "1151 \n",
-      "1152     //Check argument axis\n",
-      "1153     //TODO: implement the default and other axis\n",
-      "1154     long axis = PyInt_AsLong(axis_obj);\n",
-      "1155 \n",
-      "1156     if (axis != 0) {\n",
-      "1157         PyErr_Format(PyExc_NotImplementedError,\n",
-      "1158                      \"CudaNdarray_TakeFrom: only axis=0 is currently supported.\"\n",
-      "1159                      \" Got %ld.\", axis);\n",
-      "1160         Py_DECREF(indices);\n",
-      "1161         return NULL;\n",
-      "1162     }\n",
-      "1163 \n",
-      "1164     //Check argument out_obj\n",
-      "1165     CudaNdarray * out = NULL;\n",
-      "1166     if (out_obj && CudaNdarray_Check(out_obj))\n",
-      "1167         out = (CudaNdarray*) out_obj;\n",
-      "1168     if (out && (out->nd != self->nd ||\n",
-      "1169                 CudaNdarray_HOST_DIMS(out)[0] != nb_indices))\n",
-      "1170         out = NULL;\n",
-      "1171     int * dims = (int *)malloc(sizeof(int) * self->nd);\n",
-      "1172     dims[0] = nb_indices;\n",
-      "1173 \n",
-      "1174     for (int i=1 ; i<self->nd ; i++) {\n",
-      "1175         dims[i] = CudaNdarray_HOST_DIMS(self)[i];\n",
-      "1176         if (out && CudaNdarray_HOST_DIMS(out)[i] != dims[i]) {\n",
-      "1177             out = NULL;\n",
-      "1178         }\n",
-      "1179     }\n",
-      "1180     if (!out) {\n",
-      "1181         out = (CudaNdarray*)CudaNdarray_New();\n",
-      "1182         if (!out){\n",
-      "1183             Py_DECREF(indices);\n",
-      "1184             free(dims);\n",
-      "1185             return NULL;\n",
-      "1186         }\n",
-      "1187         if (CudaNdarray_alloc_contiguous(out, self->nd, dims)) {\n",
-      "1188             Py_DECREF(out);\n",
-      "1189             Py_DECREF(indices);\n",
-      "1190             free(dims);\n",
-      "1191             return NULL;\n",
-      "1192         }\n",
-      "1193     }else {\n",
-      "1194         Py_INCREF(out);\n",
-      "1195     }\n",
-      "1196 \n",
-      "1197     //Check argument clipmode\n",
-      "1198     if (clipmode_obj) {\n",
-      "1199         char * clipmode = PyString_AsString(clipmode_obj);\n",
-      "1200         if (! clipmode){\n",
-      "1201             Py_DECREF(indices);\n",
-      "1202             Py_DECREF(out);\n",
-      "1203             free(dims);\n",
-      "1204             return NULL;\n",
-      "1205         }\n",
-      "1206         if (strcmp(clipmode, \"raise\") != 0) {\n",
-      "1207             PyErr_Format(PyExc_NotImplementedError,\n",
-      "1208                          \"CudaNdarray_TakeFrom: only the raise mode is currently supported. Got '%s'\",\n",
-      "1209                          clipmode);\n",
-      "1210             Py_DECREF(indices);\n",
-      "1211             Py_DECREF(out);\n",
-      "1212             free(dims);\n",
-      "1213             return NULL;\n",
-      "1214         }\n",
-      "1215     }\n",
-      "1216     void (*k3)(const int, const int, const int,\n",
-      "1217                const npy_int64*,\n",
-      "1218                float*, const int, const int, const int,\n",
-      "1219                const float*, const int,\n",
-      "1220                const int, const int, const int,\n",
-      "1221                int*);\n",
-      "1222     k3 = k_take_3<CPY>;\n",
-      "1223 \n",
-      "1224     // Create the memory place that will store the error information.\n",
-      "1225     if(init_err_var() != 0) return NULL;\n",
-      "1226 \n",
-      "1227     dim3 n_blocks(std::min(CudaNdarray_HOST_DIMS(out)[0],65535),1,1);\n",
-      "1228     if(CudaNdarray_HOST_DIMS(out)[0] == 0){\n",
-      "1229         // We take 0 elements, so no need for the rest of the code.\n",
-      "1230         // This speed up that case AND fix crash otherwise.\n",
-      "1231         free(dims);\n",
-      "1232         Py_DECREF(indices);\n",
-      "1233         return (PyObject *)out;\n",
-      "1234     }\n",
-      "1235 \n",
-      "1236     switch (self->nd) {\n",
-      "1237         case 1:\n",
-      "1238             {\n",
-      "1239                 dim3 n_threads(1, 1, 1);\n",
-      "1240                 if (verbose)\n",
-      "1241                     printf(\"cudaGetLastError=%d, nd=%d\"\n",
-      "1242                            \" kernel config: (n_blocks.x=%d, n_blocks.y=%d,\"\n",
-      "1243                            \" n_threads.x=%i, n_threads.y=%i)\\n\",\n",
-      "1244                            cudaGetLastError(), self->nd,\n",
-      "1245                            n_blocks.x, n_blocks.y, n_threads.x, n_threads.y);\n",
-      "1246                 k3<<<n_blocks, n_threads>>>(\n",
-      "1247                         dims[0],\n",
-      "1248                         1,\n",
-      "1249                         1,\n",
-      "1250                         (npy_int64*) CudaNdarray_DEV_DATA(indices),\n",
-      "1251                         CudaNdarray_DEV_DATA(out),\n",
-      "1252                         CudaNdarray_HOST_STRIDES(out)[0], //strides\n",
-      "1253                         1,\n",
-      "1254                         1,\n",
-      "1255                         CudaNdarray_DEV_DATA(self),\n",
-      "1256                         CudaNdarray_HOST_DIMS(self)[0], //For indices check\n",
-      "1257                         CudaNdarray_HOST_STRIDES(self)[0], //strides\n",
-      "1258                         1,\n",
-      "1259                         1,\n",
-      "1260                         err_var);\n",
-      "1261             }\n",
-      "1262             break;\n",
-      "1263         case 2:\n",
-      "1264             {\n",
-      "1265                 dim3 n_threads(std::min(CudaNdarray_HOST_DIMS(out)[1], max_threads), 1, 1);\n",
-      "1266 \n",
-      "1267                 if (verbose)\n",
-      "1268                     printf(\"cudaGetLastError=%d, nd=%d\"\n",
-      "1269                            \" kernel config: (n_blocks.x=%d, n_blocks.y=%d,\"\n",
-      "1270                            \" n_threads.x=%i, n_threads.y=%i)\\n\",\n",
-      "1271                            cudaGetLastError(), self->nd,\n",
-      "1272                            n_blocks.x, n_blocks.y, n_threads.x, n_threads.y);\n",
-      "1273 \n",
-      "1274                 k3<<<n_blocks, n_threads>>>(\n",
-      "1275                         dims[0], //dimensions\n",
-      "1276                         dims[1],\n",
-      "1277                         1,\n",
-      "1278                         (npy_int64*) CudaNdarray_DEV_DATA(indices),\n",
-      "1279                         CudaNdarray_DEV_DATA(out),\n",
-      "1280                         CudaNdarray_HOST_STRIDES(out)[0], //strides\n",
-      "1281                         CudaNdarray_HOST_STRIDES(out)[1],\n",
-      "1282                         1,\n",
-      "1283                         CudaNdarray_DEV_DATA(self),\n",
-      "1284                         CudaNdarray_HOST_DIMS(self)[0], //For indices check\n",
-      "1285                         CudaNdarray_HOST_STRIDES(self)[0], //strides\n",
-      "1286                         CudaNdarray_HOST_STRIDES(self)[1],\n",
-      "1287                         1,\n",
-      "1288                         err_var);\n",
-      "1289             }\n",
-      "1290             break;\n",
-      "1291         case 3:\n",
-      "1292             {\n",
-      "1293                 int ty = std::min(CudaNdarray_HOST_DIMS(out)[2], max_threads);\n",
-      "1294                 int tx = std::min(CudaNdarray_HOST_DIMS(out)[1], max_threads / ty);\n",
-      "1295                 dim3 n_threads(tx, ty, 1);\n",
-      "1296                 if (verbose)\n",
-      "1297                     printf(\"cudaGetLastError=%d, nd=%d\"\n",
-      "1298                            \" kernel config: (n_blocks.x=%d, n_blocks.y=%d,\"\n",
-      "1299                            \" n_threads.x=%i, n_threads.y=%i)\\n\",\n",
-      "1300                            cudaGetLastError(), self->nd,\n",
-      "1301                            n_blocks.x, n_blocks.y, n_threads.x, n_threads.y);\n",
-      "1302                 k3<<<n_blocks, n_threads>>>(\n",
-      "1303                         dims[0], //dimensions\n",
-      "1304                         dims[1],\n",
-      "1305                         dims[2],\n",
-      "1306                         (npy_int64*) CudaNdarray_DEV_DATA(indices),\n",
-      "1307                         CudaNdarray_DEV_DATA(out),\n",
-      "1308                         CudaNdarray_HOST_STRIDES(out)[0], //strides\n",
-      "1309                         CudaNdarray_HOST_STRIDES(out)[1],\n",
-      "1310                         CudaNdarray_HOST_STRIDES(out)[2],\n",
-      "1311                         CudaNdarray_DEV_DATA(self),\n",
-      "1312                         CudaNdarray_HOST_DIMS(self)[0], //For indices check\n",
-      "1313                         CudaNdarray_HOST_STRIDES(self)[0], //strides\n",
-      "1314                         CudaNdarray_HOST_STRIDES(self)[1],\n",
-      "1315                         CudaNdarray_HOST_STRIDES(self)[2],\n",
-      "1316                         err_var);\n",
-      "1317             }\n",
-      "1318             break;\n",
-      "1319     default:\n",
-      "1320         PyErr_SetString(PyExc_NotImplementedError,\n",
-      "1321                         \"CudaNdarray_TakeFrom: only input with 1, 2 or 3\"\n",
-      "1322                         \" dimensions are currently supported\");\n",
-      "1323 \n",
-      "1324     }\n",
-      "1325     free(dims);\n",
-      "1326     CNDA_THREAD_SYNC;\n",
-      "1327     cudaError_t err = cudaGetLastError();\n",
-      "1328     if (cudaSuccess != err) {\n",
-      "1329         PyErr_Format(PyExc_RuntimeError,\n",
-      "1330                      \"Cuda error: %s: %s.\\n\",\n",
-      "1331                      \"CudaNdarray_TakeFrom\",\n",
-      "1332                      cudaGetErrorString(err));\n",
-      "1333         Py_DECREF(indices);\n",
-      "1334         Py_DECREF(out);\n",
-      "1335         return NULL;\n",
-      "1336     }\n",
-      "1337 \n",
-      "1338     int index_err = check_err_var();\n",
-      "1339     Py_DECREF(indices);\n",
-      "1340     if (index_err != 0) {\n",
-      "1341         Py_DECREF(out);\n",
-      "1342         return NULL;\n",
-      "1343     }\n",
-      "1344 \n",
-      "1345     if (verbose) printf(\"TAKE SUCCEDED\\n\");\n",
-      "1346     return (PyObject *)out;\n",
-      "1347 }\n",
-      "1348 \n",
-      "1349 \n",
-      "1350 PyObject * CudaNdarray_SetStride(CudaNdarray * self, PyObject *args)\n",
-      "1351 {\n",
-      "1352     int pos, stride;\n",
-      "1353     if (! PyArg_ParseTuple(args, \"ii\", &pos, &stride))\n",
-      "1354         return NULL;\n",
-      "1355     if ((pos < 0) || (pos >= self->nd))\n",
-      "1356     {\n",
-      "1357         PyErr_Format(PyExc_ValueError, \"position argument out of legal range [0, %i)\", self->nd);\n",
-      "1358         return NULL;\n",
-      "1359     }\n",
-      "1360     CudaNdarray_set_stride(self, pos, stride);\n",
-      "1361     if (cnda_copy_structure_to_device(self))\n",
-      "1362     {\n",
-      "1363         return NULL;\n",
-      "1364     }\n",
-      "1365     Py_INCREF(Py_None);\n",
-      "1366     return Py_None;\n",
-      "1367 }\n",
-      "1368 PyObject * CudaNdarray_SetShapeI(CudaNdarray * self, PyObject *args)\n",
-      "1369 {\n",
-      "1370     int pos, dim;\n",
-      "1371     if (! PyArg_ParseTuple(args, \"ii\", &pos, &dim))\n",
-      "1372         return NULL;\n",
-      "1373     if ((pos < 0) || (pos >= self->nd))\n",
-      "1374     {\n",
-      "1375         PyErr_Format(PyExc_ValueError, \"position argument out of legal range [0, %i)\", self->nd);\n",
-      "1376         return NULL;\n",
-      "1377     }\n",
-      "1378     CudaNdarray_set_dim(self, pos, dim);\n",
-      "1379     if (cnda_copy_structure_to_device(self))\n",
-      "1380     {\n",
-      "1381         return NULL;\n",
-      "1382     }\n",
-      "1383     Py_INCREF(Py_None);\n",
-      "1384     return Py_None;\n",
-      "1385 }\n",
-      "1386 \n",
-      "1387 static PyObject *\n",
-      "1388 CudaNdarray_exp(CudaNdarray* self)\n",
-      "1389 {\n",
-      "1390     CudaNdarray * rval = (CudaNdarray *)CudaNdarray_New();\n",
-      "1391     if ((NULL == rval) || CudaNdarray_alloc_contiguous(rval, self->nd, CudaNdarray_HOST_DIMS(self)))\n",
-      "1392     {\n",
-      "1393         Py_XDECREF(rval);\n",
-      "1394         return NULL;\n",
-      "1395     }\n",
-      "1396     unsigned int size = 1;\n",
-      "1397     for (int i = 0; i < self->nd; i++)\n",
-      "1398     {\n",
-      "1399         size *= (unsigned int) CudaNdarray_HOST_DIMS(self)[i];\n",
-      "1400     }\n",
-      "1401     unsigned int threads_per_block = std::min(size, (unsigned int)NUM_VECTOR_OP_THREADS_PER_BLOCK);\n",
-      "1402     unsigned int n_blocks = std::min(ceil_intdiv(size,threads_per_block), (unsigned int)NUM_VECTOR_OP_BLOCKS);\n",
-      "1403     k_elemwise_unary_rowmajor_exp<<<n_blocks,threads_per_block>>>(size, self->nd, CudaNdarray_DEV_DIMS(self),\n",
-      "1404             CudaNdarray_DEV_DATA(self), CudaNdarray_DEV_STRIDES(self),\n",
-      "1405             CudaNdarray_DEV_DATA(rval), CudaNdarray_DEV_STRIDES(rval));\n",
-      "1406 \n",
-      "1407     //TODO: don't do this right away, do it when we need the result\n",
-      "1408     CNDA_THREAD_SYNC;\n",
-      "1409     cudaError_t err = cudaGetLastError();\n",
-      "1410     if( cudaSuccess != err)\n",
-      "1411     {\n",
-      "1412         Py_DECREF(rval);\n",
-      "1413         PyErr_Format(PyExc_RuntimeError, \"Cuda error: %s: %s.\\n\", \"kExp\", cudaGetErrorString(err));\n",
-      "1414         return NULL;\n",
-      "1415     }\n",
-      "1416 \n",
-      "1417     return (PyObject*)rval;\n",
-      "1418 }\n",
-      "1419 \n",
-      "1420 static PyMethodDef CudaNdarray_methods[] =\n",
-      "1421 {\n",
-      "1422     {\"__array__\",\n",
-      "1423         (PyCFunction)CudaNdarray_CreateArrayObj, METH_VARARGS,\n",
-      "1424         \"Copy from the device to a numpy ndarray\"},\n",
-      "1425     {\"__copy__\",\n",
-      "1426         (PyCFunction)CudaNdarray_View, METH_NOARGS,\n",
-      "1427         \"Create a shallow copy of this object. used by module copy\"},\n",
-      "1428     {\"__deepcopy__\",\n",
-      "1429         (PyCFunction)CudaNdarray_DeepCopy, METH_O,\n",
-      "1430         \"Create a copy of this object\"},\n",
-      "1431     {\"zeros\",\n",
-      "1432         (PyCFunction)CudaNdarray_Zeros, METH_STATIC | METH_O,\n",
-      "1433         \"Create a new CudaNdarray with specified shape, filled with zeros.\"},\n",
-      "1434     {\"copy\",\n",
-      "1435         (PyCFunction)CudaNdarray_Copy, METH_NOARGS,\n",
-      "1436         \"Create a copy of this object\"},\n",
-      "1437     {\"is_c_contiguous\",\n",
-      "1438         (PyCFunction)CudaNdarray_IS_C_Contiguous, METH_NOARGS,\n",
-      "1439         \"Return True is the object is c contiguous. False otherwise.\"},\n",
-      "1440     {\"reduce_sum\",\n",
-      "1441         (PyCFunction)CudaNdarray_ReduceSum, METH_O,\n",
-      "1442         \"Reduce over the given dimensions by summation\"},\n",
-      "1443     {\"exp\",\n",
-      "1444         (PyCFunction)CudaNdarray_exp, METH_NOARGS,\n",
-      "1445         \"Return the exponential of all elements\"},\n",
-      "1446     {\"reshape\",\n",
-      "1447         (PyCFunction)CudaNdarray_Reshape, METH_O,\n",
-      "1448         \"Return a reshaped view (or copy) of this ndarray\\n\\\n",
-      "1449             The required argument is a tuple of integers specifying the shape of the new ndarray.\"},\n",
-      "1450     {\"view\",\n",
-      "1451         (PyCFunction)CudaNdarray_View, METH_NOARGS,\n",
-      "1452         \"Return an alias of this ndarray\"},\n",
-      "1453     {\"_set_stride\",\n",
-      "1454         (PyCFunction)CudaNdarray_SetStride, METH_VARARGS,\n",
-      "1455         \"For integer arguments (i, s), set the 'i'th stride to 's'\"},\n",
-      "1456     {\"take\",\n",
-      "1457         (PyCFunction)CudaNdarray_TakeFrom, METH_VARARGS,\n",
-      "1458         \"Equivalent of numpy.take\"},\n",
-      "1459     {\"_set_shape_i\",\n",
-      "1460         (PyCFunction)CudaNdarray_SetShapeI, METH_VARARGS,\n",
-      "1461         \"For integer arguments (i, s), set the 'i'th shape to 's'\"},\n",
-      "1462     {NULL, NULL, NULL, NULL}  /* Sentinel */\n",
-      "1463 };\n",
-      "1464 \n",
-      "1465 \n",
-      "1466 ////////////////////\n",
-      "1467 // Number protocol\n",
-      "1468 ////////////////////\n",
-      "1469 \n",
-      "1470 __global__ void kAdd_contiguous(float* a, float* b, float* dest, unsigned int numEls) {\n",
-      "1471     const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;\n",
-      "1472     const unsigned int numThreads = blockDim.x * gridDim.x;\n",
-      "1473 \n",
-      "1474     for (unsigned int i = idx; i < numEls; i += numThreads) {\n",
-      "1475         dest[i] = a[i] + b[i];\n",
-      "1476     }\n",
-      "1477 }\n",
-      "1478 \n",
-      "1479 // Will be called by __add__ in Python\n",
-      "1480 static PyObject *\n",
-      "1481 CudaNdarray_add(PyObject* py_self, PyObject * py_other)\n",
-      "1482 {\n",
-      "1483     if (! CudaNdarray_Check(py_self)) {\n",
-      "1484         PyErr_SetString(PyExc_TypeError, \"need a CudaNdarray on left\");\n",
-      "1485         return NULL;\n",
-      "1486     }\n",
-      "1487     if (! CudaNdarray_Check(py_other)) {\n",
-      "1488         PyErr_SetString(PyExc_TypeError, \"need a CudaNdarray on right\");\n",
-      "1489         return NULL;\n",
-      "1490     }\n",
-      "1491     CudaNdarray * self = (CudaNdarray *)py_self;\n",
-      "1492     CudaNdarray * other = (CudaNdarray *)py_other;\n",
-      "1493     if(!CudaNdarray_is_c_contiguous(self) || !CudaNdarray_is_c_contiguous(other)){\n",
-      "1494         PyErr_SetString(PyExc_TypeError, \"We have implementet only the c_contiguous version for now.\");\n",
-      "1495         return NULL;\n",
-      "1496     }\n",
-      "1497 \n",
-      "1498     //standard elemwise size checks\n",
-      "1499     if (self->nd != other->nd)\n",
-      "1500     {\n",
-      "1501         PyErr_SetString(PyExc_TypeError, \"CudaNdarray_add: need same number of dims\");\n",
-      "1502         return NULL;\n",
-      "1503     }\n",
-      "1504     //standard elemwise dim checks\n",
-      "1505     unsigned int size = 1;\n",
-      "1506     for (int i = 0; i< self->nd; ++i)\n",
-      "1507     {\n",
-      "1508         if (CudaNdarray_HOST_DIMS(self)[i] != CudaNdarray_HOST_DIMS(other)[i])\n",
-      "1509         {\n",
-      "1510             PyErr_SetString(PyExc_TypeError, \"need same dimensions\");\n",
-      "1511             return NULL;\n",
-      "1512         }\n",
-      "1513         size *= (unsigned int) CudaNdarray_HOST_DIMS(self)[i];\n",
-      "1514     }\n",
-      "1515     CudaNdarray * rval = (CudaNdarray *)CudaNdarray_New();\n",
-      "1516     if (!rval || CudaNdarray_alloc_contiguous(rval, self->nd, CudaNdarray_HOST_DIMS(self)))\n",
-      "1517     {\n",
-      "1518         Py_XDECREF(rval);\n",
-      "1519         return NULL;\n",
-      "1520     }\n",
-      "1521 \n",
-      "1522     if(CudaNdarray_SIZE((CudaNdarray *)py_self)==0 && CudaNdarray_SIZE((CudaNdarray *)py_other)==0){\n",
-      "1523       return (PyObject *) rval;\n",
-      "1524     }\n",
-      "1525 \n",
-      "1526     int threads_per_block = std::min(size, (unsigned int)NUM_VECTOR_OP_THREADS_PER_BLOCK);\n",
-      "1527     int n_blocks = std::min(ceil_intdiv(size,(unsigned int)threads_per_block), (unsigned int)NUM_VECTOR_OP_BLOCKS);\n",
-      "1528     kAdd_contiguous<<<n_blocks,threads_per_block>>>(\n",
-      "1529             self->devdata, other->devdata, rval->devdata, size);\n",
-      "1530     CNDA_THREAD_SYNC;\n",
-      "1531     cudaError_t err = cudaGetLastError();\n",
-      "1532     if( cudaSuccess != err)\n",
-      "1533     {\n",
-      "1534         PyErr_Format(PyExc_RuntimeError, \"Cuda error: %s: %s.\\n\", \"kAdd\", cudaGetErrorString(err));\n",
-      "1535         Py_DECREF(rval);\n",
-      "1536         return NULL;\n",
-      "1537     }\n",
-      "1538     return (PyObject *) rval;\n",
-      "1539 }\n",
-      "1540 \n",
-      "1541 template <int operator_num>\n",
-      "1542 __global__ void k_ielem_3(const int d0, const int d1, const int d2,\n",
-      "1543         float* a, const int sA0, const int sA1, const int sA2,\n",
-      "1544         const float* b, const int sB0, const int sB1, const int sB2){\n",
-      "1545     for (int i0 = blockIdx.x; i0 < d0; i0 += gridDim.x){\n",
-      "1546         for (int i1 = blockIdx.y; i1 < d1; i1 += gridDim.y){\n",
-      "1547             for (int i2 = threadIdx.x; i2 < d2; i2 += blockDim.x){\n",
-      "1548                 switch (operator_num)\n",
-      "1549                 {\n",
-      "1550                   case IADD:\n",
-      "1551                     a[i0*sA0 + i1*sA1 + i2*sA2] += b[i0*sB0 + i1*sB1 + i2*sB2];\n",
-      "1552                     break;\n",
-      "1553                   case IDIV:\n",
-      "1554                     a[i0*sA0 + i1*sA1 + i2*sA2] /= b[i0*sB0 + i1*sB1 + i2*sB2];\n",
-      "1555                     break;\n",
-      "1556                   case CPY:\n",
-      "1557                     a[i0*sA0 + i1*sA1 + i2*sA2] = b[i0*sB0 + i1*sB1 + i2*sB2];\n",
-      "1558                     break;\n",
-      "1559                 }\n",
-      "1560             }\n",
-      "1561         }\n",
-      "1562     }\n",
-      "1563 }\n",
-      "1564 \n",
-      "1565 template <int operator_num>\n",
-      "1566 __global__ void k_ielem_4(const int d0, const int d1, const int d2, const int d3,\n",
-      "1567                          float* a, const int sA0, const int sA1,\n",
-      "1568                          const int sA2, const int sA3,\n",
-      "1569                          const float* b, const int sB0, const int sB1,\n",
-      "1570                          const int sB2, const int sB3){\n",
-      "1571     for (int i0 = blockIdx.x; i0 < d0; i0 += gridDim.x){\n",
-      "1572         for (int i1 = blockIdx.y; i1 < d1; i1 += gridDim.y){\n",
-      "1573             for (int i2 = threadIdx.x; i2 < d2; i2 += blockDim.x){\n",
-      "1574                 for (int i3 = threadIdx.y; i3 < d3; i3 += blockDim.y){\n",
-      "1575                     switch (operator_num) {\n",
-      "1576                         case IADD:\n",
-      "1577                             a[i0*sA0 + i1*sA1 + i2*sA2 + i3*sA3]\n",
-      "1578                             += b[i0*sB0 + i1*sB1 + i2*sB2 + i3*sB3];\n",
-      "1579                             break;\n",
-      "1580                         case IDIV:\n",
-      "1581                             a[i0*sA0 + i1*sA1 + i2*sA2 + i3*sA3]\n",
-      "1582                             /= b[i0*sB0 + i1*sB1 + i2*sB2 + i3*sB3];\n",
-      "1583                             break;\n",
-      "1584                         case CPY:\n",
-      "1585                             a[i0*sA0 + i1*sA1 + i2*sA2 + i3*sA3]\n",
-      "1586                             = b[i0*sB0 + i1*sB1 + i2*sB2 + i3*sB3];\n",
-      "1587                             break;\n",
-      "1588                     }\n",
-      "1589                 }\n",
-      "1590             }\n",
-      "1591         }\n",
-      "1592     }\n",
-      "1593 }\n",
-      "1594 \n",
-      "1595 template <int operator_num>\n",
-      "1596 __global__ void k_ielem_6(const int d0, const int d1,\n",
-      "1597                           const int d2, const int d3,\n",
-      "1598                           const int d4, const int d5,\n",
-      "1599                           float* a, const int sA0, const int sA1,\n",
-      "1600                           const int sA2, const int sA3,\n",
-      "1601                           const int sA4, const int sA5,\n",
-      "1602                           const float* b, const int sB0, const int sB1,\n",
-      "1603                           const int sB2, const int sB3,\n",
-      "1604                           const int sB4, const int sB5\n",
-      "1605                           ){\n",
-      "1606     for (int i0 = blockIdx.x; i0 < d0; i0 += gridDim.x){\n",
-      "1607         for (int i1 = blockIdx.y; i1 < d1; i1 += gridDim.y){\n",
-      "1608             for (int i2 = blockIdx.z; i2 < d2; i2 += gridDim.z){\n",
-      "1609                 for (int i3 = threadIdx.x; i3 < d3; i3 += blockDim.x){\n",
-      "1610                     for (int i4 = threadIdx.y; i4 < d4; i4 += blockDim.y){\n",
-      "1611                         for (int i5 = threadIdx.z; i5 < d5; i5 += blockDim.z){\n",
-      "1612                             switch (operator_num) {\n",
-      "1613                             case IADD:\n",
-      "1614                                 a[i0*sA0 + i1*sA1 + i2*sA2 + i3*sA3 + i4*sA4 + i5*sA5]\n",
-      "1615                                     += b[i0*sB0 + i1*sB1 + i2*sB2 + i3*sB3 + i4*sB4 + i5*sB5];\n",
-      "1616                                 break;\n",
-      "1617                             case IDIV:\n",
-      "1618                                 a[i0*sA0 + i1*sA1 + i2*sA2 + i3*sA3 + i4*sA4 + i5*sA5]\n",
-      "1619                                     /= b[i0*sB0 + i1*sB1 + i2*sB2 + i3*sB3 + i4*sB4 + i5*sB5];\n",
-      "1620                                 break;\n",
-      "1621                             case CPY:\n",
-      "1622                                 a[i0*sA0 + i1*sA1 + i2*sA2 + i3*sA3 + i4*sA4 + i5*sA5]\n",
-      "1623                                     = b[i0*sB0 + i1*sB1 + i2*sB2 + i3*sB3 + i4*sB4 + i5*sB5];\n",
-      "1624                                 break;\n",
-      "1625                             }\n",
-      "1626                         }\n",
-      "1627                     }\n",
-      "1628                 }\n",
-      "1629             }\n",
-      "1630         }\n",
-      "1631     }\n",
-      "1632 }\n",
-      "1633 \n",
-      "1634 /*\n",
-      "1635 CudaNdarray_inplace_elemwise\n",
-      "1636 Compute elemwise, working inplace on A.\n",
-      "1637 Currently implemented A / B, A + B and A = B\n",
-      "1638 (the last is not tested and not used!)\n",
-      "1639 \n",
-      "1640 py_self - the CudaNdarray that we'll modify (A)\n",
-      "1641 py_other - the other argument (B)\n",
-      "1642 fct_nb - which operation to perform (operator_t)\n",
-      "1643 \n",
-      "1644 Returns 0 on success.\n",
-      "1645 Returns -1 on failure, and sets Python exception.\n",
-      "1646 \n",
-      "1647 */\n",
-      "1648 int\n",
-      "1649 CudaNdarray_inplace_elemwise(PyObject* py_self, PyObject * py_other, operator_t fct_nb)\n",
-      "1650 {\n",
-      "1651     int verbose = 0;\n",
-      "1652     void (*k3)(const int, const int, const int,\n",
-      "1653                     float*, const int, const int, const int,\n",
-      "1654                     const float*, const int, const int, const int);\n",
-      "1655     void (*k4)(const int, const int, const int, const int,\n",
-      "1656                     float*, const int, const int,\n",
-      "1657                     const int, const int,\n",
-      "1658                     const float*, const int, const int,\n",
-      "1659                     const int, const int);\n",
-      "1660     void (*k6)(const int, const int,\n",
-      "1661                const int, const int,\n",
-      "1662                const int, const int,\n",
-      "1663                float*, const int, const int,\n",
-      "1664                const int, const int,\n",
-      "1665                const int, const int,\n",
-      "1666                const float*, const int, const int,\n",
-      "1667                const int, const int,\n",
-      "1668                const int, const int);\n",
-      "1669     switch (fct_nb)\n",
-      "1670     {\n",
-      "1671         case IADD:\n",
-      "1672             k3 = k_ielem_3<IADD>;\n",
-      "1673             k4 = k_ielem_4<IADD>;\n",
-      "1674             k6 = k_ielem_6<IADD>;\n",
-      "1675             break;\n",
-      "1676         case IDIV:\n",
-      "1677             k3 = k_ielem_3<IDIV>;\n",
-      "1678             k4 = k_ielem_4<IDIV>;\n",
-      "1679             k6 = k_ielem_6<IDIV>;\n",
-      "1680             break;\n",
-      "1681         case CPY:\n",
-      "1682             k3 = k_ielem_3<CPY>;\n",
-      "1683             k4 = k_ielem_4<CPY>;\n",
-      "1684             k6 = k_ielem_6<CPY>;\n",
-      "1685             break;\n",
-      "1686         default:\n",
-      "1687             assert (0);\n",
-      "1688             PyErr_Format(\n",
-      "1689                 PyExc_TypeError,\n",
-      "1690                 \"CudaNdarray_inplace_elemwise invalid fct_nb (%i).\",\n",
-      "1691                 (int)fct_nb);\n",
-      "1692             return -1;\n",
-      "1693     }\n",
-      "1694     if (!CudaNdarray_Check(py_self)) {\n",
-      "1695         PyErr_SetString(\n",
-      "1696             PyExc_TypeError,\n",
-      "1697             \"CudaNdarray_inplace_elemwise need a CudaNdarray on left\");\n",
-      "1698         return -1;\n",
-      "1699     }\n",
-      "1700     CudaNdarray * new_other = NULL;\n",
-      "1701     if (!CudaNdarray_Check(py_other)) {\n",
-      "1702         new_other = (CudaNdarray*) CudaNdarray_New();\n",
-      "1703         if(!new_other)\n",
-      "1704         {\n",
-      "1705             return -1;\n",
-      "1706         }\n",
-      "1707         if(CudaNdarray_CopyFromArray(new_other, (PyArrayObject *) py_other))\n",
-      "1708         {\n",
-      "1709             Py_XDECREF(new_other);\n",
-      "1710             return -1;\n",
-      "1711         }\n",
-      "1712         py_other = (PyObject *) new_other;\n",
-      "1713     }\n",
-      "1714 \n",
-      "1715     CudaNdarray * self = (CudaNdarray *)py_self;\n",
-      "1716     CudaNdarray * other = (CudaNdarray *)py_other;\n",
-      "1717 \n",
-      "1718     if (verbose)\n",
-      "1719     {\n",
-      "1720         fprintf(stderr,\n",
-      "1721             \"INPLACE ADD/DIV for self->nd=%d other->nd=%d\\n\",\n",
-      "1722             self->nd, other->nd);\n",
-      "1723     }\n",
-      "1724 \n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "1725     //standard elemwise nb dim checks\n",
-      "1726     if (self->nd < other->nd)\n",
-      "1727     {\n",
-      "1728         PyErr_Format(\n",
-      "1729             PyExc_TypeError,\n",
-      "1730             \"CudaNdarray_inplace_elemwise: The destination need more or the\"\n",
-      "1731             \" same number of dimensions then the source. Got %d and %d.\",\n",
-      "1732             self->nd, other->nd);\n",
-      "1733         Py_XDECREF(new_other);\n",
-      "1734         return -1;\n",
-      "1735     }\n",
-      "1736 \n",
-      "1737     //broadcast to the same number of dimensions.\n",
-      "1738     int* other_dims = (int*) alloca(self->nd * sizeof(int));\n",
-      "1739     int* other_strides = (int*) alloca(self->nd * sizeof(int));\n",
-      "1740     int added_dims = self->nd - other->nd;\n",
-      "1741     // Add the added broadcasted dimensions\n",
-      "1742     for (int i = 0; i< added_dims; ++i)\n",
-      "1743     {\n",
-      "1744         other_dims[i] = 1;\n",
-      "1745         other_strides[i] = 0;\n",
-      "1746     }\n",
-      "1747     // Copy the existing dimensions\n",
-      "1748     for (int i = 0; i< other->nd; ++i)\n",
-      "1749     {\n",
-      "1750         other_dims[i+added_dims] = CudaNdarray_HOST_DIMS(other)[i];\n",
-      "1751         other_strides[i+added_dims] = CudaNdarray_HOST_STRIDES(other)[i];\n",
-      "1752     }\n",
-      "1753 \n",
-      "1754     //standard elemwise dim checks\n",
-      "1755     unsigned int size = 1;\n",
-      "1756     for (int i = 0; i< self->nd; ++i)\n",
-      "1757     {\n",
-      "1758         if ((CudaNdarray_HOST_DIMS(self)[i] != other_dims[i])\n",
-      "1759             && (other_dims[i] != 1))\n",
-      "1760         {\n",
-      "1761             PyErr_SetString(\n",
-      "1762                 PyExc_ValueError,\n",
-      "1763                 \"CudaNdarray_inplace_elemwise need same dimensions (or broadcastable dimension)\");\n",
-      "1764             Py_XDECREF(new_other);\n",
-      "1765             return -1;\n",
-      "1766         }\n",
-      "1767         // if we're broadcasting other, then make sure it has stride 0\n",
-      "1768         assert ((CudaNdarray_HOST_DIMS(self)[i] == other_dims[i])\n",
-      "1769             || (other_strides[i] == 0));\n",
-      "1770         size *= (unsigned int) CudaNdarray_HOST_DIMS(self)[i];\n",
-      "1771     }\n",
-      "1772 \n",
-      "1773     if (size==0)\n",
-      "1774     {\n",
-      "1775         int other_size = CudaNdarray_SIZE((CudaNdarray *)py_other);\n",
-      "1776         if (!(other_size == 0 || other_size == 1))\n",
-      "1777         {\n",
-      "1778             PyErr_SetString(\n",
-      "1779                 PyExc_ValueError,\n",
-      "1780                 \"CudaNdarray_inplace_elemwise cannot work inplace on\"\n",
-      "1781                 \" un-initialized array when the new value have more than\"\n",
-      "1782                 \" 0 or 1 broadcastable dimensions\");\n",
-      "1783             Py_XDECREF(new_other);\n",
-      "1784             return 0;\n",
-      "1785         }\n",
-      "1786         Py_XDECREF(new_other);\n",
-      "1787         return 0;\n",
-      "1788     }\n",
-      "1789 \n",
-      "1790     switch(self->nd)\n",
-      "1791     {\n",
-      "1792         case 0:\n",
-      "1793             {\n",
-      "1794                 dim3 n_blocks(1, 1, 1);\n",
-      "1795                 dim3 n_threads(1);\n",
-      "1796                 k3<<<n_blocks, n_threads>>>(\n",
-      "1797                         1, //d0\n",
-      "1798                         1, //d1\n",
-      "1799                         1, //d2\n",
-      "1800                         CudaNdarray_DEV_DATA(self),\n",
-      "1801                         1, //strides\n",
-      "1802                         1,\n",
-      "1803                         1,\n",
-      "1804                         CudaNdarray_DEV_DATA(other),\n",
-      "1805                         1, //strides\n",
-      "1806                         1,\n",
-      "1807                         1);\n",
-      "1808                 CNDA_THREAD_SYNC;\n",
-      "1809                 cudaError_t err = cudaGetLastError();\n",
-      "1810                 if (cudaSuccess != err)\n",
-      "1811                 {\n",
-      "1812                     PyErr_Format(\n",
-      "1813                         PyExc_RuntimeError,\n",
-      "1814                         \"CudaNdarray_inplace_elemwise case0: Cuda error: %s: %s.\\n\",\n",
-      "1815                         \"k3\",\n",
-      "1816                         cudaGetErrorString(err));\n",
-      "1817                     Py_XDECREF(new_other);\n",
-      "1818                     return -1;\n",
-      "1819                 }\n",
-      "1820             }\n",
-      "1821             break;\n",
-      "1822         case 1:\n",
-      "1823             {\n",
-      "1824                 dim3 n_blocks(1, 1, 1);\n",
-      "1825                 dim3 n_threads(\n",
-      "1826                         std::min(\n",
-      "1827                             CudaNdarray_HOST_DIMS(self)[0],\n",
-      "1828                             NUM_VECTOR_OP_THREADS_PER_BLOCK));\n",
-      "1829                 k3<<<n_blocks, n_threads>>>(\n",
-      "1830                         1, //dimensions\n",
-      "1831                         1,\n",
-      "1832                         CudaNdarray_HOST_DIMS(self)[0],\n",
-      "1833                         CudaNdarray_DEV_DATA(self),\n",
-      "1834                         1, //strides\n",
-      "1835                         1,\n",
-      "1836                         CudaNdarray_HOST_STRIDES(self)[0],\n",
-      "1837                         CudaNdarray_DEV_DATA(other),\n",
-      "1838                         1, //strides\n",
-      "1839                         1,\n",
-      "1840                         other_strides[0]);\n",
-      "1841                 CNDA_THREAD_SYNC;\n",
-      "1842                 cudaError_t err = cudaGetLastError();\n",
-      "1843                 if (cudaSuccess != err)\n",
-      "1844                 {\n",
-      "1845                     PyErr_Format(\n",
-      "1846                         PyExc_RuntimeError,\n",
-      "1847                         \"CudaNdarray_inplace_elemwise case1: Cuda error: %s: %s.\\n\",\n",
-      "1848                         \"k3\",\n",
-      "1849                         cudaGetErrorString(err));\n",
-      "1850                     Py_XDECREF(new_other);\n",
-      "1851                     return -1;\n",
-      "1852                 }\n",
-      "1853             }\n",
-      "1854             break;\n",
-      "1855         case 2:\n",
-      "1856             {\n",
-      "1857                 //TODO:  if both self and other are f-contiguous\n",
-      "1858                 //       Then flip the block and thread dimensions\n",
-      "1859                 //       to make contiguous reads & writes\n",
-      "1860                 dim3 n_blocks(1,\n",
-      "1861                         std::min(\n",
-      "1862                             CudaNdarray_HOST_DIMS(self)[0],\n",
-      "1863                             NUM_VECTOR_OP_BLOCKS));\n",
-      "1864                 dim3 n_threads(\n",
-      "1865                         std::min(\n",
-      "1866                             CudaNdarray_HOST_DIMS(self)[1],\n",
-      "1867                             NUM_VECTOR_OP_THREADS_PER_BLOCK));\n",
-      "1868                 k3<<<n_blocks, n_threads>>>(1,\n",
-      "1869                         CudaNdarray_HOST_DIMS(self)[0],\n",
-      "1870                         CudaNdarray_HOST_DIMS(self)[1],\n",
-      "1871                         CudaNdarray_DEV_DATA(self),\n",
-      "1872                         1,\n",
-      "1873                         CudaNdarray_HOST_STRIDES(self)[0],\n",
-      "1874                         CudaNdarray_HOST_STRIDES(self)[1],\n",
-      "1875                         CudaNdarray_DEV_DATA(other),\n",
-      "1876                         1,\n",
-      "1877                         other_strides[0],\n",
-      "1878                         other_strides[1]);\n",
-      "1879                 CNDA_THREAD_SYNC;\n",
-      "1880                 cudaError_t err = cudaGetLastError();\n",
-      "1881                 if (cudaSuccess != err)\n",
-      "1882                 {\n",
-      "1883                     PyErr_Format(\n",
-      "1884                         PyExc_RuntimeError,\n",
-      "1885                         \"CudaNdarray_inplace_elemwise case2: Cuda error: %s: %s.\\n\",\n",
-      "1886                         \"k3\",\n",
-      "1887                         cudaGetErrorString(err));\n",
-      "1888                     Py_XDECREF(new_other);\n",
-      "1889                     return -1;\n",
-      "1890                 }\n",
-      "1891             }\n",
-      "1892             break;\n",
-      "1893         case 3:\n",
-      "1894             {\n",
-      "1895                 //TODO:  Dimshuffle so that at least one of the arrays\n",
-      "1896                 //       has a contiguous dimension on the thread idx.\n",
-      "1897                 dim3 n_blocks(\n",
-      "1898                         std::min(\n",
-      "1899                             CudaNdarray_HOST_DIMS(self)[0],\n",
-      "1900                             NUM_VECTOR_OP_BLOCKS),\n",
-      "1901                         CudaNdarray_HOST_DIMS(self)[1]);\n",
-      "1902                 while (n_blocks.x * n_blocks.y > NUM_VECTOR_OP_BLOCKS)\n",
-      "1903                     n_blocks.y /= 2;\n",
-      "1904                 dim3 n_threads(\n",
-      "1905                         std::min(\n",
-      "1906                             CudaNdarray_HOST_DIMS(self)[2],\n",
-      "1907                             NUM_VECTOR_OP_THREADS_PER_BLOCK));\n",
-      "1908                 k3<<<n_blocks, n_threads>>>(\n",
-      "1909                         CudaNdarray_HOST_DIMS(self)[0],\n",
-      "1910                         CudaNdarray_HOST_DIMS(self)[1],\n",
-      "1911                         CudaNdarray_HOST_DIMS(self)[2],\n",
-      "1912                         CudaNdarray_DEV_DATA(self),\n",
-      "1913                         CudaNdarray_HOST_STRIDES(self)[0],\n",
-      "1914                         CudaNdarray_HOST_STRIDES(self)[1],\n",
-      "1915                         CudaNdarray_HOST_STRIDES(self)[2],\n",
-      "1916                         CudaNdarray_DEV_DATA(other),\n",
-      "1917                         other_strides[0],\n",
-      "1918                         other_strides[1],\n",
-      "1919                         other_strides[2]);\n",
-      "1920                 CNDA_THREAD_SYNC;\n",
-      "1921                 cudaError_t err = cudaGetLastError();\n",
-      "1922                 if (cudaSuccess != err)\n",
-      "1923                 {\n",
-      "1924                     PyErr_Format(\n",
-      "1925                         PyExc_RuntimeError,\n",
-      "1926                         \"CudaNdarray_inplace_elemwise case3: Cuda error: %s: %s.\\n\",\n",
-      "1927                         \"k3\",\n",
-      "1928                         cudaGetErrorString(err));\n",
-      "1929                     Py_XDECREF(new_other);\n",
-      "1930                     return -1;\n",
-      "1931                 }\n",
-      "1932             }\n",
-      "1933             break;\n",
-      "1934         case 4:\n",
-      "1935             {\n",
-      "1936                 dim3 n_blocks(\n",
-      "1937                         std::min(\n",
-      "1938                             CudaNdarray_HOST_DIMS(self)[0],\n",
-      "1939                             NUM_VECTOR_OP_BLOCKS),\n",
-      "1940                         CudaNdarray_HOST_DIMS(self)[1]\n",
-      "1941                         );\n",
-      "1942                 while (n_blocks.x * n_blocks.y > NUM_VECTOR_OP_BLOCKS)\n",
-      "1943                     n_blocks.y /= 2;\n",
-      "1944                 dim3 n_threads(\n",
-      "1945                         std::min(\n",
-      "1946                             CudaNdarray_HOST_DIMS(self)[2],\n",
-      "1947                             NUM_VECTOR_OP_THREADS_PER_BLOCK)\n",
-      "1948                     //TODO: DON\"T YOU NEED OT PUT DIMS[3] in here???\n",
-      "1949                             );\n",
-      "1950                 k4<<<n_blocks, n_threads>>>(\n",
-      "1951                         CudaNdarray_HOST_DIMS(self)[0],\n",
-      "1952                         CudaNdarray_HOST_DIMS(self)[1],\n",
-      "1953                         CudaNdarray_HOST_DIMS(self)[2],\n",
-      "1954                         CudaNdarray_HOST_DIMS(self)[3],\n",
-      "1955                         CudaNdarray_DEV_DATA(self),\n",
-      "1956                         CudaNdarray_HOST_STRIDES(self)[0],\n",
-      "1957                         CudaNdarray_HOST_STRIDES(self)[1],\n",
-      "1958                         CudaNdarray_HOST_STRIDES(self)[2],\n",
-      "1959                         CudaNdarray_HOST_STRIDES(self)[3],\n",
-      "1960                         CudaNdarray_DEV_DATA(other),\n",
-      "1961                         other_strides[0],\n",
-      "1962                         other_strides[1],\n",
-      "1963                         other_strides[2],\n",
-      "1964                         other_strides[3]);\n",
-      "1965                 CNDA_THREAD_SYNC;\n",
-      "1966                 cudaError_t err = cudaGetLastError();\n",
-      "1967                 if (cudaSuccess != err)\n",
-      "1968                 {\n",
-      "1969                     PyErr_Format(\n",
-      "1970                         PyExc_RuntimeError,\n",
-      "1971                         \"CudaNdarray_inplace_elemwise case4: Cuda error: %s: %s.\\n\",\n",
-      "1972                         \"k4\",\n",
-      "1973                         cudaGetErrorString(err));\n",
-      "1974                     Py_XDECREF(new_other);\n",
-      "1975                     return -1;\n",
-      "1976                 }\n",
-      "1977             }\n",
-      "1978             break;\n",
-      "1979         case 5:\n",
-      "1980             {\n",
-      "1981                 dim3 n_blocks(\n",
-      "1982                         std::min(\n",
-      "1983                             CudaNdarray_HOST_DIMS(self)[1],\n",
-      "1984                             NUM_VECTOR_OP_BLOCKS),\n",
-      "1985                         CudaNdarray_HOST_DIMS(self)[2]);\n",
-      "1986                 while (n_blocks.x * n_blocks.y > NUM_VECTOR_OP_BLOCKS)\n",
-      "1987                     n_blocks.y /= 2;\n",
-      "1988                 dim3 n_threads(\n",
-      "1989                         std::min(\n",
-      "1990                             CudaNdarray_HOST_DIMS(self)[3],\n",
-      "1991                             NUM_VECTOR_OP_THREADS_PER_BLOCK)\n",
-      "1992                     //TODO: DON\"T YOU NEED OT PUT DIMS[3] in here???\n",
-      "1993                     );\n",
-      "1994                 for (int i = 0; i < CudaNdarray_HOST_DIMS(self)[0]; ++i)\n",
-      "1995                 {\n",
-      "1996                      k4<<<n_blocks, n_threads>>>(\n",
-      "1997                             CudaNdarray_HOST_DIMS(self)[1],\n",
-      "1998                             CudaNdarray_HOST_DIMS(self)[2],\n",
-      "1999                             CudaNdarray_HOST_DIMS(self)[3],\n",
-      "2000                             CudaNdarray_HOST_DIMS(self)[4],\n",
-      "2001                             CudaNdarray_DEV_DATA(self) + i * CudaNdarray_HOST_STRIDES(self)[0],\n",
-      "2002                             CudaNdarray_HOST_STRIDES(self)[1],\n",
-      "2003                             CudaNdarray_HOST_STRIDES(self)[2],\n",
-      "2004                             CudaNdarray_HOST_STRIDES(self)[3],\n",
-      "2005                             CudaNdarray_HOST_STRIDES(self)[4],\n",
-      "2006                             CudaNdarray_DEV_DATA(other) + i * other_strides[0],\n",
-      "2007                             other_strides[1],\n",
-      "2008                             other_strides[2],\n",
-      "2009                             other_strides[3],\n",
-      "2010                             other_strides[4]);\n",
-      "2011                     CNDA_THREAD_SYNC;\n",
-      "2012                     cudaError_t err = cudaGetLastError();\n",
-      "2013                     if( cudaSuccess != err)\n",
-      "2014                     {\n",
-      "2015                         PyErr_Format(\n",
-      "2016                             PyExc_RuntimeError,\n",
-      "2017                             \"CudaNdarray_inplace_elemwise case5: Cuda error: %s: %s. n_block=(%ld,%ld) n_threads=%ld\\n\",\n",
-      "2018                             \"k5 with loop over k4\",\n",
-      "2019                             cudaGetErrorString(err),\n",
-      "2020                             (long) n_blocks.x, (long) n_blocks.y, (long) n_threads.x);\n",
-      "2021                         Py_XDECREF(new_other);\n",
-      "2022                         return -1;\n",
-      "2023                     }\n",
-      "2024                 }\n",
-      "2025             }\n",
-      "2026             break;\n",
-      "2027         case 6:\n",
-      "2028             {\n",
-      "2029                 dim3 n_blocks(\n",
-      "2030                         std::min(\n",
-      "2031                             CudaNdarray_HOST_DIMS(self)[0],\n",
-      "2032                             NUM_VECTOR_OP_BLOCKS),\n",
-      "2033                         CudaNdarray_HOST_DIMS(self)[1],\n",
-      "2034                         CudaNdarray_HOST_DIMS(self)[2]\n",
-      "2035                         );\n",
-      "2036                 while (n_blocks.x * n_blocks.y > NUM_VECTOR_OP_BLOCKS)\n",
-      "2037                     n_blocks.y /= 2;\n",
-      "2038                 // GTX285(compute capabilities 1.3) don't support n_blocks.z > 1\n",
-      "2039                 // (compute capabilities 2.0) support 65535 for n_blocks.z\n",
-      "2040                 //while (n_blocks.x * n_blocks.y * n_blocks.z > NUM_VECTOR_OP_BLOCKS)\n",
-      "2041                 //    n_blocks.z /= 2;\n",
-      "2042                 n_blocks.z = 1;\n",
-      "2043                 dim3 n_threads(\n",
-      "2044                         std::min(\n",
-      "2045                             CudaNdarray_HOST_DIMS(self)[3],\n",
-      "2046                             NUM_VECTOR_OP_THREADS_PER_BLOCK)\n",
-      "2047                     //TODO: DON'T YOU NEED TO PUT DIMS[4] in here???\n",
-      "2048                     //TODO: DON'T YOU NEED TO PUT DIMS[5] in here???\n",
-      "2049                             );\n",
-      "2050                 k6<<<n_blocks, n_threads>>>(\n",
-      "2051                         CudaNdarray_HOST_DIMS(self)[0],\n",
-      "2052                         CudaNdarray_HOST_DIMS(self)[1],\n",
-      "2053                         CudaNdarray_HOST_DIMS(self)[2],\n",
-      "2054                         CudaNdarray_HOST_DIMS(self)[3],\n",
-      "2055                         CudaNdarray_HOST_DIMS(self)[4],\n",
-      "2056                         CudaNdarray_HOST_DIMS(self)[5],\n",
-      "2057                         CudaNdarray_DEV_DATA(self),\n",
-      "2058                         CudaNdarray_HOST_STRIDES(self)[0],\n",
-      "2059                         CudaNdarray_HOST_STRIDES(self)[1],\n",
-      "2060                         CudaNdarray_HOST_STRIDES(self)[2],\n",
-      "2061                         CudaNdarray_HOST_STRIDES(self)[3],\n",
-      "2062                         CudaNdarray_HOST_STRIDES(self)[4],\n",
-      "2063                         CudaNdarray_HOST_STRIDES(self)[5],\n",
-      "2064                         CudaNdarray_DEV_DATA(other),\n",
-      "2065                         other_strides[0],\n",
-      "2066                         other_strides[1],\n",
-      "2067                         other_strides[2],\n",
-      "2068                         other_strides[3],\n",
-      "2069                         other_strides[4],\n",
-      "2070                         other_strides[5]);\n",
-      "2071                 CNDA_THREAD_SYNC;\n",
-      "2072                 cudaError_t err = cudaGetLastError();\n",
-      "2073                 if (cudaSuccess != err)\n",
-      "2074                 {\n",
-      "2075                     PyErr_Format(\n",
-      "2076                         PyExc_RuntimeError,\n",
-      "2077                         \"CudaNdarray_inplace_elemwise case6: Cuda error: %s: %s. n_blocks=(%ld, %ld, %ld) n_threads=(%ld)\\n\",\n",
-      "2078                         \"k6\",\n",
-      "2079                         cudaGetErrorString(err),\n",
-      "2080                         (long) n_blocks.x, (long) n_blocks.y, (long) n_blocks.z,\n",
-      "2081                         (long) n_threads.x);\n",
-      "2082                     Py_XDECREF(new_other);\n",
-      "2083                     return -1;\n",
-      "2084                 }\n",
-      "2085             }\n",
-      "2086             break;\n",
-      "2087         default:\n",
-      "2088         {\n",
-      "2089             PyErr_Format(\n",
-      "2090                 PyExc_NotImplementedError,\n",
-      "2091                 \"inplace_elemwise w nd=%i\\n\",\n",
-      "2092                 self->nd);\n",
-      "2093             Py_XDECREF(new_other);\n",
-      "2094             return -1;\n",
-      "2095         }\n",
-      "2096     }\n",
-      "2097     if (verbose)\n",
-      "2098         fprintf(stderr, \"INPLACE ADD/DIV end\\n\");\n",
-      "2099     Py_XDECREF(new_other);\n",
-      "2100     return 0;\n",
-      "2101 }\n",
-      "2102 \n",
-      "2103 /*\n",
-      "2104  * We need this inplace Add to support IncSubTensor\n",
-      "2105  * It returns py_self on success with an additional reference. Else NULL.\n",
-      "2106  */\n",
-      "2107 // Will be called by __iadd__ in Python\n",
-      "2108 PyObject *\n",
-      "2109 CudaNdarray_inplace_add(PyObject* py_self, PyObject * py_other)\n",
-      "2110 {\n",
-      "2111     if (CudaNdarray_inplace_elemwise(py_self, py_other, IADD))\n",
-      "2112     {\n",
-      "2113         return NULL;\n",
-      "2114     }\n",
-      "2115     Py_INCREF(py_self);\n",
-      "2116     return py_self;\n",
-      "2117 }\n",
-      "2118 \n",
-      "2119 /*\n",
-      "2120  * We need this inplace div for cuda/tests/test_basic_ops.py:test_shared_options\n",
-      "2121  * It returns py_self on success with an additional reference. Else NULL.\n",
-      "2122  */\n",
-      "2123 // Will be called by __idiv__ in Python\n",
-      "2124 static PyObject *\n",
-      "2125 CudaNdarray_inplace_div(PyObject* py_self, PyObject * py_other)\n",
-      "2126 {\n",
-      "2127     if (CudaNdarray_inplace_elemwise(py_self, py_other, IDIV))\n",
-      "2128     {\n",
-      "2129         return NULL;\n",
-      "2130     }\n",
-      "2131     Py_INCREF(py_self);\n",
-      "2132     return py_self;\n",
-      "2133 }\n",
-      "2134 \n",
-      "2135 // The PyNumberMethods struct layout changed in a non-trivial way from 2 to 3.\n",
-      "2136 #if PY_MAJOR_VERSION == 3\n",
-      "2137 static PyNumberMethods CudaNdarrayNumberMethods =\n",
-      "2138 {\n",
-      "2139     (binaryfunc)CudaNdarray_add,  //binaryfunc nb_add;  __add__\n",
-      "2140     0,  //binaryfunc nb_subtract;\n",
-      "2141     0,  //binaryfunc nb_multiply;\n",
-      "2142     0,  //binaryfunc nb_remainder;\n",
-      "2143     0,  //binaryfunc nb_divmod;\n",
-      "2144     0,  //ternaryfunc nb_power;\n",
-      "2145     0,  //unaryfunc nb_negative;\n",
-      "2146     0,  //unaryfunc nb_positive;\n",
-      "2147     0,  //unaryfunc nb_absolute;\n",
-      "2148     0,  //inquiry nb_bool;\n",
-      "2149     0,  //unaryfunc nb_invert;\n",
-      "2150     0,  //binaryfunc nb_lshift;\n",
-      "2151     0,  //binaryfunc nb_rshift;\n",
-      "2152     0,  //binaryfunc nb_and;\n",
-      "2153     0,  //binaryfunc nb_xor;\n",
-      "2154     0,  //binaryfunc nb_or;\n",
-      "2155     0,  //unaryfunc nb_int;\n",
-      "2156     0,  //void *nb_reserved;\n",
-      "2157     0,  //unaryfunc nb_float;\n",
-      "2158 \n",
-      "2159     (binaryfunc)CudaNdarray_inplace_add,  //binaryfunc nb_inplace_add;  __iadd__\n",
-      "2160     0,  //binaryfunc nb_inplace_subtract;\n",
-      "2161     0,  //binaryfunc nb_inplace_multiply;\n",
-      "2162     0,  //binaryfunc nb_inplace_remainder;\n",
-      "2163     0,  //ternaryfunc nb_inplace_power;\n",
-      "2164     0,  //binaryfunc nb_inplace_lshift;\n",
-      "2165     0,  //binaryfunc nb_inplace_rshift;\n",
-      "2166     0,  //binaryfunc nb_inplace_and;\n",
-      "2167     0,  //binaryfunc nb_inplace_xor;\n",
-      "2168     0,  //binaryfunc nb_inplace_or;\n",
-      "2169 \n",
-      "2170     0,  //binaryfunc nb_floor_divide;\n",
-      "2171     0,  //binaryfunc nb_true_divide;\n",
-      "2172     0,  //binaryfunc nb_inplace_floor_divide;\n",
-      "2173     (binaryfunc)CudaNdarray_inplace_div,  //binaryfunc nb_inplace_true_divide;        __idiv__\n",
-      "2174 \n",
-      "2175     0,  //unaryfunc nb_index\n",
-      "2176 };\n",
-      "2177 #else\n",
-      "2178 static PyNumberMethods CudaNdarrayNumberMethods =\n",
-      "2179 {\n",
-      "2180     (binaryfunc)CudaNdarray_add,  //binaryfunc nb_add;  __add__\n",
-      "2181     0,  //binaryfunc nb_subtract;      __sub__\n",
-      "2182     0,  //binaryfunc nb_multiply;      __mul__\n",
-      "2183     0,  //binaryfunc nb_divide;        __div__\n",
-      "2184     0,  //binaryfunc nb_remainder;     __mod__\n",
-      "2185     0,  //binaryfunc nb_divmod;        __divmod__\n",
-      "2186     0,  //ternaryfunc nb_power;        __pow__\n",
-      "2187     0,  //unaryfunc nb_negative;       __neg__\n",
-      "2188     0,  //unaryfunc nb_positive;       __pos__\n",
-      "2189     0,  //unaryfunc nb_absolute;       __abs__\n",
-      "2190     0,  //inquiry nb_nonzero;          __nonzero__     /* Used by PyObject_IsTrue */\n",
-      "2191     0,  //unaryfunc nb_invert;         __invert__\n",
-      "2192     0,  //binaryfunc nb_lshift;        __lshift__\n",
-      "2193     0,  //binaryfunc nb_rshift;        __rshift__\n",
-      "2194     0,  //binaryfunc nb_and;           __and__\n",
-      "2195     0,  //binaryfunc nb_xor;           __xor__\n",
-      "2196     0,  //binaryfunc nb_or;            __or__\n",
-      "2197     0,  //coercion nb_coerce;          __coerce__     /* Used by the coerce() function */\n",
-      "2198     0,  //unaryfunc nb_int;            __int__\n",
-      "2199     0,  //unaryfunc nb_long;           __long__\n",
-      "2200     0,  //unaryfunc nb_float;          __float__\n",
-      "2201     0,  //unaryfunc nb_oct;            __oct__\n",
-      "2202     0,  //unaryfunc nb_hex;            __hex__\n",
-      "2203 \n",
-      "2204     /* Added in release 2.0 */\n",
-      "2205     (binaryfunc)CudaNdarray_inplace_add,  //binaryfunc nb_inplace_add;  __iadd__\n",
-      "2206     0,  //binaryfunc nb_inplace_subtract;      __isub__\n",
-      "2207     0,  //binaryfunc nb_inplace_multiply;      __imul__\n",
-      "2208     (binaryfunc)CudaNdarray_inplace_div,  //binaryfunc nb_inplace_divide;        __idiv__\n",
-      "2209     0,  //binaryfunc nb_inplace_remainder;     __imod__\n",
-      "2210     0,  //ternaryfunc nb_inplace_power;        __ipow__\n",
-      "2211     0,  //binaryfunc nb_inplace_lshift;        __ilshift__\n",
-      "2212     0,  //binaryfunc nb_inplace_rshift;        __irshift__\n",
-      "2213     0,  //binaryfunc nb_inplace_and;           __iand__\n",
-      "2214     0,  //binaryfunc nb_inplace_xor;           __ixor__\n",
-      "2215     0,  //binaryfunc nb_inplace_or;            __ior__\n",
-      "2216 \n",
-      "2217     /* Added in release 2.2 */\n",
-      "2218     0,  //binaryfunc nb_floor_divide;          __floordiv__\n",
-      "2219     0,  //binaryfunc nb_true_divide;           __truediv__\n",
-      "2220     0,  //binaryfunc nb_inplace_floor_divide;  __ifloordiv__\n",
-      "2221     (binaryfunc)CudaNdarray_inplace_div,  //binaryfunc nb_inplace_true_divide;   __itruediv__\n",
-      "2222 \n",
-      "2223 #if PY_MINOR_VERSION > 4\n",
-      "2224     /* Added in release 2.5 */\n",
-      "2225     0  //unaryfunc nb_index;  __index__\n",
-      "2226 #endif\n",
-      "2227 };\n",
-      "2228 #endif\n",
-      "2229 \n",
-      "2230 \n",
-      "2231 /////////////////////\n",
-      "2232 // Mapping protocol\n",
-      "2233 /////////////////////\n",
-      "2234 \n",
-      "2235 // Will by called by __len__ in Python\n",
-      "2236 static Py_ssize_t\n",
-      "2237 CudaNdarray_len(PyObject * py_self)\n",
-      "2238 {\n",
-      "2239     CudaNdarray * self = (CudaNdarray*) py_self;\n",
-      "2240     if (self->nd <= 0)\n",
-      "2241     {\n",
-      "2242         return (Py_ssize_t) 0;\n",
-      "2243     }\n",
-      "2244     else\n",
-      "2245     {\n",
-      "2246         return (Py_ssize_t) CudaNdarray_HOST_DIMS(self)[0];\n",
-      "2247     }\n",
-      "2248 }\n",
-      "2249 \n",
-      "2250 // Will by called by __getitem__ in Python\n",
-      "2251 PyObject *\n",
-      "2252 CudaNdarray_Subscript(PyObject * py_self, PyObject * key)\n",
-      "2253 {\n",
-      "2254     int verbose = 0;\n",
-      "2255     if (verbose) fprintf(stderr, \"Subscript .... \\n\");\n",
-      "2256     CudaNdarray * self = (CudaNdarray*) py_self;\n",
-      "2257     PyObject * py_rval = NULL;\n",
-      "2258     CudaNdarray * rval = NULL;\n",
-      "2259     PyObject * intobj = NULL;\n",
-      "2260 \n",
-      "2261     //PyObject_Print(key, stderr, 0);\n",
-      "2262 \n",
-      "2263     if (key == Py_Ellipsis)\n",
-      "2264     {\n",
-      "2265         Py_INCREF(py_self);\n",
-      "2266         return py_self;\n",
-      "2267     }\n",
-      "2268     if ((intobj=PyNumber_Int(key))) //INDEXING BY INTEGER\n",
-      "2269     //else if (PyInt_Check(key)) //INDEXING BY INTEGER\n",
-      "2270     {\n",
-      "2271         int d_idx = PyInt_AsLong(intobj);\n",
-      "2272         Py_DECREF(intobj); intobj=NULL;\n",
-      "2273         //int d_idx = PyInt_AsLong(key);\n",
-      "2274         if (self->nd == 0)\n",
-      "2275         {\n",
-      "2276             PyErr_SetString(PyExc_IndexError, \"0-d arrays can't be indexed\");\n",
-      "2277             return NULL;\n",
-      "2278         }\n",
-      "2279         int d_dim = CudaNdarray_HOST_DIMS(self)[0];\n",
-      "2280         int offset = 0;\n",
-      "2281 \n",
-      "2282         if ((d_idx >= 0) && (d_idx < d_dim))\n",
-      "2283         {\n",
-      "2284             //normal indexing\n",
-      "2285             offset += d_idx * CudaNdarray_HOST_STRIDES(self)[0];\n",
-      "2286         }\n",
-      "2287         else if ((d_idx < 0) && (d_idx >= -d_dim))\n",
-      "2288         {\n",
-      "2289             //end-based indexing\n",
-      "2290             // d_idx is negative\n",
-      "2291             offset += (d_dim + d_idx) * CudaNdarray_HOST_STRIDES(self)[0];\n",
-      "2292         }\n",
-      "2293         else\n",
-      "2294         {\n",
-      "2295             PyErr_Format(PyExc_IndexError,\n",
-      "2296                          \"index out of bounds. Asked %d, but size of %d\",\n",
-      "2297                          d_idx, d_dim);\n",
-      "2298             return NULL;\n",
-      "2299         }\n",
-      "2300 \n",
-      "2301         //allocate our subtensor view\n",
-      "2302         py_rval = CudaNdarray_new_nd(self->nd - 1);\n",
-      "2303         rval = (CudaNdarray*) py_rval;\n",
-      "2304         if (!rval) return NULL;\n",
-      "2305         assert (0 == rval->data_allocated);\n",
-      "2306 \n",
-      "2307         //initialize the view's data pointer to our own.\n",
-      "2308         if (CudaNdarray_set_device_data(rval, CudaNdarray_DEV_DATA(self) + offset, self))\n",
-      "2309         {\n",
-      "2310             Py_DECREF(rval);\n",
-      "2311             return NULL;\n",
-      "2312         }\n",
-      "2313         for (int d = 1; d < self->nd; ++d)\n",
-      "2314         {\n",
-      "2315             CudaNdarray_set_stride(rval, d-1, CudaNdarray_HOST_STRIDES(self)[d]);\n",
-      "2316             CudaNdarray_set_dim(rval, d-1, CudaNdarray_HOST_DIMS(self)[d]);\n",
-      "2317         }\n",
-      "2318     }\n",
-      "2319     else\n",
-      "2320     {\n",
-      "2321         PyErr_Clear();\n",
-      "2322     }\n",
-      "2323     if (PySlice_Check(key)) //INDEXING BY SLICE\n",
-      "2324     {\n",
-      "2325         if (verbose) fprintf(stderr, \"by slice\\n\");\n",
-      "2326         if (self->nd == 0)\n",
-      "2327         {\n",
-      "2328             PyErr_SetString(PyExc_ValueError, \"cannot slice a 0-d array\");\n",
-      "2329             return NULL;\n",
-      "2330         }\n",
-      "2331 \n",
-      "2332         int d_dim = CudaNdarray_HOST_DIMS(self)[0];\n",
-      "2333         Py_ssize_t start, stop, step, slen;\n",
-      "2334         if (PySlice_GetIndicesEx(SLICE_CAST(key), d_dim, &start, &stop, &step, &slen))\n",
-      "2335         {\n",
-      "2336             if (verbose)\n",
-      "2337                 fprintf(stderr, \"PySlice_GetIndicesEx failed\\n\");\n",
-      "2338             return NULL;\n",
-      "2339         }\n",
-      "2340         if (verbose)\n",
-      "2341         {\n",
-      "2342             std::cerr << \"start \" << start << \"\\n\";\n",
-      "2343             std::cerr << \"stop \" << stop << \"\\n\";\n",
-      "2344             std::cerr << \"step \" << step << \"\\n\";\n",
-      "2345             std::cerr << \"slen \" << slen << \"\\n\";\n",
-      "2346         }\n",
-      "2347 \n",
-      "2348         //allocate our subtensor view\n",
-      "2349         py_rval = CudaNdarray_new_nd(self->nd);\n",
-      "2350         rval = (CudaNdarray*) py_rval;\n",
-      "2351         if (!rval) return NULL;\n",
-      "2352         assert (0 == rval->data_allocated);\n",
-      "2353 \n",
-      "2354 \n",
-      "2355         //initialize the view's data pointer to our own.\n",
-      "2356         if (CudaNdarray_set_device_data(rval,\n",
-      "2357                     CudaNdarray_DEV_DATA(self) + start * CudaNdarray_HOST_STRIDES(self)[0],\n",
-      "2358                     self))\n",
-      "2359         {\n",
-      "2360             Py_DECREF(rval);\n",
-      "2361             return NULL;\n",
-      "2362         }\n",
-      "2363         //initialize dimension 0 of rval\n",
-      "2364         CudaNdarray_set_stride(rval, 0,\n",
-      "2365                 (slen == 1) ? 0 : step * CudaNdarray_HOST_STRIDES(self)[0]);\n",
-      "2366         CudaNdarray_set_dim(rval, 0, slen);\n",
-      "2367         if (verbose) std::cerr << \"rval stride \" << CudaNdarray_HOST_STRIDES(rval)[0] << \"\\n\";\n",
-      "2368         // initialize dimensions > 0 of rval\n",
-      "2369         for (int d = 1; d < self->nd; ++d)\n",
-      "2370         {\n",
-      "2371             CudaNdarray_set_stride(rval, d, CudaNdarray_HOST_STRIDES(self)[d]);\n",
-      "2372             CudaNdarray_set_dim(rval, d, CudaNdarray_HOST_DIMS(self)[d]);\n",
-      "2373         }\n",
-      "2374     }\n",
-      "2375     if (PyTuple_Check(key)) //INDEXING BY TUPLE\n",
-      "2376     {\n",
-      "2377         if (verbose) fprintf(stderr, \"by tuple\\n\");\n",
-      "2378         //elements of the tuple can be either integers or slices\n",
-      "2379         //the dimensionality of the view we will return is diminished for each slice in the tuple\n",
-      "2380 \n",
-      "2381         if (PyTuple_Size(key) > self->nd)\n",
-      "2382         {\n",
-      "2383             PyErr_SetString(PyExc_IndexError, \"index error\");\n",
-      "2384             return NULL;\n",
-      "2385         }\n",
-      "2386 \n",
-      "2387         //calculate the number of dimensions in the return value\n",
-      "2388         int rval_nd = self->nd;\n",
-      "2389         for (int d = 0; d < PyTuple_Size(key); ++d)\n",
-      "2390         {\n",
-      "2391             //On some paltform PyInt_Check(<type 'numpy.int64'>) return true, other it return false.\n",
-      "2392             //So we use PyArray_IsAnyScalar that should covert everything.\n",
-      "2393             rval_nd -= PyArray_IsAnyScalar(PyTuple_GetItem(key, d));\n",
-      "2394         }\n",
-      "2395 \n",
-      "2396         //allocate our subtensor view\n",
-      "2397         py_rval = CudaNdarray_new_nd(rval_nd);\n",
-      "2398         rval = (CudaNdarray*) py_rval;\n",
-      "2399         if (!rval) return NULL;\n",
-      "2400         assert (0 == rval->data_allocated);\n",
-      "2401 \n",
-      "2402         //initialize the view's data pointer to our own.\n",
-      "2403         if (CudaNdarray_set_device_data(rval, CudaNdarray_DEV_DATA(self), self))\n",
-      "2404         {\n",
-      "2405             Py_DECREF(rval);\n",
-      "2406             return NULL;\n",
-      "2407         }\n",
-      "2408 \n",
-      "2409         // rval_d will refer to the current dimension in the rval.\n",
-      "2410         // It will not be incremented for integer keys, but will be incremented for slice\n",
-      "2411         // keys\n",
-      "2412         int rval_d = 0;\n",
-      "2413 \n",
-      "2414         for (int d = 0; d < self->nd; ++d)\n",
-      "2415         {\n",
-      "2416             // keys can be shorter than self->nd.\n",
-      "2417             // when that happens, it means that the remaining dimensions are \"full slices\"\n",
-      "2418             if (d >=PyTuple_Size(key))\n",
-      "2419             {\n",
-      "2420                 CudaNdarray_set_stride(rval, rval_d, CudaNdarray_HOST_STRIDES(self)[d]);\n",
-      "2421                 CudaNdarray_set_dim(rval, rval_d, CudaNdarray_HOST_DIMS(self)[d]);\n",
-      "2422                 ++rval_d;\n",
-      "2423             }\n",
-      "2424             else\n",
-      "2425             {\n",
-      "2426                 PyObject * key_d = PyTuple_GetItem(key, d);\n",
-      "2427 \n",
-      "2428                 if (PySlice_Check(key_d))\n",
-      "2429                 {\n",
-      "2430                     Py_ssize_t start, stop, step, slen;\n",
-      "2431                     if (PySlice_GetIndicesEx(SLICE_CAST(key_d), CudaNdarray_HOST_DIMS(self)[d], &start, &stop, &step, &slen))\n",
-      "2432                     {\n",
-      "2433                         Py_DECREF(rval);\n",
-      "2434                         return NULL;\n",
-      "2435                     }\n",
-      "2436                     rval->devdata += start * CudaNdarray_HOST_STRIDES(self)[d];\n",
-      "2437                     CudaNdarray_set_stride(rval, rval_d,\n",
-      "2438                             (slen == 1) ? 0 : step * CudaNdarray_HOST_STRIDES(self)[d]);\n",
-      "2439                     CudaNdarray_set_dim(rval, rval_d, slen);\n",
-      "2440                     if (0)\n",
-      "2441                     {\n",
-      "2442                         std::cerr << \"start \" << start << \"\\n\";\n",
-      "2443                         std::cerr << \"stop \" << stop << \"\\n\";\n",
-      "2444                         std::cerr << \"step \" << step << \"\\n\";\n",
-      "2445                         std::cerr << \"slen \" << slen << \"\\n\";\n",
-      "2446                     }\n",
-      "2447                     ++rval_d;\n",
-      "2448                 }\n",
-      "2449                 else if ((intobj=PyNumber_Int(key_d)))\n",
-      "2450                 {\n",
-      "2451                     assert(PyArray_IsAnyScalar(key_d));\n",
-      "2452                     int d_idx = PyInt_AsLong(intobj);\n",
-      "2453                     Py_DECREF(intobj);\n",
-      "2454                     intobj = NULL;\n",
-      "2455                     int d_dim = CudaNdarray_HOST_DIMS(self)[d];\n",
-      "2456 \n",
-      "2457                     if ((d_idx >= 0) && (d_idx < d_dim))\n",
-      "2458                     {\n",
-      "2459                         //normal indexing\n",
-      "2460                         rval->devdata += d_idx * CudaNdarray_HOST_STRIDES(self)[d];\n",
-      "2461                     }\n",
-      "2462                     else if ((d_idx < 0) && (d_idx >= -d_dim))\n",
-      "2463                     {\n",
-      "2464                         //end-based indexing\n",
-      "2465                         rval->devdata += (d_dim + d_idx) * CudaNdarray_HOST_STRIDES(self)[d];\n",
-      "2466                     }\n",
-      "2467                     else\n",
-      "2468                     {\n",
-      "2469                         PyErr_Format(PyExc_IndexError,\n",
-      "2470                                      \"index out of bounds. Asked %d for dimensions %d, but size of %d\",\n",
-      "2471                                      d_idx, d, d_dim);\n",
-      "2472                         Py_DECREF(rval);\n",
-      "2473                         return NULL;\n",
-      "2474                     }\n",
-      "2475                 }\n",
-      "2476                 else\n",
-      "2477                 {\n",
-      "2478                     PyErr_Clear(); // clear the error set by PyNumber_Int\n",
-      "2479                     PyErr_SetString(PyExc_IndexError, \"index must be either int or slice\");\n",
-      "2480                     Py_DECREF(rval);\n",
-      "2481                     return NULL;\n",
-      "2482                 }\n",
-      "2483             }\n",
-      "2484         }\n",
-      "2485     }\n",
-      "2486     if (py_rval)\n",
-      "2487     {\n",
-      "2488         if (verbose) fprint_CudaNdarray(stderr, self);\n",
-      "2489         if (verbose) fprint_CudaNdarray(stderr, rval);\n",
-      "2490     }\n",
-      "2491     else\n",
-      "2492     {\n",
-      "2493         PyErr_SetString(PyExc_NotImplementedError, \"Unknown key type\");\n",
-      "2494         return NULL;\n",
-      "2495     }\n",
-      "2496     return py_rval;\n",
-      "2497 }\n",
-      "2498 \n",
-      "2499 // Will by called by __setitem__ in Python\n",
-      "2500 // See http://docs.python.org/dev/py3k/c-api/object.html#PyObject_SetItem\n",
-      "2501 // Doesn't handle broadcasting, e.g. a[:] = 5\n",
-      "2502 // Can only be assigned from a CudaNdarray on the right side\n",
-      "2503 // Or a ndarray\n",
-      "2504 // Or a python scalar with value 0 when the left side part is c contiguous.\n",
-      "2505 static int\n",
-      "2506 CudaNdarray_setitem(PyObject *o, PyObject  *key, PyObject  *value)\n",
-      "2507 {\n",
-      "2508     int verbose = 0;\n",
-      "2509     if (verbose) fprintf(stderr, \"CudaNdarray_setitem start\\n\");\n",
-      "2510     // We try to copy directly into this CudaNdarray from the ndarray\n",
-      "2511     CudaNdarray* rval = (CudaNdarray*)CudaNdarray_Subscript(o, key);\n",
-      "2512     CudaNdarray* new_value = NULL;\n",
-      "2513 \n",
-      "2514     if(!rval){\n",
-      "2515         // CudaNdarray_Subscript failed and set the error msg.\n",
-      "2516         Py_XDECREF(rval);\n",
-      "2517         return -1;\n",
-      "2518     }\n",
-      "2519 \n",
-      "2520     if(rval != (CudaNdarray*)o &&\n",
-      "2521                 (rval->data_allocated ||\n",
-      "2522                  // The new array should have a base\n",
-      "2523                  !(((CudaNdarray*)rval)->base) ||\n",
-      "2524                  // If the original array has no base, the base of the new\n",
-      "2525                  // array should be the original one\n",
-      "2526                  (!((CudaNdarray*)o)->base && ((CudaNdarray*)rval)->base != o) ||\n",
-      "2527                  // Else, the two arrays should have the same base\n",
-      "2528                  (((CudaNdarray*)o)->base && ((CudaNdarray*)rval)->base != ((CudaNdarray*)o)->base)))\n",
-      "2529     {\n",
-      "2530         // This case shouldn't happen, based on what I see in Subscript\n",
-      "2531         // but just in case it happens sometime in the future\n",
-      "2532 \n",
-      "2533         PyErr_Format(PyExc_RuntimeError,\n",
-      "2534                      \"__getitem__ must return a CudaNdarray that refers to\"\n",
-      "2535                      \" the original CudaNdarray, not a copy. rval.base=%p\"\n",
-      "2536                      \" o.base=%p o=%p\",\n",
-      "2537                      (((CudaNdarray*)rval)->base), ((CudaNdarray*)o)->base, o);\n",
-      "2538         Py_DECREF(rval);\n",
-      "2539         return -1;\n",
-      "2540     }\n",
-      "2541 \n",
-      "2542     PyObject * intobj = NULL;\n",
-      "2543     if (CudaNdarray_Check(o)  && PyArray_Check(value)){\n",
-      "2544         if (verbose)\n",
-      "2545             fprintf(stderr,\n",
-      "2546                     \"CudaNdarray_setitem dest is a CudaNdarray and\"\n",
-      "2547                     \" value is a ndarray\\n\");\n",
-      "2548         new_value = (CudaNdarray*) CudaNdarray_New();\n",
-      "2549         if(!new_value)\n",
-      "2550         {\n",
-      "2551             return -1;\n",
-      "2552         }\n",
-      "2553         if (CudaNdarray_CopyFromArray(new_value, (PyArrayObject *) value))\n",
-      "2554         {\n",
-      "2555             Py_XDECREF(new_value);\n",
-      "2556             Py_XDECREF(rval);\n",
-      "2557             return -1;\n",
-      "2558         }\n",
-      "2559         value = (PyObject *) new_value;\n",
-      "2560     }\n",
-      "2561     else if ((intobj=PyNumber_Int(value)))\n",
-      "2562     {\n",
-      "2563         if (verbose)\n",
-      "2564             fprintf(stderr,\n",
-      "2565                     \"CudaNdarray_setitem dest and value is a python number\\n\");\n",
-      "2566         if(! CudaNdarray_is_c_contiguous(rval)){\n",
-      "2567             PyErr_SetString(PyExc_NotImplementedError,\n",
-      "2568                  \"CudaNdarray.__setitem__: When the new value is a scalar\"\n",
-      "2569                  \" of value 0 the part where we copy to must be c contiguous.\");\n",
-      "2570             Py_XDECREF(rval);\n",
-      "2571             return -1;\n",
-      "2572         }\n",
-      "2573 \n",
-      "2574         long val = PyInt_AsLong(intobj);\n",
-      "2575         Py_DECREF(intobj); intobj=NULL;\n",
-      "2576         if (val == 0)\n",
-      "2577         {\n",
-      "2578             cudaError_t err = cudaMemset(rval->devdata, 0,\n",
-      "2579                                          CudaNdarray_SIZE(rval) * sizeof(real));\n",
-      "2580             Py_XDECREF(rval);\n",
-      "2581             if (err)\n",
-      "2582             {\n",
-      "2583                 // Clear the error flag, cudaMemset doesn't do it.\n",
-      "2584                 // Currently this returns the same thing as err, but if in future\n",
-      "2585                 // it returns something else I still don't see why we should ignore\n",
-      "2586                 // it.  All we want to do here is reset the flag.\n",
-      "2587                 cudaGetLastError();\n",
-      "2588                 PyErr_SetString(PyExc_RuntimeError,\n",
-      "2589                                 \"CudaNdarray.__setitem__: cudaMemset failed\");\n",
-      "2590                 return -1;\n",
-      "2591             }\n",
-      "2592             return 0;\n",
-      "2593         } else {\n",
-      "2594             Py_XDECREF(rval);\n",
-      "2595             PyErr_SetString(PyExc_NotImplementedError,\n",
-      "2596                   \"CudaNdarray.__setitem__: we support setting only python\"\n",
-      "2597                   \" scalar of value 0, numpy nd array and CudaNdarray.\");\n",
-      "2598                 return -1;\n",
-      "2599         }\n",
-      "2600     }\n",
-      "2601 \n",
-      "2602     PyErr_Clear(); // clear PyNumber_Int error.\n",
-      "2603 \n",
-      "2604     if(!CudaNdarray_Check(o) || !CudaNdarray_Check(value))\n",
-      "2605     {\n",
-      "2606         PyErr_SetString(PyExc_TypeError,\n",
-      "2607           \"CudaNdarray.__setitem__: left must be a CudaNdarrays and right\"\n",
-      "2608           \" must be a CudaNdarrays, an ndarray or a python scalar of value 0.\");\n",
-      "2609         Py_XDECREF(new_value);\n",
-      "2610         return -1;\n",
-      "2611     }\n",
-      "2612 \n",
-      "2613     if (verbose)\n",
-      "2614         fprintf(stderr, \"CudaNdarray_setitem dest and value are CudaNdarray\\n\");\n",
-      "2615 \n",
-      "2616     if (cnda_copy_structure_to_device(rval))\n",
-      "2617     {\n",
-      "2618         PyErr_SetString(PyExc_RuntimeError,\n",
-      "2619                 \"CudaNdarray.__setitem__: syncing structure to device failed\");\n",
-      "2620         Py_DECREF(rval);\n",
-      "2621         Py_XDECREF(new_value);\n",
-      "2622 \n",
-      "2623         if (verbose)\n",
-      "2624             fprintf(stderr, \"CudaNdarray_setitem error end\\n\");\n",
-      "2625         return -1;\n",
-      "2626     }\n",
-      "2627 \n",
-      "2628     PyObject *baseSavedForComparison = rval->base;\n",
-      "2629 \n",
-      "2630     if (CudaNdarray_CopyFromCudaNdarray(rval, (CudaNdarray*)value, true))\n",
-      "2631     {\n",
-      "2632         Py_DECREF((PyObject*)rval);\n",
-      "2633         Py_XDECREF(new_value);\n",
-      "2634 \n",
-      "2635         if (verbose)\n",
-      "2636             fprintf(stderr, \"CudaNdarray_setitem error end\\n\");\n",
-      "2637         return -1;\n",
-      "2638     }\n",
-      "2639 \n",
-      "2640     assert (rval->base == baseSavedForComparison);\n",
-      "2641     assert (rval->dev_structure_fresh);\n",
-      "2642 \n",
-      "2643     // Clean up locally-created references\n",
-      "2644     Py_DECREF(rval);\n",
-      "2645     Py_XDECREF(new_value);\n",
-      "2646 \n",
-      "2647     return 0;\n",
-      "2648 }\n",
-      "2649 \n",
-      "2650 \n",
-      "2651 PyMappingMethods CudaNdarrayMappingMethods = {\n",
-      "2652     CudaNdarray_len, //lenfunc mp_length;               __len__\n",
-      "2653     CudaNdarray_Subscript, //binaryfunc mp_subscript;   __getitem__\n",
-      "2654     CudaNdarray_setitem //objobjargproc mp_ass_subscript;                __setitem__\n",
-      "2655 };\n",
-      "2656 \n",
-      "2657 ////////////////////\n",
-      "2658 //\n",
-      "2659 ////////////////////\n",
-      "2660 \n",
-      "2661 static PyObject *\n",
-      "2662 CudaNdarray_get_shape(CudaNdarray *self, void *closure)\n",
-      "2663 {\n",
-      "2664     if (self->nd < 0)\n",
-      "2665     {\n",
-      "2666         PyErr_SetString(PyExc_ValueError, \"CudaNdarray not initialized\");\n",
-      "2667         return NULL;\n",
-      "2668     }\n",
-      "2669     PyObject * rval = PyTuple_New(self->nd);\n",
-      "2670     for (int i = 0; i < self->nd; ++i)\n",
-      "2671     {\n",
-      "2672         if (!rval || PyTuple_SetItem(rval, i, PyInt_FromLong(CudaNdarray_HOST_DIMS(self)[i])))\n",
-      "2673         {\n",
-      "2674             Py_XDECREF(rval);\n",
-      "2675             return NULL;\n",
-      "2676         }\n",
-      "2677 \n",
-      "2678     }\n",
-      "2679     return rval;\n",
-      "2680 }\n",
-      "2681 \n",
-      "2682 static int\n",
-      "2683 CudaNdarray_set_shape(CudaNdarray *self, PyObject *value, void *closure)\n",
-      "2684 {\n",
-      "2685     PyErr_SetString(PyExc_NotImplementedError, \"TODO: call reshape\");\n",
-      "2686     return -1;\n",
-      "2687 }\n",
-      "2688 \n",
-      "2689 static PyObject *\n",
-      "2690 CudaNdarray_get_strides(CudaNdarray *self, void *closure)\n",
-      "2691 {\n",
-      "2692     if (self->nd < 0)\n",
-      "2693     {\n",
-      "2694         PyErr_SetString(PyExc_ValueError, \"CudaNdarray not initialized\");\n",
-      "2695         return NULL;\n",
-      "2696     }\n",
-      "2697     PyObject * rval = PyTuple_New(self->nd);\n",
-      "2698     for (int i = 0; i < self->nd; ++i)\n",
-      "2699     {\n",
-      "2700         if (!rval || PyTuple_SetItem(rval, i, PyInt_FromLong(CudaNdarray_HOST_STRIDES(self)[i])))\n",
-      "2701         {\n",
-      "2702             Py_XDECREF(rval);\n",
-      "2703             return NULL;\n",
-      "2704         }\n",
-      "2705 \n",
-      "2706     }\n",
-      "2707     return rval;\n",
-      "2708 }\n",
-      "2709 \n",
-      "2710 static int\n",
-      "2711 CudaNdarray_set_strides(CudaNdarray *self, PyObject *value, void *closure)\n",
-      "2712 {\n",
-      "2713     //npy_intp newstrides_bytes[PyTuple_Size(value)];\n",
-      "2714     if (PyTuple_Check(value)){\n",
-      "2715         if (PyTuple_Size(value) != CudaNdarray_NDIM(self)){\n",
-      "2716             PyErr_SetString(PyExc_ValueError,\n",
-      "2717                             \"The new strides tuple must have the same length\"\n",
-      "2718                             \" as the number of dimensions\");\n",
-      "2719             return -1;\n",
-      "2720         }\n",
-      "2721     }else if (PyList_Check(value)){\n",
-      "2722         if (PyList_Size(value) != CudaNdarray_NDIM(self)){\n",
-      "2723             PyErr_SetString(PyExc_ValueError,\n",
-      "2724                             \"The new strides list must have the same length\"\n",
-      "2725                             \" as the number of dimensions\");\n",
-      "2726             return -1;\n",
-      "2727         }\n",
-      "2728     }else{\n",
-      "2729         PyErr_SetString(PyExc_ValueError,\n",
-      "2730                         \"The new strides need to be encoded in a tuple or list\");\n",
-      "2731         return -1;\n",
-      "2732     }\n",
-      "2733     npy_intp* newstrides = (npy_intp*) alloca(CudaNdarray_NDIM(self) * sizeof(npy_intp));\n",
-      "2734     if (PyTuple_Check(value)){\n",
-      "2735         for(int i=0; i < CudaNdarray_NDIM(self); i++){\n",
-      "2736             newstrides[i] = PyInt_AsLong(PyTuple_GetItem(value, Py_ssize_t(i)));\n",
-      "2737             //newstrides_bytes[i] = newstrides[i] * 4;\n",
-      "2738         }\n",
-      "2739     }else if (PyList_Check(value)){\n",
-      "2740         for(int i=0; i < CudaNdarray_NDIM(self); i++){\n",
-      "2741             newstrides[i] = PyInt_AsLong(PyList_GetItem(value, Py_ssize_t(i)));\n",
-      "2742             //newstrides_bytes[i] = newstrides[i] * 4;\n",
-      "2743         }\n",
-      "2744     }\n",
-      "2745     /*\n",
-      "2746     // Do not do this check, as ExtractDiag needs that, and NumPy does not seem\n",
-      "2747     // to do it.\n",
-      "2748     npy_intp dims[PyTuple_Size(value)];\n",
-      "2749     for(int i=0; i < CudaNdarray_NDIM(self); i++){\n",
-      "2750         dims[i] = CudaNdarray_HOST_DIMS(self)[i];\n",
-      "2751     }\n",
-      "2752     if (!PyArray_CheckStrides(4,\n",
-      "2753                               CudaNdarray_NDIM(self),\n",
-      "2754                               0, 0,\n",
-      "2755                               dims,\n",
-      "2756                               newstrides_bytes)){\n",
-      "2757         PyErr_SetString(PyExc_ValueError, \"bad new strides\");\n",
-      "2758         return -1;\n",
-      "2759         }\n",
-      "2760     */\n",
-      "2761     for(int i=0; i < CudaNdarray_NDIM(self); i++){\n",
-      "2762         CudaNdarray_set_stride(self, i, newstrides[i]);\n",
-      "2763     }\n",
-      "2764     return 0;\n",
-      "2765 }\n",
-      "2766 \n",
-      "2767 static PyObject *\n",
-      "2768 CudaNdarray_get_dev_data(CudaNdarray *self, void *closure)\n",
-      "2769 {\n",
-      "2770     float * p =  CudaNdarray_DEV_DATA(self);\n",
-      "2771     //printf(\"get_dev_data %p %li \\n\", p, (long int)p );\n",
-      "2772     return PyInt_FromSize_t((size_t) CudaNdarray_DEV_DATA(self));\n",
-      "2773 }\n",
-      "2774 \n",
-      "2775 static int\n",
-      "2776 CudaNdarray_set_dev_data(CudaNdarray *self, PyObject *value, void *closure)\n",
-      "2777 {\n",
-      "2778     Py_ssize_t newdevdata = PyInt_AsSsize_t(value);\n",
-      "2779     //printf(\"set_dev_data %p %li \\n\",(float*)newdevdata ,newdevdata);\n",
-      "2780     if (PyErr_Occurred())\n",
-      "2781     {\n",
-      "2782         return -1;\n",
-      "2783     }\n",
-      "2784     return  CudaNdarray_set_device_data(self, (float*)newdevdata, (CudaNdarray*)self->base);\n",
-      "2785 }\n",
-      "2786 \n",
-      "2787 static PyObject *\n",
-      "2788 CudaNdarray_get_dtype(CudaNdarray *self, void *closure)\n",
-      "2789 {\n",
-      "2790     return PyString_FromString(\"float32\");\n",
-      "2791 }\n",
-      "2792 \n",
-      "2793 static PyObject *\n",
-      "2794 CudaNdarray_get_ndim(CudaNdarray *self, void *closure)\n",
-      "2795 {\n",
-      "2796     return PyInt_FromLong(self->nd);\n",
-      "2797 }\n",
-      "2798 \n",
-      "2799 static PyObject *\n",
-      "2800 CudaNdarray_get_base(CudaNdarray *self, void *closure)\n",
-      "2801 {\n",
-      "2802     PyObject * base = self->base;\n",
-      "2803     if (!base)\n",
-      "2804     {\n",
-      "2805         // We cannot return a NULL pointer, use None instead\n",
-      "2806         base = Py_None;\n",
-      "2807     }\n",
-      "2808     Py_INCREF(base);\n",
-      "2809     return base;\n",
-      "2810 }\n",
-      "2811 \n",
-      "2812 void put_in_dict(PyObject * dict, const char * key, int val)\n",
-      "2813 {\n",
-      "2814   PyObject * k = PyString_FromString(key);\n",
-      "2815   PyObject * v = PyInt_FromLong(val);\n",
-      "2816   PyDict_SetItem(dict, k, v);\n",
-      "2817   Py_DECREF(k);\n",
-      "2818   Py_DECREF(v);\n",
-      "2819 }\n",
-      "2820 \n",
-      "2821 PyObject *\n",
-      "2822 GetDeviceProperties(PyObject* _unused, PyObject* args)\n",
-      "2823 {\n",
-      "2824   int dev_id = -1;\n",
-      "2825   if (! PyArg_ParseTuple(args, \"i\", &dev_id))\n",
-      "2826     return NULL;\n",
-      "2827   cudaDeviceProp deviceProp;\n",
-      "2828   cudaGetDeviceProperties(&deviceProp, dev_id);\n",
-      "2829 \n",
-      "2830   PyObject * dict = PyDict_New();\n",
-      "2831   PyObject * str= PyString_FromString(\"name\");\n",
-      "2832   PyObject * i = PyString_FromString(deviceProp.name);\n",
-      "2833   PyDict_SetItem(dict, str, i);\n",
-      "2834   Py_DECREF(str);\n",
-      "2835   Py_DECREF(i);\n",
-      "2836 \n",
-      "2837   put_in_dict(dict, \"major\", deviceProp.major);\n",
-      "2838   put_in_dict(dict, \"minor\", deviceProp.minor);\n",
-      "2839 #if CUDART_VERSION >= 2020\n",
-      "2840   int driverVersion = 0, runtimeVersion = 0;\n",
-      "2841   cudaDriverGetVersion(&driverVersion);\n",
-      "2842   cudaRuntimeGetVersion(&runtimeVersion);\n",
-      "2843   put_in_dict(dict, \"driverVersion\", driverVersion);\n",
-      "2844   put_in_dict(dict, \"runtimeVersion\", runtimeVersion);\n",
-      "2845 #endif\n",
-      "2846 #if CUDART_VERSION >= 2000\n",
-      "2847 \n",
-      "2848   put_in_dict(dict, \"multiProcessorCount\", deviceProp.multiProcessorCount);\n",
-      "2849   //if ConvertSMVer2Cores is not defined in cuda_runtime_api.h, the run time is too old.\n",
-      "2850   int sm_cores = -1;\n",
-      "2851   if(deviceProp.major==1)\n",
-      "2852     sm_cores = 32;\n",
-      "2853   else if(deviceProp.major==2 && deviceProp.minor==0)\n",
-      "2854     sm_cores = 32;\n",
-      "2855   else if(deviceProp.major==2 && deviceProp.minor==1)\n",
-      "2856     sm_cores = 48;\n",
-      "2857   put_in_dict(dict, \"coresCount\", sm_cores * deviceProp.multiProcessorCount);\n",
-      "2858 #endif\n",
-      "2859   put_in_dict(dict, \"totalConstMem\", deviceProp.totalConstMem);\n",
-      "2860   put_in_dict(dict, \"sharedMemPerBlock\", deviceProp.sharedMemPerBlock);\n",
-      "2861   put_in_dict(dict, \"regsPerBlock\", deviceProp.regsPerBlock);\n",
-      "2862   put_in_dict(dict, \"warpSize\", deviceProp.warpSize);\n",
-      "2863   put_in_dict(dict, \"maxThreadsPerBlock\", deviceProp.maxThreadsPerBlock);\n",
-      "2864   put_in_dict(dict, \"maxThreadsDim0\", deviceProp.maxThreadsDim[0]);\n",
-      "2865   put_in_dict(dict, \"maxThreadsDim1\", deviceProp.maxThreadsDim[1]);\n",
-      "2866   put_in_dict(dict, \"maxThreadsDim2\", deviceProp.maxThreadsDim[2]);\n",
-      "2867   put_in_dict(dict, \"maxGridSize0\", deviceProp.maxGridSize[0]);\n",
-      "2868   put_in_dict(dict, \"maxGridSize1\", deviceProp.maxGridSize[1]);\n",
-      "2869   put_in_dict(dict, \"maxGridSize2\", deviceProp.maxGridSize[2]);\n",
-      "2870   put_in_dict(dict, \"memPitch\", deviceProp.memPitch);\n",
-      "2871   put_in_dict(dict, \"textureAlignment\", deviceProp.textureAlignment);\n",
-      "2872   put_in_dict(dict, \"clockRate\", deviceProp.clockRate);\n",
-      "2873 #if CUDART_VERSION >= 2000\n",
-      "2874   put_in_dict(dict, \"deviceOverlap\", deviceProp.deviceOverlap);\n",
-      "2875 #endif\n",
-      "2876 #if CUDART_VERSION >= 2020\n",
-      "2877   put_in_dict(dict, \"kernelExecTimeoutEnabled\", deviceProp.kernelExecTimeoutEnabled);\n",
-      "2878   put_in_dict(dict, \"integrated\", deviceProp.integrated);\n",
-      "2879   put_in_dict(dict, \"canMapHostMemory\", deviceProp.canMapHostMemory);\n",
-      "2880   put_in_dict(dict, \"computeMode\", deviceProp.computeMode);\n",
-      "2881   //in the doc of this fct tell that 0 - Normal mode, 1 - only 1 context, 2 - no context\n",
-      "2882 #endif\n",
-      "2883 #if CUDART_VERSION >= 3000\n",
-      "2884   put_in_dict(dict, \"concurrentKernels\", deviceProp.concurrentKernels);\n",
-      "2885 #endif\n",
-      "2886 #if CUDART_VERSION >= 3010\n",
-      "2887   put_in_dict(dict, \"ECCEnabled\", deviceProp.ECCEnabled);\n",
-      "2888 #endif\n",
-      "2889 #if CUDART_VERSION >= 3020\n",
-      "2890   put_in_dict(dict, \"tccDriver\", deviceProp.tccDriver);\n",
-      "2891 #endif\n",
-      "2892 \n",
-      "2893   return dict;\n",
-      "2894 }\n",
-      "2895 \n",
-      "2896 /*\n",
-      "2897  * Returns in *free and *total respectively, the free and total amount of memory available for allocation by the device in bytes.\n",
-      "2898  */\n",
-      "2899 PyObject *\n",
-      "2900 GetDeviceMemInfo(PyObject* _unused, PyObject* dummy)\n",
-      "2901 {\n",
-      "2902     size_t free = 0, total = 0;\n",
-      "2903     if(g_gpu_context_active == 0){\n",
-      "2904         PyErr_Format(PyExc_RuntimeError, \"No gpu device selected yet. Please make sure the gpu device was initialized by Theano before.\");\n",
-      "2905         return NULL;\n",
-      "2906     }\n",
-      "2907 \n",
-      "2908     cudaError_t err = cudaMemGetInfo(&free, &total);\n",
-      "2909     if (err != cudaSuccess){\n",
-      "2910         // Clear the error flag, cudaMemGetInfo doesn't do it.\n",
-      "2911         // Currently this returns the same thing as err, but if in future\n",
-      "2912         // it returns something else I still don't see why we should ignore\n",
-      "2913         // it.  All we want to do here is reset the flag.\n",
-      "2914         cudaGetLastError();\n",
-      "2915         PyErr_Format(PyExc_RuntimeError,\n",
-      "2916                      \"Error while getting memory info about the gpu: %s\",\n",
-      "2917                      cudaGetErrorString(err));\n",
-      "2918         return NULL;\n",
-      "2919     }\n",
-      "2920     return PyTuple_Pack(2, PyLong_FromSize_t(free), PyLong_FromSize_t(total));\n",
-      "2921 }\n",
-      "2922 \n",
-      "2923 /*\n",
-      "2924  * Synchronize with all the gpu device stream.\n",
-      "2925  */\n",
-      "2926 PyObject *\n",
-      "2927 CudaNdarray_synchronize(PyObject* _unused, PyObject* dummy)\n",
-      "2928 {\n",
-      "2929     CNDA_BEGIN_ALLOW_THREADS\n",
-      "2930     cudaThreadSynchronize();\n",
-      "2931     CNDA_END_ALLOW_THREADS\n",
-      "2932     Py_INCREF(Py_None);\n",
-      "2933     return Py_None;\n",
-      "2934 }\n",
-      "2935 \n",
-      "2936 /*\n",
-      "2937  * Exist and return true if we link with cublas v2.\n",
-      "2938  */\n",
-      "2939 PyObject *\n",
-      "2940 CudaNdarray_cublasv2(PyObject* _unused, PyObject* dummy)\n",
-      "2941 {\n",
-      "2942     Py_INCREF(Py_True);\n",
-      "2943     return Py_True;\n",
-      "2944 }\n",
-      "2945 \n",
-      "2946 PyObject *\n",
-      "2947 CudaNdarray_select_a_gpu(PyObject* _unused, PyObject* dummy)\n",
-      "2948 {\n",
-      "2949     void * rval = NULL;\n",
-      "2950     cudaError_t err;\n",
-      "2951     int num_gpus = 0;\n",
-      "2952 \n",
-      "2953     err = cudaGetDeviceCount(&num_gpus);\n",
-      "2954     if (cudaSuccess != err){\n",
-      "2955         printf(\"ERR!\\\\n\");\n",
-      "2956             PyErr_Format(PyExc_RuntimeError,\n",
-      "2957                          \"Not able to get number of GPUs (%s).\",\n",
-      "2958                          cudaGetErrorString(err));\n",
-      "2959             return NULL;\n",
-      "2960     }\n",
-      "2961 \n",
-      "2962     for (int device = 0; device < num_gpus; device++) {\n",
-      "2963         cudaSetDevice(device);\n",
-      "2964         err = cudaDeviceSynchronize(); // << CUDA context gets created here.\n",
-      "2965         cudaGetLastError(); // reset the error state\n",
-      "2966         if (cudaSuccess == err)\n",
-      "2967             break;\n",
-      "2968     }\n",
-      "2969 \n",
-      "2970     if (cudaSuccess != err){\n",
-      "2971             printf(\"ERR!\\\\n\");\n",
-      "2972                 PyErr_Format(PyExc_RuntimeError,\n",
-      "2973                              \"Not able to select available GPU from %d cards (%s).\",\n",
-      "2974                              num_gpus, cudaGetErrorString(err));\n",
-      "2975                 return NULL;\n",
-      "2976     }\n",
-      "2977 \n",
-      "2978     Py_INCREF(Py_None);\n",
-      "2979     return Py_None;\n",
-      "2980 }\n",
-      "2981 \n",
-      "2982 #if COMPUTE_GPU_MEM_USED\n",
-      "2983 /*\n",
-      "2984  * Return the size in bytes that Theano currently have allocated on the gpu.\n",
-      "2985  */\n",
-      "2986 PyObject *\n",
-      "2987 GetTheanoAllocInfo(PyObject* _unused, PyObject* dummy)\n",
-      "2988 {\n",
-      "2989     PyObject* a = PyLong_FromSize_t(_allocated_size);\n",
-      "2990     PyObject* b = PyLong_FromSize_t(_max_allocated_size);\n",
-      "2991 \n",
-      "2992     PyObject* tuple = PyTuple_New(2);\n",
-      "2993     PyTuple_SetItem(tuple, 0, a);\n",
-      "2994     PyTuple_SetItem(tuple, 1, b);\n",
-      "2995     return tuple;\n",
-      "2996 }\n",
-      "2997 #endif\n",
-      "2998 \n",
-      "2999 static PyGetSetDef CudaNdarray_getset[] = {\n",
-      "3000     {\"shape\",\n",
-      "3001         (getter)CudaNdarray_get_shape,\n",
-      "3002         (setter)CudaNdarray_set_shape,\n",
-      "3003         \"shape of this ndarray (tuple)\",\n",
-      "3004         NULL},\n",
-      "3005     {\"_strides\",\n",
-      "3006         (getter)CudaNdarray_get_strides,\n",
-      "3007         (setter)CudaNdarray_set_strides,\n",
-      "3008         \"data pointer strides (in elements)\",\n",
-      "3009         NULL},\n",
-      "3010     {\"strides\",\n",
-      "3011         (getter)CudaNdarray_get_strides,\n",
-      "3012         (setter)CudaNdarray_set_strides,\n",
-      "3013         \"data pointer strides (in elements)\",\n",
-      "3014         NULL},\n",
-      "3015     //gpudata is needed to allow calling pycuda fct with CudaNdarray input.\n",
-      "3016     {\"gpudata\",\n",
-      "3017         (getter)CudaNdarray_get_dev_data,\n",
-      "3018         NULL,\n",
-      "3019         \"device data pointer\",\n",
-      "3020         NULL},\n",
-      "3021     {\"_dev_data\",\n",
-      "3022         (getter)CudaNdarray_get_dev_data,\n",
-      "3023         (setter)CudaNdarray_set_dev_data,\n",
-      "3024         \"device data pointer\",\n",
-      "3025         NULL},\n",
-      "3026     {\"dtype\",\n",
-      "3027         (getter)CudaNdarray_get_dtype,\n",
-      "3028         NULL,\n",
-      "3029         \"The dtype of the element. Now always float32\",\n",
-      "3030         NULL},\n",
-      "3031     {\"size\",\n",
-      "3032         (getter)CudaNdarray_SIZE_Object,\n",
-      "3033         NULL,\n",
-      "3034         \"The number of elements in this object.\",\n",
-      "3035         NULL},\n",
-      "3036     //mem_size is neede for pycuda.elementwise.ElementwiseKernel Why do they use size and mem_size of the same value?\n",
-      "3037     {\"mem_size\",\n",
-      "3038         (getter)CudaNdarray_SIZE_Object,\n",
-      "3039         NULL,\n",
-      "3040         \"The number of elements in this object.\",\n",
-      "3041         NULL},\n",
-      "3042     {\"ndim\",\n",
-      "3043         (getter)CudaNdarray_get_ndim,\n",
-      "3044         NULL,\n",
-      "3045         \"The number of dimensions in this object.\",\n",
-      "3046         NULL},\n",
-      "3047     {\"base\",\n",
-      "3048         (getter)CudaNdarray_get_base,\n",
-      "3049         NULL,\n",
-      "3050         \"If this ndarray is a view, base is the original ndarray.\",\n",
-      "3051         NULL},\n",
-      "3052 \n",
-      "3053     {NULL, NULL, NULL, NULL}  /* Sentinel */\n",
-      "3054 };\n",
-      "3055 \n",
-      "3056 PyObject *CudaNdarray_repr(PyObject *self)\n",
-      "3057 {\n",
-      "3058     CudaNdarray *object = (CudaNdarray *)self;\n",
-      "3059     PyObject * np_object = CudaNdarray_CreateArrayObj(object);\n",
-      "3060     PyObject * str = PyObject_Str((PyObject *) np_object);\n",
-      "3061     char * cstr = PyString_AsString(str);\n",
-      "3062     PyObject * out = PyString_FromFormat(\"%s%s%s\",\n",
-      "3063                         \"CudaNdarray(\",\n",
-      "3064                         cstr,\n",
-      "3065                         \")\");\n",
-      "3066     Py_DECREF(str);\n",
-      "3067     Py_DECREF(np_object);\n",
-      "3068     #if PY_MAJOR_VERSION >= 3\n",
-      "3069     // In Python 3 PyString_FromFormat return a Bytes object\n",
-      "3070     PyObject* out2 = PyObject_Str(out);\n",
-      "3071     Py_DECREF(out);\n",
-      "3072     return out2;\n",
-      "3073     #endif\n",
-      "3074     return out;\n",
-      "3075 }\n",
-      "3076 \n",
-      "3077 static PyTypeObject CudaNdarrayType =\n",
-      "3078 {\n",
-      "3079 #if PY_MAJOR_VERSION >= 3\n",
-      "3080     PyVarObject_HEAD_INIT(NULL, 0)\n",
-      "3081 #else\n",
-      "3082     PyObject_HEAD_INIT(NULL)\n",
-      "3083     0,                         /*ob_size*/\n",
-      "3084 #endif\n",
-      "3085     \"CudaNdarray\",             /*tp_name*/\n",
-      "3086     sizeof(CudaNdarray),       /*tp_basicsize*/\n",
-      "3087     0,                         /*tp_itemsize*/\n",
-      "3088     (destructor)CudaNdarray_dealloc, /*tp_dealloc*/\n",
-      "3089     0,                         /*tp_print*/\n",
-      "3090     0,                         /*tp_getattr*/\n",
-      "3091     0,                         /*tp_setattr*/\n",
-      "3092     0,                         /*tp_compare*/\n",
-      "3093     CudaNdarray_repr,          /*tp_repr*/\n",
-      "3094     &CudaNdarrayNumberMethods, /*tp_as_number*/\n",
-      "3095     0,                         /*tp_as_sequence*/\n",
-      "3096     &CudaNdarrayMappingMethods,/*tp_as_mapping*/\n",
-      "3097     0,                         /*tp_hash */\n",
-      "3098     0,                         /*tp_call*/\n",
-      "3099     0,                         /*tp_str*/\n",
-      "3100     0,                         /*tp_getattro*/\n",
-      "3101     0,                         /*tp_setattro*/\n",
-      "3102     0,                         /*tp_as_buffer*/\n",
-      "3103 #if PY_MAJOR_VERSION >= 3\n",
-      "3104     // Py_TPFLAGS_CHECKTYPES is always true and was removed in Python 3.\n",
-      "3105     Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/\n",
-      "3106 #else\n",
-      "3107     Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_CHECKTYPES, /*tp_flags*/\n",
-      "3108 #endif\n",
-      "3109     \"CudaNdarray objects\",     /* tp_doc */\n",
-      "3110     0,                         /* tp_traverse */\n",
-      "3111     0,                         /* tp_clear */\n",
-      "3112     0,                         /* tp_richcompare */\n",
-      "3113     0,                         /* tp_weaklistoffset */\n",
-      "3114     0,                         /* tp_iter */\n",
-      "3115     0,                         /* tp_iternext */\n",
-      "3116     CudaNdarray_methods,       /* tp_methods */\n",
-      "3117     CudaNdarray_members,       /* tp_members */\n",
-      "3118     CudaNdarray_getset,        /* tp_getset */\n",
-      "3119     0,                         /* tp_base */\n",
-      "3120     0,                         /* tp_dict */\n",
-      "3121     0,                         /* tp_descr_get */\n",
-      "3122     0,                         /* tp_descr_set */\n",
-      "3123     0,                         /* tp_dictoffset */\n",
-      "3124     (initproc)CudaNdarray_init,/* tp_init */\n",
-      "3125     0,                         /* tp_alloc */\n",
-      "3126     CudaNdarray_new,           /* tp_new */\n",
-      "3127 };\n",
-      "3128 \n",
-      "3129 static __global__ void get_gpu_ptr_size(int* dst)\n",
-      "3130 {\n",
-      "3131     dst[0] = sizeof(float*);\n",
-      "3132     dst[1] = sizeof(int);\n",
-      "3133 }\n",
-      "3134 \n",
-      "3135 PyObject *\n",
-      "3136 CudaNdarray_ptr_int_size(PyObject* _unused, PyObject* args)\n",
-      "3137 {\n",
-      "3138     int *gpu_data = (int*)device_malloc(sizeof(int)*2);\n",
-      "3139     if(gpu_data == NULL){\n",
-      "3140         return NULL;\n",
-      "3141     }\n",
-      "3142     get_gpu_ptr_size<<<1,1>>>(gpu_data);\n",
-      "3143 \n",
-      "3144     cudaError_t cudaErr = cudaGetLastError();\n",
-      "3145     if (cudaSuccess != cudaErr){\n",
-      "3146 \n",
-      "3147         device_free(gpu_data);\n",
-      "3148         return PyErr_Format(PyExc_RuntimeError,\n",
-      "3149                             \"CudaNdarray_ptr_int_size: error when calling the gpu code. (%s)\",\n",
-      "3150                             cudaGetErrorString(cudaErr));\n",
-      "3151     }\n",
-      "3152 \n",
-      "3153     // Transfer the result to cpu\n",
-      "3154     int gpu_sizes[] = {-1,-1};\n",
-      "3155     cublasStatus_t err;\n",
-      "3156     err = cublasGetVector(2, sizeof(int), gpu_data, 1, gpu_sizes, 1);\n",
-      "3157     device_free(gpu_data);\n",
-      "3158 \n",
-      "3159     if (CUBLAS_STATUS_SUCCESS != err){\n",
-      "3160         PyErr_SetString(PyExc_RuntimeError, \"error copying data to from memory\");\n",
-      "3161         return NULL;\n",
-      "3162     }\n",
-      "3163     return Py_BuildValue(\"iiii\", (int) gpu_sizes[0], (int)sizeof(float*),\n",
-      "3164                          (int)sizeof(int), (int) gpu_sizes[1]);\n",
-      "3165 }\n",
-      "3166 \n",
-      "3167 static int cublas_init();\n",
-      "3168 static void cublas_shutdown();\n",
-      "3169 // Initialize the gpu.\n",
-      "3170 // Takes two optional parameters, the device number and if we should use cnmem.\n",
-      "3171 // If the device number is provided, it sets that device to be the active device.\n",
-      "3172 // If not provided (usually just to test whether the gpu is available at all),\n",
-      "3173 // it does not set an active device.\n",
-      "3174 // Raises EnvironmentError or ValueError (as appropriate) if the initialization failed.\n",
-      "3175 // cnmem is threaded like a bool. If converted to 0, don't use cnmem. Otherwise, use it.\n",
-      "3176 PyObject *\n",
-      "3177 CudaNdarray_gpu_init(PyObject* _unused, PyObject* args)\n",
-      "3178 {\n",
-      "3179     int card_nb = 0;\n",
-      "3180     int card_number_provided = 1;\n",
-      "3181     float cnmem = 0; // Theano flag lib.cnmem\n",
-      "3182     // if we're given something wildly invalid, this will throw a TypeError\n",
-      "3183     if(!PyArg_ParseTuple(args, \"|if\", &card_nb, &cnmem))\n",
-      "3184         return NULL;\n",
-      "3185     if(cnmem)\n",
-      "3186         g_use_cnmem = true;\n",
-      "3187 \n",
-      "3188     if(PyTuple_Size(args) == 0) {\n",
-      "3189         card_number_provided = 0;\n",
-      "3190         card_nb = 0;\n",
-      "3191     }\n",
-      "3192 \n",
-      "3193     int deviceCount;\n",
-      "3194     cudaError err = cudaGetDeviceCount(&deviceCount);\n",
-      "3195     if(cudaSuccess != err) {\n",
-      "3196         return PyErr_Format(PyExc_EnvironmentError,\n",
-      "3197                             \"Unable to get the number of gpus available: %s\",\n",
-      "3198                             cudaGetErrorString(cudaGetLastError()));\n",
-      "3199     }\n",
-      "3200 \n",
-      "3201     // as soon as the first successful call to a cuda* function is made, a\n",
-      "3202     // gpu context has been created\n",
-      "3203     g_gpu_context_active = 1;\n",
-      "3204 \n",
-      "3205     if(deviceCount <= 0) {\n",
-      "3206         return PyErr_Format(PyExc_EnvironmentError,\n",
-      "3207                             \"Can't use the GPU, no devices support CUDA\");\n",
-      "3208     }\n",
-      "3209     if(card_number_provided && (card_nb < 0 || card_nb > (deviceCount - 1))) {\n",
-      "3210         return PyErr_Format(PyExc_ValueError,\n",
-      "3211                             \"Bad device number %d. Only %d devices available.\",\n",
-      "3212                             card_nb,\n",
-      "3213                             deviceCount);\n",
-      "3214     }\n",
-      "3215 \n",
-      "3216     cudaDeviceProp deviceProp;\n",
-      "3217     err = cudaGetDeviceProperties(&deviceProp, card_nb);\n",
-      "3218     if(cudaSuccess != err) {\n",
-      "3219         return PyErr_Format(PyExc_EnvironmentError,\n",
-      "3220                             \"Unable to get properties of gpu %i: %s\",\n",
-      "3221                             card_nb,\n",
-      "3222                             cudaGetErrorString(cudaGetLastError()));\n",
-      "3223     }\n",
-      "3224 \n",
-      "3225     if(deviceProp.major == 9999 && deviceProp.minor == 9999 ){\n",
-      "3226         return PyErr_Format(PyExc_EnvironmentError,\n",
-      "3227                             \"There is no device that supports CUDA\");\n",
-      "3228     }\n",
-      "3229 \n",
-      "3230     if(card_number_provided) {\n",
-      "3231         err = cudaSetDevice(card_nb);\n",
-      "3232         if(cudaSuccess != err) {\n",
-      "3233             return PyErr_Format(PyExc_EnvironmentError,\n",
-      "3234                                 \"Unable to set device %i: %s\",\n",
-      "3235                                 card_nb,\n",
-      "3236                                 cudaGetErrorString(cudaGetLastError()));\n",
-      "3237         }\n",
-      "3238         if (cublas_init() == -1)\n",
-      "3239             return NULL;\n",
-      "3240     }\n",
-      "3241     if(card_number_provided && g_use_cnmem) {\n",
-      "3242         size_t mem = 0;\n",
-      "3243         if (cnmem > 1)\n",
-      "3244             mem = cnmem * 1024 * 1024;\n",
-      "3245         else{\n",
-      "3246             // Clip to 95% to let memory for the driver.\n",
-      "3247             // 98% didn't worked in some cases.\n",
-      "3248             if (cnmem > .95){\n",
-      "3249                 cnmem = .95;\n",
-      "3250             }\n",
-      "3251             size_t free = 0, total = 0;\n",
-      "3252             cudaError_t err = cudaMemGetInfo(&free, &total);\n",
-      "3253             if (err != cudaSuccess){\n",
-      "3254                 // Clear the error flag, cudaMemGetInfo doesn't do it.\n",
-      "3255                 // Currently this returns the same thing as err, but if in future\n",
-      "3256                 // it returns something else I still don't see why we should ignore\n",
-      "3257                 // it.  All we want to do here is reset the flag.\n",
-      "3258                 cudaGetLastError();\n",
-      "3259                 PyErr_Format(PyExc_RuntimeError,\n",
-      "3260                              \"Error while getting memory info about the gpu: %s\",\n",
-      "3261                              cudaGetErrorString(err));\n",
-      "3262                 return NULL;\n",
-      "3263             }\n",
-      "3264             mem = total * cnmem;\n",
-      "3265         }\n",
-      "3266         if(initCnmem(card_number_provided, card_nb, mem) == -1){\n",
-      "3267             return NULL;\n",
-      "3268         }\n",
-      "3269     }\n",
-      "3270 \n",
-      "3271     Py_INCREF(Py_None);\n",
-      "3272     return Py_None;\n",
-      "3273 }\n",
-      "3274 \n",
-      "3275 PyObject *\n",
-      "3276 CudaNdarray_active_device_number(PyObject* _unused, PyObject* _unused_args) {\n",
-      "3277     // NB: No cuda error checking here; keeps things simple, and it's not\n",
-      "3278     // really necessary.\n",
-      "3279     int currentDevice;\n",
-      "3280     cudaGetDevice(&currentDevice);\n",
-      "3281     return PyInt_FromLong(currentDevice);\n",
-      "3282 }\n",
-      "3283 \n",
-      "3284 PyObject *\n",
-      "3285 CudaNdarray_active_device_name(PyObject* _unused, PyObject* _unused_args) {\n",
-      "3286     // NB: No cuda error checking here; keeps things simple, and it's not\n",
-      "3287     // really necessary.\n",
-      "3288     int currentDevice;\n",
-      "3289     cudaGetDevice(&currentDevice);\n",
-      "3290 \n",
-      "3291     cudaDeviceProp deviceProp;\n",
-      "3292     cudaGetDeviceProperties(&deviceProp, currentDevice);\n",
-      "3293     return PyString_FromString(deviceProp.name);\n",
-      "3294 }\n",
-      "3295 \n",
-      "3296 PyObject *\n",
-      "3297 CudaNdarray_gpu_shutdown(PyObject* _unused, PyObject* _unused_args) {\n",
-      "3298     // Don't handle errors here\n",
-      "3299     cublas_shutdown();\n",
-      "3300     g_gpu_context_active = 0; // context has now been closed down\n",
-      "3301     if(g_use_cnmem) {\n",
-      "3302         cnmemStatus_t status = cnmemFinalize();\n",
-      "3303         if(status != CNMEM_STATUS_SUCCESS) {\n",
-      "3304             fprintf(stderr, \"CudaNdarray_gpu_shutdown: cnmemFinalize failed! Reason=%s\\n\",\n",
-      "3305                     cnmemGetErrorString(status));\n",
-      "3306             if(status == CNMEM_STATUS_CUDA_ERROR) {\n",
-      "3307                 fprintf(stderr, \"  Cuda-Reason=%s\\n\",\n",
-      "3308                         cudaGetErrorString(cudaGetLastError()));\n",
-      "3309             }\n",
-      "3310         }\n",
-      "3311     }\n",
-      "3312 \n",
-      "3313     Py_INCREF(Py_None);\n",
-      "3314     return Py_None;\n",
-      "3315 }\n",
-      "3316 \n",
-      "3317 /*\n",
-      "3318  * This function is tested in theano/misc/test_pycuda_theano_simple.py\n",
-      "3319  */\n",
-      "3320 PyObject *\n",
-      "3321 CudaNdarray_from_gpu_pointer(PyObject* _unused, PyObject* args)\n",
-      "3322 {\n",
-      "3323     int verbose = 0;\n",
-      "3324     PyObject *gpu_ptr = NULL;\n",
-      "3325     PyObject *shapes = NULL;\n",
-      "3326     PyObject *strides = NULL;\n",
-      "3327     PyObject *base = NULL;\n",
-      "3328     PyObject *rval = NULL;\n",
-      "3329 \n",
-      "3330     //args should consist of 3 python objects\n",
-      "3331     //The first is the gpu ptr\n",
-      "3332     //The second if the shape\n",
-      "3333     //The third if the strides\n",
-      "3334     if (! PyArg_ParseTuple(args, \"OOOO\", &gpu_ptr, &shapes, &strides, &base))\n",
-      "3335         return NULL;\n",
-      "3336 \n",
-      "3337     if (verbose) printf(\"In CudaNdarray_from_gpu_pointer\\n\");\n",
-      "3338     if (!PyLong_Check(gpu_ptr))\n",
-      "3339     {\n",
-      "3340         PyErr_Format(PyExc_Exception, \"CudaNdarray_from_gpu_pointer: The gpu pointor is not an long\");\n",
-      "3341         return NULL;\n",
-      "3342     }\n",
-      "3343 \n",
-      "3344     Py_ssize_t nd =  PyObject_Length(shapes);\n",
-      "3345     if (nd < 0)\n",
-      "3346     {\n",
-      "3347         PyErr_SetString(PyExc_TypeError, \"CudaNdarray_from_gpu_pointer: Couldn't get length of second argument\");\n",
-      "3348         return NULL;\n",
-      "3349     }\n",
-      "3350     Py_ssize_t nd_stride =  PyObject_Length(strides);\n",
-      "3351     if (nd_stride < 0)\n",
-      "3352     {\n",
-      "3353         PyErr_SetString(PyExc_TypeError, \"CudaNdarray_from_gpu_pointer: Couldn't get length of third argument\");\n",
-      "3354         return NULL;\n",
-      "3355     }\n",
-      "3356 \n",
-      "3357     if (nd != nd_stride)\n",
-      "3358     {\n",
-      "3359         PyErr_SetString(PyExc_TypeError, \"CudaNdarray_from_gpu_pointer: We need the same number of shapes and strides\");\n",
-      "3360         return NULL;\n",
-      "3361     }\n",
-      "3362 \n",
-      "3363     rval = CudaNdarray_New();\n",
-      "3364 \n",
-      "3365     if (CudaNdarray_set_nd((CudaNdarray *)rval, nd))\n",
-      "3366     {\n",
-      "3367         //CudaNdarray_set_nd set the error msg\n",
-      "3368         return NULL;\n",
-      "3369     }\n",
-      "3370     // set gpu pointeur\n",
-      "3371     assert(((CudaNdarray *)rval)->data_allocated == 0);\n",
-      "3372     if (CudaNdarray_set_device_data((CudaNdarray *)rval, (float *)PyInt_AsLong(gpu_ptr), base))\n",
-      "3373     {\n",
-      "3374         PyErr_SetString(PyExc_TypeError, \"CudaNdarray_from_gpu_pointer: Error while setting the gpu pointor\");\n",
-      "3375         return NULL;\n",
-      "3376 \n",
-      "3377     }\n",
-      "3378 \n",
-      "3379     // Set dims and strides\n",
-      "3380     for (int i = nd-1; i >= 0; --i)\n",
-      "3381     {\n",
-      "3382         PyObject * idx = PyLong_FromLong(i);\n",
-      "3383         if (idx == NULL)\n",
-      "3384         {\n",
-      "3385             PyErr_SetString(PyExc_Exception, \"CudaNdarray_from_gpu_pointer: Couldn't make long object to loop over list/tuple\");\n",
-      "3386             return NULL;\n",
-      "3387         }\n",
-      "3388         PyObject* dim_ = PyObject_GetItem(shapes, idx);\n",
-      "3389         PyObject* strd_ = PyObject_GetItem(strides, idx);\n",
-      "3390         if (!PyInt_Check(dim_))\n",
-      "3391         {\n",
-      "3392             PyErr_Format(PyExc_Exception, \"CudaNdarray_from_gpu_pointer: shapes[%d] is not an int\", i);\n",
-      "3393             return NULL;\n",
-      "3394         }\n",
-      "3395         if (!PyInt_Check(strd_))\n",
-      "3396         {\n",
-      "3397             PyErr_Format(PyExc_Exception, \"CudaNdarray_from_gpu_pointer: strides[%d] is not an int\", i);\n",
-      "3398             return NULL;\n",
-      "3399         }\n",
-      "3400         int dim = PyInt_AsLong(dim_);\n",
-      "3401         int strd = PyInt_AsLong(strd_);\n",
-      "3402         CudaNdarray_set_stride((CudaNdarray *)rval, i, strd);\n",
-      "3403         CudaNdarray_set_dim((CudaNdarray *)rval, i, dim);\n",
-      "3404         Py_DECREF(idx);\n",
-      "3405         Py_DECREF(dim_);\n",
-      "3406         Py_DECREF(strd_);\n",
-      "3407     }\n",
-      "3408     if (verbose) printf(\"CudaNdarray_from_gpu_pointer normal return\\n\");\n",
-      "3409     return rval;\n",
-      "3410 }\n",
-      "3411 \n",
-      "3412 PyObject *\n",
-      "3413 CudaNdarray_Dot(PyObject* _unused, PyObject* args)\n",
-      "3414 {\n",
-      "3415     PyObject *l=NULL;\n",
-      "3416     PyObject *r=NULL;\n",
-      "3417     PyObject * rval = NULL;\n",
-      "3418 \n",
-      "3419     //args should consist of two python objects (\"OO\")\n",
-      "3420     if (! PyArg_ParseTuple(args, \"OO\", &l, &r))\n",
-      "3421         return NULL;\n",
-      "3422 \n",
-      "3423     if (!CudaNdarray_Check(l) || !CudaNdarray_Check(r))\n",
-      "3424     {\n",
-      "3425         PyErr_SetString(PyExc_TypeError, \"CudaNdarray arguments required \");\n",
-      "3426         goto CudaNdarray_dot_fail;\n",
-      "3427     }\n",
-      "3428     if (((CudaNdarray*)l)->nd != 2)\n",
-      "3429     {\n",
-      "3430         PyErr_SetString(PyExc_TypeError, \"need 2d CudaNdarray arg for now\");\n",
-      "3431         goto CudaNdarray_dot_fail;\n",
-      "3432     }\n",
-      "3433     if (((CudaNdarray*)r)->nd != 2)\n",
-      "3434     {\n",
-      "3435         PyErr_SetString(PyExc_TypeError, \"need 2d CudaNdarray arg for now\");\n",
-      "3436         goto CudaNdarray_dot_fail;\n",
-      "3437     }\n",
-      "3438     rval = CudaNdarray_New();\n",
-      "3439     if (!rval)\n",
-      "3440     {\n",
-      "3441         goto CudaNdarray_dot_fail;\n",
-      "3442     }\n",
-      "3443     int dims[2];\n",
-      "3444     dims[0] = CudaNdarray_HOST_DIMS((CudaNdarray*)l)[0];\n",
-      "3445     dims[1] = CudaNdarray_HOST_DIMS((CudaNdarray*)r)[1];\n",
-      "3446     if (CudaNdarray_alloc_contiguous((CudaNdarray*)rval, 2, dims))\n",
-      "3447     {\n",
-      "3448         goto CudaNdarray_dot_fail;\n",
-      "3449     }\n",
-      "3450     if (CudaNdarray_gemm(1.0, (CudaNdarray*)l, (CudaNdarray*)r, 0.0, (CudaNdarray*)rval))\n",
-      "3451     {\n",
-      "3452         goto CudaNdarray_dot_fail;\n",
-      "3453     }\n",
-      "3454 \n",
-      "3455     return rval;\n",
-      "3456 \n",
-      "3457     CudaNdarray_dot_fail:\n",
-      "3458     Py_XDECREF(rval);\n",
-      "3459     return NULL;\n",
-      "3460 }\n",
-      "3461 \n",
-      "3462 static PyObject *\n",
-      "3463 filter(PyObject* __unsed_self, PyObject *args) // args = (data, broadcastable, strict, storage)\n",
-      "3464 {\n",
-      "3465     /*\n",
-      "3466      * TODO: DOC what this function should do in the various cases of\n",
-      "3467      * What is 'strict' supposed to mean in the context of this function?\n",
-      "3468      * What do we do with input that could be interpreted as matching the broadcastable pattern in strict vs. non-strict cases?\n",
-      "3469      *\n",
-      "3470      */\n",
-      "3471     PyObject *py_data=NULL;\n",
-      "3472     PyArrayObject * data = NULL;\n",
-      "3473     int strict = 0;\n",
-      "3474     PyObject * broadcastable=NULL;\n",
-      "3475     PyObject * storage=NULL;\n",
-      "3476     CudaNdarray * rval=NULL;\n",
-      "3477 \n",
-      "3478     //Python object references which are provided to the caller are borrowed references\n",
-      "3479     if (!PyArg_ParseTuple(args, \"OOiO\", &py_data, &broadcastable, &strict, &storage)) return NULL;\n",
-      "3480 \n",
-      "3481     if (!PyTuple_Check(broadcastable)){\n",
-      "3482         PyErr_SetString(PyExc_TypeError, \"broadcastable arg should be a tuple of int.\");\n",
-      "3483         return NULL;\n",
-      "3484     }\n",
-      "3485     Py_INCREF(py_data);\n",
-      "3486     Py_INCREF(broadcastable);\n",
-      "3487 \n",
-      "3488     CudaNdarray * cnda = (CudaNdarray*)py_data;\n",
-      "3489 \n",
-      "3490     if (strict || CudaNdarray_Check(py_data))\n",
-      "3491     {\n",
-      "3492         //TODO: support non-strict \"casting\" from a vt to the broadcastable/type/size that we need.\n",
-      "3493         if (!CudaNdarray_Check(py_data))\n",
-      "3494         {\n",
-      "3495             Py_DECREF(py_data);\n",
-      "3496             Py_DECREF(broadcastable);\n",
-      "3497             PyErr_SetString(PyExc_TypeError, \"strict mode requires CudaNdarray\");\n",
-      "3498             return NULL;\n",
-      "3499         }\n",
-      "3500         if (cnda->nd != PyTuple_Size(broadcastable))\n",
-      "3501         {\n",
-      "3502             Py_DECREF(py_data);\n",
-      "3503             Py_DECREF(broadcastable);\n",
-      "3504             PyErr_Format(PyExc_TypeError, \"Wrong rank: %i vs %li\", cnda->nd, (long)PyTuple_Size(broadcastable));\n",
-      "3505             return NULL;\n",
-      "3506         }\n",
-      "3507         for (int i = 0; i < cnda->nd; ++i)\n",
-      "3508         {\n",
-      "3509             if ((CudaNdarray_HOST_DIMS(cnda)[i] > 1) && PyInt_AsLong(PyTuple_GetItem(broadcastable, Py_ssize_t(i))))\n",
-      "3510             {\n",
-      "3511                 PyErr_Format(PyExc_TypeError, \"Non-unit size in broadcastable vt dimension %i\", i);\n",
-      "3512                 Py_DECREF(py_data);\n",
-      "3513                 Py_DECREF(broadcastable);\n",
-      "3514                 return NULL;\n",
-      "3515             }else if (CudaNdarray_HOST_DIMS(cnda)[i] == 1 && CudaNdarray_HOST_STRIDES(cnda)[i] != 0){\n",
-      "3516                 PyErr_Format(PyExc_TypeError, \"Non-zeros strides(%d) on dimension %d of size 1\",\n",
-      "3517                              CudaNdarray_HOST_STRIDES(cnda)[i], i);\n",
-      "3518                 Py_DECREF(py_data);\n",
-      "3519                 Py_DECREF(broadcastable);\n",
-      "3520                 return NULL;\n",
-      "3521             }\n",
-      "3522         }\n",
-      "3523         Py_DECREF(broadcastable);\n",
-      "3524         return py_data;\n",
-      "3525     }\n",
-      "3526     else\n",
-      "3527     {\n",
-      "3528         data = (PyArrayObject*)PyArray_FromObject(py_data, REAL_TYPENUM, PyTuple_Size(broadcastable), PyTuple_Size(broadcastable));\n",
-      "3529         if (!data)\n",
-      "3530         {\n",
-      "3531             //err message already defined\n",
-      "3532             Py_DECREF(py_data);\n",
-      "3533             Py_DECREF(broadcastable);\n",
-      "3534             return NULL;\n",
-      "3535         }\n",
-      "3536         for (int i = 0; i < PyArray_NDIM(data); ++i)\n",
-      "3537         {\n",
-      "3538             if ((PyArray_DIMS(data)[i] > 1) && PyInt_AsLong(PyTuple_GetItem(broadcastable, Py_ssize_t(i))))\n",
-      "3539             {\n",
-      "3540                 PyErr_Format(PyExc_TypeError, \"Non-unit size in broadcastable dimension %i\", i);\n",
-      "3541                 Py_DECREF(data);\n",
-      "3542                 Py_DECREF(py_data);\n",
-      "3543                 Py_DECREF(broadcastable);\n",
-      "3544                 return NULL;\n",
-      "3545             }\n",
-      "3546         }\n",
-      "3547         if (storage && CudaNdarray_Check(storage))\n",
-      "3548         {\n",
-      "3549             rval = (CudaNdarray*) storage;\n",
-      "3550             Py_INCREF(rval);\n",
-      "3551         }\n",
-      "3552         else\n",
-      "3553         {\n",
-      "3554             rval = (CudaNdarray*) CudaNdarray_New();\n",
-      "3555         }\n",
-      "3556         if (rval)\n",
-      "3557         {\n",
-      "3558             if (CudaNdarray_CopyFromArray(rval, data))\n",
-      "3559             {\n",
-      "3560                 Py_DECREF(rval);\n",
-      "3561                 rval = NULL;\n",
-      "3562             }\n",
-      "3563         }\n",
-      "3564         Py_DECREF(data);\n",
-      "3565         Py_DECREF(py_data);\n",
-      "3566         Py_DECREF(broadcastable);\n",
-      "3567         return (PyObject*)rval;\n",
-      "3568     }\n",
-      "3569 }\n",
-      "3570 \n",
-      "3571 //TODO-- CudaNdarray_Dot and CudaNdarray_active_device_name are following different capitalization conventions.\n",
-      "3572 //       Pick one and standardize it, this file is already annoying enough to grep through\n",
-      "3573 static PyMethodDef module_methods[] = {\n",
-      "3574     {\"dimshuffle\", CudaNdarray_Dimshuffle, METH_VARARGS, \"Returns the dimshuffle of a CudaNdarray.\"},\n",
-      "3575     {\"dot\", CudaNdarray_Dot, METH_VARARGS, \"Returns the matrix product of two CudaNdarray arguments.\"},\n",
-      "3576     {\"gpu_init\", CudaNdarray_gpu_init, METH_VARARGS, \"Select the gpu card to use; also usable to test whether CUDA is available.\"},\n",
-      "3577     {\"select_a_gpu\", CudaNdarray_select_a_gpu, METH_NOARGS, \"Call this method if you want to select a GPU before gpu_init call and let the driver choose the GPU.\"},\n",
-      "3578     {\"active_device_name\", CudaNdarray_active_device_name, METH_VARARGS, \"Get the name of the active device.\"},\n",
-      "3579     {\"active_device_number\", CudaNdarray_active_device_number, METH_VARARGS, \"Get the number of the active device.\"},\n",
-      "3580     {\"gpu_shutdown\", CudaNdarray_gpu_shutdown, METH_VARARGS, \"Shut down the gpu.\"},\n",
-      "3581     {\"device_properties\", GetDeviceProperties, METH_VARARGS, \"Return a dictionary with the device properties.\"},\n",
-      "3582     {\"mem_info\", GetDeviceMemInfo, METH_NOARGS, \"Return a tuple with the free and total memory on the gpu in bytes.\"},\n",
-      "3583 #if COMPUTE_GPU_MEM_USED\n",
-      "3584     {\"theano_allocated\", GetTheanoAllocInfo, METH_NOARGS, \"Return the size in bytes of memory Theano currently have allocated on the gpu.\"},\n",
-      "3585 #endif\n",
-      "3586     {\"ptr_int_size\", CudaNdarray_ptr_int_size, METH_VARARGS, \"Return a tuple with the size of gpu pointer, cpu pointer and int in bytes.\"},\n",
-      "3587     {\"filter\", filter, METH_VARARGS, \"filter(obj, broadcastable, strict, storage) returns a CudaNdarray initialized to obj if it matches the constraints of broadcastable.  strict=True prevents any numeric casting. If storage is a CudaNdarray it may be overwritten and used as the return value.\"},\n",
-      "3588     {\"outstanding_mallocs\", outstanding_mallocs, METH_VARARGS, \"how many more mallocs have been called than free's\"},\n",
-      "3589     {\"from_gpu_pointer\", CudaNdarray_from_gpu_pointer, METH_VARARGS, \"Used to create a CudaNdarray from already allocated memory on the gpu.(example by pycuda)\"},\n",
-      "3590     {\"synchronize\", CudaNdarray_synchronize, METH_NOARGS, \"Used to synchronize the device\"},\n",
-      "3591     {\"cublas_v2\", CudaNdarray_cublasv2, METH_NOARGS,\n",
-      "3592      \"Used to know if this version of cuda_ndarray is linked with cublas v2.\"},\n",
-      "3593     {NULL, NULL, NULL, NULL}  /* Sentinel */\n",
-      "3594 };\n",
-      "3595 \n",
-      "3596 #define CNDA_MOD_NAME \"cuda_ndarray\"\n",
-      "3597 #define CNDA_DOCSTRING \"CUDA implementation of a numpy ndarray-like object.\"\n",
-      "3598 \n",
-      "3599 #if PY_MAJOR_VERSION == 3\n",
-      "3600 static struct PyModuleDef cuda_ndarray_moduledef =\n",
-      "3601 {\n",
-      "3602     PyModuleDef_HEAD_INIT,\n",
-      "3603     CNDA_MOD_NAME,\n",
-      "3604     CNDA_DOCSTRING,\n",
-      "3605     -1,     /* size of per-interpreter state of the module,\n",
-      "3606                or -1 if the module keeps state in global variables. */\n",
-      "3607     module_methods\n",
-      "3608 };\n",
-      "3609 \n",
-      "3610 PyMODINIT_FUNC\n",
-      "3611 PyInit_cuda_ndarray(void)\n",
-      "3612 #else\n",
-      "3613 PyMODINIT_FUNC\n",
-      "3614 initcuda_ndarray(void)\n",
-      "3615 #endif\n",
-      "3616 {\n",
-      "3617     import_array();\n",
-      "3618 \n",
-      "3619     PyObject* m;\n",
-      "3620 \n",
-      "3621     if (PyType_Ready(&CudaNdarrayType) < 0) {\n",
-      "3622 #if PY_MAJOR_VERSION == 3\n",
-      "3623         return NULL;\n",
-      "3624 #else\n",
-      "3625         return;\n",
-      "3626 #endif\n",
-      "3627     }\n",
-      "3628 \n",
-      "3629 #if PY_MAJOR_VERSION == 3\n",
-      "3630     m = PyModule_Create(&cuda_ndarray_moduledef);\n",
-      "3631 #else\n",
-      "3632     m = Py_InitModule3(CNDA_MOD_NAME, module_methods, CNDA_DOCSTRING);\n",
-      "3633 #endif\n",
-      "3634 \n",
-      "3635     if (m == NULL) {\n",
-      "3636 #if PY_MAJOR_VERSION == 3\n",
-      "3637         return NULL;\n",
-      "3638 #else\n",
-      "3639         return;\n",
-      "3640 #endif\n",
-      "3641     }\n",
-      "3642 \n",
-      "3643     Py_INCREF(&CudaNdarrayType);\n",
-      "3644     PyModule_AddObject(m, \"CudaNdarray\", (PyObject *)&CudaNdarrayType);\n",
-      "3645 #if COMPUTE_GPU_MEM_USED\n",
-      "3646     for(int i=0;i<TABLE_SIZE;i++){\n",
-      "3647         _alloc_size_table[i].ptr=NULL;\n",
-      "3648         _alloc_size_table[i].size=0;\n",
-      "3649     }\n",
-      "3650 #endif\n",
-      "3651     //    cublasInit();\n",
-      "3652     //if (0&&CUBLAS_STATUS_SUCCESS != cublasGetError())\n",
-      "3653     //{\n",
-      "3654         //std::cerr << \"WARNING: initcuda_ndarray: error initializing device\\n\";\n",
-      "3655     //}\n",
-      "3656     if (0) //TODO: is this necessary?\n",
-      "3657     {\n",
-      "3658         int deviceId = 0; // TODO: what number goes here?\n",
-      "3659         cudaSetDevice(deviceId);\n",
-      "3660         cudaError_t err = cudaGetLastError();\n",
-      "3661         if( cudaSuccess != err)\n",
-      "3662         {\n",
-      "3663             std::cerr << \"Error in SetDevice:\" << cudaGetErrorString(err) << \"\\n\";\n",
-      "3664         }\n",
-      "3665     }\n",
-      "3666 \n",
-      "3667 #if PY_MAJOR_VERSION == 3\n",
-      "3668     return m;\n",
-      "3669 #endif\n",
-      "3670 }\n",
-      "3671 \n",
-      "3672 \n",
-      "3673 //////////////////////////////////////\n",
-      "3674 //\n",
-      "3675 // C API FOR CudaNdarray\n",
-      "3676 //\n",
-      "3677 //////////////////////////////////////\n",
-      "3678 \n",
-      "3679 int\n",
-      "3680 CudaNdarray_Check(const PyObject * ob)\n",
-      "3681 {\n",
-      "3682     //TODO: doesn't work with inheritance\n",
-      "3683     return CudaNdarray_CheckExact(ob);\n",
-      "3684 }\n",
-      "3685 int\n",
-      "3686 CudaNdarray_CheckExact(const PyObject * ob)\n",
-      "3687 {\n",
-      "3688     return ((Py_TYPE(ob) == &CudaNdarrayType) ? 1 : 0);\n",
-      "3689 }\n",
-      "3690 \n",
-      "3691 PyObject *\n",
-      "3692 CudaNdarray_New(int nd)\n",
-      "3693 {\n",
-      "3694     CudaNdarray *self = (CudaNdarray *)CudaNdarrayType.tp_alloc(&CudaNdarrayType, 0);\n",
-      "3695     if (self == NULL)\n",
-      "3696     {\n",
-      "3697         PyErr_SetString(PyExc_RuntimeError, \"CudaNdarray_New failed to allocate self\");\n",
-      "3698         return NULL;\n",
-      "3699     }\n",
-      "3700     CudaNdarray_null_init(self);\n",
-      "3701 \n",
-      "3702     if (nd == 0)\n",
-      "3703     {\n",
-      "3704         self->nd = 0;\n",
-      "3705     }\n",
-      "3706     else if (nd > 0)\n",
-      "3707     {\n",
-      "3708         if (CudaNdarray_set_nd(self, nd))\n",
-      "3709         {\n",
-      "3710             Py_DECREF(self);\n",
-      "3711             return NULL;\n",
-      "3712         }\n",
-      "3713     }\n",
-      "3714     ++_outstanding_mallocs[1];\n",
-      "3715     return (PyObject *)self;\n",
-      "3716 }\n",
-      "3717 \n",
-      "3718 \n",
-      "3719 \n",
-      "3720 //////////////////////////////\n",
-      "3721 //\n",
-      "3722 // Published helper functions\n",
-      "3723 //\n",
-      "3724 //////////////////////////////\n",
-      "3725 \n",
-      "3726 static int\n",
-      "3727 cublas_init()\n",
-      "3728 {\n",
-      "3729     cublasStatus_t err;\n",
-      "3730     err = cublasCreate(&handle);\n",
-      "3731     if (CUBLAS_STATUS_SUCCESS != err)\n",
-      "3732     {\n",
-      "3733         if(CUBLAS_STATUS_NOT_INITIALIZED == err)\n",
-      "3734             PyErr_SetString(PyExc_RuntimeError,\n",
-      "3735                             \"cublasCreate() returned this error \"\n",
-      "3736                             \"'the CUDA Runtime initialization failed'\");\n",
-      "3737         else if(CUBLAS_STATUS_ALLOC_FAILED == err)\n",
-      "3738             PyErr_SetString(PyExc_RuntimeError,\n",
-      "3739                             \"cublasCreate() returned this error \"\n",
-      "3740                             \"'the resources could not be allocated'\");\n",
-      "3741         else\n",
-      "3742             PyErr_SetString(PyExc_RuntimeError,\n",
-      "3743                             \"unknow error during returned by cublasCreate()\");\n",
-      "3744         return -1;\n",
-      "3745     }\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "3746     // Set the default stream as the one to execute on (default)\n",
-      "3747     cublasSetStream(handle, NULL);\n",
-      "3748     // Pointer to scalars are on the host (also default)\n",
-      "3749     cublasSetPointerMode(handle, CUBLAS_POINTER_MODE_HOST);\n",
-      "3750 #if CUDA_VERSION >= 5000\n",
-      "3751     // atomics can be used in kernels to speed up operations (not default)\n",
-      "3752     // This may lead to a slight variance from run to run in some operations\n",
-      "3753     cublasSetAtomicsMode(handle, CUBLAS_ATOMICS_ALLOWED);\n",
-      "3754 #endif\n",
-      "3755     return 0;\n",
-      "3756 }\n",
-      "3757 \n",
-      "3758 static void\n",
-      "3759 cublas_shutdown()\n",
-      "3760 {\n",
-      "3761     if (handle != NULL)\n",
-      "3762         cublasDestroy(handle);\n",
-      "3763     // No point in handling any errors here\n",
-      "3764     handle = NULL;\n",
-      "3765 }\n",
-      "3766 \n",
-      "3767 int\n",
-      "3768 CudaNdarray_CopyFromArray(CudaNdarray * self, PyArrayObject*obj)\n",
-      "3769 {\n",
-      "3770     int err = CudaNdarray_alloc_contiguous(self, PyArray_NDIM(obj),\n",
-      "3771                                            PyArray_DIMS(obj));\n",
-      "3772     if (err) {\n",
-      "3773         return err;\n",
-      "3774     }\n",
-      "3775 \n",
-      "3776     int typenum = PyArray_TYPE(obj);\n",
-      "3777     if (typenum != REAL_TYPENUM)\n",
-      "3778     {\n",
-      "3779         PyErr_SetString(PyExc_TypeError, \"can only copy from float arrays\");\n",
-      "3780         return -1;\n",
-      "3781     }\n",
-      "3782     assert( 4 ==  PyArray_ITEMSIZE(obj));\n",
-      "3783     PyArrayObject * py_src = (PyArrayObject *)PyArray_ContiguousFromAny(\n",
-      "3784         (PyObject*)obj, typenum, self->nd, self->nd);\n",
-      "3785     if (!py_src) {\n",
-      "3786         return -1;\n",
-      "3787     }\n",
-      "3788     npy_intp py_src_size = PyArray_SIZE(py_src);\n",
-      "3789     void *py_src_data = PyArray_DATA(py_src);\n",
-      "3790     cudaError_t cerr;\n",
-      "3791     CNDA_BEGIN_ALLOW_THREADS;\n",
-      "3792     cerr = cudaMemcpy(self->devdata, py_src_data,\n",
-      "3793                       py_src_size * sizeof(real),\n",
-      "3794                       cudaMemcpyHostToDevice);\n",
-      "3795     //CNDA_THREAD_SYNC;  // unneeded because cudaMemcpy is blocking anyway\n",
-      "3796     CNDA_END_ALLOW_THREADS;\n",
-      "3797     if (cudaSuccess != cerr)\n",
-      "3798     {\n",
-      "3799         PyErr_Format(PyExc_RuntimeError,\n",
-      "3800                      \"Cuda error '%s' while copying %lli data element\"\n",
-      "3801                      \" to device memory. str ptr=%p. dst ptr=%p\",\n",
-      "3802                      cudaGetErrorString(cerr),\n",
-      "3803                      (long long)py_src_size,\n",
-      "3804                      py_src_data,\n",
-      "3805                      self->devdata);\n",
-      "3806         Py_DECREF(py_src);\n",
-      "3807         return -1;\n",
-      "3808     }\n",
-      "3809     Py_DECREF(py_src);\n",
-      "3810     return 0;\n",
-      "3811 }\n",
-      "3812 \n",
-      "3813 PyObject *\n",
-      "3814 CudaNdarray_new_nd(int nd)\n",
-      "3815 {\n",
-      "3816     CudaNdarray * rval = (CudaNdarray*) CudaNdarray_New();\n",
-      "3817     if (!rval || CudaNdarray_set_nd(rval, nd))\n",
-      "3818     {\n",
-      "3819         Py_XDECREF(rval);\n",
-      "3820         rval = NULL;\n",
-      "3821     }\n",
-      "3822     return (PyObject *) rval;\n",
-      "3823 }\n",
-      "3824 \n",
-      "3825 \n",
-      "3826 /**\n",
-      "3827  * Initialize 'self' as a view of 'base', with memory storage 'data'\n",
-      "3828  */\n",
-      "3829 \n",
-      "3830 int CudaNdarray_set_device_data(CudaNdarray * self, float * data, PyObject * base)\n",
-      "3831 {\n",
-      "3832     if (self->data_allocated)\n",
-      "3833     {\n",
-      "3834         assert(self->devdata);\n",
-      "3835         if (device_free(self->devdata))\n",
-      "3836         {\n",
-      "3837             self->devdata = NULL;\n",
-      "3838             self->data_allocated = 0;\n",
-      "3839             return -1;\n",
-      "3840         }\n",
-      "3841     }\n",
-      "3842     // Get the original base object (base.base.base...)\n",
-      "3843     PyObject * orig_base = base;\n",
-      "3844     // base is not always a CudaNdarray. It can be a GpuArray from pycuda, ...\n",
-      "3845     while (orig_base && CudaNdarray_Check(orig_base) && ((CudaNdarray*) orig_base)->base)\n",
-      "3846     {\n",
-      "3847         // base_base is itself a view\n",
-      "3848         orig_base = ((CudaNdarray*) orig_base)->base;\n",
-      "3849     }\n",
-      "3850     //N.B. XDECREF and XINCREF are no-ops for NULL pointers\n",
-      "3851     if (self->base != orig_base)\n",
-      "3852     {\n",
-      "3853         Py_XDECREF(self->base);\n",
-      "3854         self->base = orig_base;\n",
-      "3855         Py_XINCREF(self->base);\n",
-      "3856     }\n",
-      "3857     self->data_allocated = 0;\n",
-      "3858     self->devdata = data;\n",
-      "3859     return 0;\n",
-      "3860 }\n",
-      "3861 \n",
-      "3862 static __global__ void k_copy_1d(const int N, const float * x, const int sx, float * y, const int sy)\n",
-      "3863 {\n",
-      "3864     for (int i = threadIdx.x + blockIdx.x * blockDim.x; i < N; i += gridDim.x*blockDim.x)\n",
-      "3865     {\n",
-      "3866         y[i*sy] = x[i*sx];\n",
-      "3867     }\n",
-      "3868 }\n",
-      "3869 \n",
-      "3870 // N1 through N4 are the size of y\n",
-      "3871 static __global__ void k_copy_4d(const int N1,\n",
-      "3872         const int N2, const int N3, const int N4,\n",
-      "3873         const float * x, const int sx1, const int sx2, const int sx3,\n",
-      "3874         const int sx4,  float * y, const int sy1, const int sy2,\n",
-      "3875         const int sy3, const int sy4)\n",
-      "3876 {\n",
-      "3877     // These must be made int instead of unsigned int due to a bug in nvcc\n",
-      "3878     int bx = blockIdx.x;\n",
-      "3879     int by = blockIdx.y;\n",
-      "3880 \n",
-      "3881     for (int i = bx; i < N1; i += gridDim.x)\n",
-      "3882     {\n",
-      "3883         for (int j = by; j < N2; j += gridDim.y)\n",
-      "3884         {\n",
-      "3885             for (int k = threadIdx.x; k < N3; k += (int) blockDim.x)\n",
-      "3886             {\n",
-      "3887                 for (int l = threadIdx.y; l < N4; l += (int) blockDim.y)\n",
-      "3888                 {\n",
-      "3889                     y[i * sy1 + j * sy2 + k * sy3 + l * sy4] =\n",
-      "3890                         x[i * sx1 + j * sx2 + k * sx3 + l * sx4];\n",
-      "3891                 }\n",
-      "3892             }\n",
-      "3893         }\n",
-      "3894     }\n",
-      "3895 }\n",
-      "3896 \n",
-      "3897 //copy from other into self\n",
-      "3898 int CudaNdarray_CopyFromCudaNdarray(CudaNdarray * self,\n",
-      "3899                                     const CudaNdarray * other,\n",
-      "3900                                     bool unbroadcast)\n",
-      "3901 {\n",
-      "3902     int verbose = 0;\n",
-      "3903     if (verbose>1) fprintf(stderr, \"CudaNdarray_CopyFromCudaNdarray\\n\");\n",
-      "3904 \n",
-      "3905     //standard elemwise size checks\n",
-      "3906     if (self->nd == -1)\n",
-      "3907     {\n",
-      "3908         PyErr_SetString(PyExc_TypeError,\n",
-      "3909                         \"can't copy into un-initialized CudaNdarray\");\n",
-      "3910         return -1;\n",
-      "3911     }\n",
-      "3912     CudaNdarray * new_other = NULL;\n",
-      "3913 \n",
-      "3914     if (self->nd < other->nd)\n",
-      "3915     {\n",
-      "3916         PyErr_Format(PyExc_NotImplementedError,\n",
-      "3917             \"CudaNdarray_CopyFromCudaNdarray: The number of dimensions of the \"\n",
-      "3918             \"destination needs to be >= the number of dimensions of the \"\n",
-      "3919             \"source. Got %d and %d.\", self->nd, other->nd);\n",
-      "3920         return -1;\n",
-      "3921     }\n",
-      "3922     else if (self->nd != other->nd)\n",
-      "3923     {\n",
-      "3924         new_other = (CudaNdarray *) CudaNdarray_View(other);\n",
-      "3925         int added_dims = self->nd - other->nd;\n",
-      "3926         int* pattern = (int*) alloca(self->nd * sizeof(int));\n",
-      "3927         for(int i = 0; i < added_dims; i++)\n",
-      "3928             pattern[i] = -1;\n",
-      "3929         for(int i = 0; i < other->nd; i++)\n",
-      "3930             pattern[i + added_dims] = i;\n",
-      "3931         CudaNdarray_dimshuffle(new_other, self->nd, pattern);\n",
-      "3932         other = new_other;\n",
-      "3933     }\n",
-      "3934     assert(self->nd == other->nd);\n",
-      "3935     //standard elemwise dim checks (also compute total size)\n",
-      "3936     unsigned int size = 1;\n",
-      "3937     unsigned int size_source = 1;\n",
-      "3938     for (int i = 0; i< self->nd; ++i)\n",
-      "3939     {\n",
-      "3940         if ((CudaNdarray_HOST_DIMS(self)[i] != CudaNdarray_HOST_DIMS(other)[i])\n",
-      "3941             && (1!=CudaNdarray_HOST_DIMS(other)[i] || !unbroadcast) )\n",
-      "3942         {\n",
-      "3943           PyErr_Format(PyExc_ValueError,\n",
-      "3944                        \"CudaNdarray_CopyFromCudaNdarray:\"\n",
-      "3945                        \" need same dimensions for dim %d,\"\n",
-      "3946                        \" destination=%d, source=%d\",\n",
-      "3947                        i, CudaNdarray_HOST_DIMS(self)[i],\n",
-      "3948                        CudaNdarray_HOST_DIMS(other)[i]);\n",
-      "3949           Py_XDECREF(new_other);\n",
-      "3950           return -1;\n",
-      "3951         }\n",
-      "3952         size *= (unsigned int) CudaNdarray_HOST_DIMS(self)[i];\n",
-      "3953         size_source *= (unsigned int) CudaNdarray_HOST_DIMS(other)[i];\n",
-      "3954     }\n",
-      "3955     if (0 == size)\n",
-      "3956     {\n",
-      "3957         Py_XDECREF(new_other);\n",
-      "3958         return 0; //nothing to copy, we're done.\n",
-      "3959     }\n",
-      "3960     if (CudaNdarray_is_c_contiguous(self) &&\n",
-      "3961         CudaNdarray_is_c_contiguous(other) &&\n",
-      "3962         size == size_source)\n",
-      "3963     {\n",
-      "3964         if (verbose)\n",
-      "3965             fprintf(stderr, \"Copying contiguous vector with cublasScopy\\n\");\n",
-      "3966 \n",
-      "3967         cublasStatus_t err;\n",
-      "3968         err = cublasScopy(handle, size, CudaNdarray_DEV_DATA(other), 1,\n",
-      "3969                           CudaNdarray_DEV_DATA(self), 1);\n",
-      "3970         CNDA_THREAD_SYNC;\n",
-      "3971         Py_XDECREF(new_other);\n",
-      "3972         if (CUBLAS_STATUS_SUCCESS != err)\n",
-      "3973         {\n",
-      "3974             PyErr_SetString(PyExc_RuntimeError, \"Error copying memory\");\n",
-      "3975             return -1;\n",
-      "3976         }\n",
-      "3977         return 0;\n",
-      "3978     }\n",
-      "3979     //TODO: rewrite these copy operations to be more efficient\n",
-      "3980     //      See, for example the transpose example in the cuda_sdk.\n",
-      "3981     switch (self->nd)\n",
-      "3982     {\n",
-      "3983         case 0: // scalar\n",
-      "3984             {\n",
-      "3985                 // THIS CASE SHOULD NEVER HAPPEN BECAUSE SCALARS ARE ALWAYS C CONTIGUOUS\n",
-      "3986                 assert(0);\n",
-      "3987             }; break;\n",
-      "3988         case 1: // vector\n",
-      "3989             {\n",
-      "3990                 if (verbose) fprintf(stderr, \"Copying non-contiguous vector\\n\");\n",
-      "3991                 if (verbose) fprint_CudaNdarray(stderr, other);\n",
-      "3992                 unsigned int n_blocks = std::min(size,\n",
-      "3993                                                  (unsigned int)NUM_VECTOR_OP_BLOCKS);\n",
-      "3994                 unsigned int n_threads = std::min(ceil_intdiv(size, n_blocks),\n",
-      "3995                                                   (unsigned int)NUM_VECTOR_OP_THREADS_PER_BLOCK);\n",
-      "3996                 k_copy_1d<<<n_blocks, n_threads>>>(size,\n",
-      "3997                                             CudaNdarray_DEV_DATA(other),\n",
-      "3998                                             CudaNdarray_HOST_STRIDES(other)[0],\n",
-      "3999                                             CudaNdarray_DEV_DATA(self),\n",
-      "4000                                             CudaNdarray_HOST_STRIDES(self)[0]);\n",
-      "4001                 CNDA_THREAD_SYNC;\n",
-      "4002                 cudaError_t err = cudaGetLastError();\n",
-      "4003                 if( cudaSuccess != err)\n",
-      "4004                 {\n",
-      "4005                     PyErr_Format(PyExc_RuntimeError,\n",
-      "4006                                  \"Cuda error: %s: %s. (n_blocks=%i,\"\n",
-      "4007                                  \" n_threads_per_block=%i)\\n\", \"k_copy_1d\",\n",
-      "4008                                  cudaGetErrorString(err), n_blocks, n_threads);\n",
-      "4009                     Py_XDECREF(new_other);\n",
-      "4010                     return -1;\n",
-      "4011                 }\n",
-      "4012             }; break;\n",
-      "4013         case 4: // 4-tensor\n",
-      "4014             {\n",
-      "4015                 if (verbose)\n",
-      "4016                 {\n",
-      "4017                     if (0 != fprint_CudaNdarray(stderr, other))\n",
-      "4018                     {\n",
-      "4019                         Py_XDECREF(new_other);\n",
-      "4020                         return -1;\n",
-      "4021                     }\n",
-      "4022                 }\n",
-      "4023 \n",
-      "4024                 // The blocks implement the looping over the first two axes so\n",
-      "4025                 // this needs to be (N1, N2)\n",
-      "4026                 dim3 n_blocks( std::min(CudaNdarray_HOST_DIMS(self)[0],\n",
-      "4027                                         NUM_VECTOR_OP_BLOCKS),\n",
-      "4028                                std::min(CudaNdarray_HOST_DIMS(self)[1],\n",
-      "4029                                         NUM_VECTOR_OP_BLOCKS));\n",
-      "4030                 // For the threads, just make as many as possible\n",
-      "4031                 dim3 n_threads( std::min( (unsigned int) CudaNdarray_HOST_DIMS(self)[2],\n",
-      "4032                                  (unsigned int) NUM_VECTOR_OP_THREADS_PER_BLOCK),\n",
-      "4033                                 std::min( (unsigned int) CudaNdarray_HOST_DIMS(self)[3],\n",
-      "4034                                     (unsigned int) NUM_VECTOR_OP_THREADS_PER_BLOCK));\n",
-      "4035 \n",
-      "4036                 n_threads.x = std::min( (unsigned int) 32, (unsigned int) n_threads.x);\n",
-      "4037                 n_threads.y = std::min( n_threads.y, NUM_VECTOR_OP_THREADS_PER_BLOCK / n_threads.x);\n",
-      "4038 \n",
-      "4039                 k_copy_4d<<<n_blocks, n_threads>>>(\n",
-      "4040                                             // size of y\n",
-      "4041                                             (unsigned int) CudaNdarray_HOST_DIMS(self)[0], // N1\n",
-      "4042                                             (unsigned int) CudaNdarray_HOST_DIMS(self)[1], // N2\n",
-      "4043                                             (unsigned int) CudaNdarray_HOST_DIMS(self)[2], // N3\n",
-      "4044                                             (unsigned int) CudaNdarray_HOST_DIMS(self)[3], // N4\n",
-      "4045                                             CudaNdarray_DEV_DATA(other), // x\n",
-      "4046                                             // x strides\n",
-      "4047                                             CudaNdarray_HOST_STRIDES(other)[0],\n",
-      "4048                                             CudaNdarray_HOST_STRIDES(other)[1],\n",
-      "4049                                             CudaNdarray_HOST_STRIDES(other)[2],\n",
-      "4050                                             CudaNdarray_HOST_STRIDES(other)[3],\n",
-      "4051                                             CudaNdarray_DEV_DATA(self), // y\n",
-      "4052                                             // y strides\n",
-      "4053                                             CudaNdarray_HOST_STRIDES(self)[0],\n",
-      "4054                                             CudaNdarray_HOST_STRIDES(self)[1],\n",
-      "4055                                             CudaNdarray_HOST_STRIDES(self)[2],\n",
-      "4056                                             CudaNdarray_HOST_STRIDES(self)[3]\n",
-      "4057                                             );\n",
-      "4058                 CNDA_THREAD_SYNC;\n",
-      "4059                 cudaError_t err = cudaGetLastError();\n",
-      "4060                 if( cudaSuccess != err)\n",
-      "4061                 {\n",
-      "4062                     PyErr_Format(PyExc_RuntimeError,\n",
-      "4063                                  \"Cuda error: %s: %s.\",\n",
-      "4064                                  \"k_copy_4d\",\n",
-      "4065                                  cudaGetErrorString(err));\n",
-      "4066                     Py_XDECREF(new_other);\n",
-      "4067                     return -1;\n",
-      "4068                 }\n",
-      "4069             }; break;\n",
-      "4070         default:\n",
-      "4071             {\n",
-      "4072                 cudaError_t err = cudaGetLastError();\n",
-      "4073                 if(cudaSuccess != err){\n",
-      "4074                     PyErr_Format(PyExc_RuntimeError,\n",
-      "4075                                  \"Unexpected Cuda error: %s: %s\\n\",\n",
-      "4076                                  \"CudaNdarray_CopyFromCudaNdarray\",\n",
-      "4077                                  cudaGetErrorString(err));\n",
-      "4078                     Py_XDECREF(new_other);\n",
-      "4079                     return -1;\n",
-      "4080                 }\n",
-      "4081 \n",
-      "4082                 if (verbose)\n",
-      "4083                     fprintf(stderr,\n",
-      "4084                             \"Copying with default version unbroadcast=%d\\n\",\n",
-      "4085                             unbroadcast);\n",
-      "4086                 // call worker routine\n",
-      "4087                 unsigned int threads_per_block = std::min(size,\n",
-      "4088                                                           (unsigned int)NUM_VECTOR_OP_THREADS_PER_BLOCK);\n",
-      "4089                 unsigned int n_blocks = std::min(ceil_intdiv(size, threads_per_block),\n",
-      "4090                                                  (unsigned int)NUM_VECTOR_OP_BLOCKS);\n",
-      "4091                 const CudaNdarray * cuda_dims = other;\n",
-      "4092                 if(unbroadcast)\n",
-      "4093                     cuda_dims = self;\n",
-      "4094                 //copy from other into self\n",
-      "4095                 k_elemwise_unary_rowmajor_copy<<<n_blocks, threads_per_block>>>(\n",
-      "4096                         size,\n",
-      "4097                         (unsigned int)other->nd,\n",
-      "4098                         (const int *)CudaNdarray_DEV_DIMS(cuda_dims),\n",
-      "4099                         (const float*)CudaNdarray_DEV_DATA(other),\n",
-      "4100                         (const int *)CudaNdarray_DEV_STRIDES(other),\n",
-      "4101                         CudaNdarray_DEV_DATA(self),\n",
-      "4102                         (const int *)CudaNdarray_DEV_STRIDES(self));\n",
-      "4103                 CNDA_THREAD_SYNC;\n",
-      "4104                 err = cudaGetLastError();\n",
-      "4105                 if(verbose>1)\n",
-      "4106                     fprintf(stderr,\n",
-      "4107                             \"INFO k_elemwise_unary_rowmaj (n_blocks=%i,\"\n",
-      "4108                             \" n_threads_per_block=%i)\\n\",\n",
-      "4109                             n_blocks, threads_per_block);\n",
-      "4110                 if( cudaSuccess != err)\n",
-      "4111                 {\n",
-      "4112                     //fprint_CudaNdarray(stderr, self);\n",
-      "4113                     //fprint_CudaNdarray(stderr, other);\n",
-      "4114                     PyErr_Format(PyExc_RuntimeError,\n",
-      "4115                                  \"Cuda error: %s: %s. (n_blocks=%i,\"\n",
-      "4116                                  \" n_threads_per_block=%i)\\n\",\n",
-      "4117                                  \"k_elemwise_unary_rowmajor_copy\",\n",
-      "4118                                  cudaGetErrorString(err), n_blocks,\n",
-      "4119                                  threads_per_block);\n",
-      "4120                     Py_XDECREF(new_other);\n",
-      "4121                     return -1;\n",
-      "4122                 }\n",
-      "4123             }\n",
-      "4124     };\n",
-      "4125     Py_XDECREF(new_other);\n",
-      "4126     return 0;\n",
-      "4127 }\n",
-      "4128 \n",
-      "4129 int CudaNdarray_gemm(float alpha, const CudaNdarray * A, const CudaNdarray * B, float beta, CudaNdarray * C)\n",
-      "4130 {\n",
-      "4131     if (A->nd != 2)\n",
-      "4132     {\n",
-      "4133         PyErr_SetString(PyExc_ValueError, \"non-matrix arg A to gemm\");\n",
-      "4134         return -1;\n",
-      "4135     }\n",
-      "4136     if (B->nd != 2)\n",
-      "4137     {\n",
-      "4138         PyErr_SetString(PyExc_ValueError, \"non-matrix arg B to gemm\");\n",
-      "4139         return -1;\n",
-      "4140     }\n",
-      "4141     if (C->nd != 2)\n",
-      "4142     {\n",
-      "4143         PyErr_SetString(PyExc_ValueError, \"non-matrix arg C to gemm\");\n",
-      "4144         return -1;\n",
-      "4145     }\n",
-      "4146 \n",
-      "4147     // We must allow dimensions to be zeros.\n",
-      "4148     if ((CudaNdarray_HOST_DIMS(A)[1] != CudaNdarray_HOST_DIMS(B)[0])\n",
-      "4149             || (CudaNdarray_HOST_DIMS(A)[0] != CudaNdarray_HOST_DIMS(C)[0])\n",
-      "4150             || (CudaNdarray_HOST_DIMS(B)[1] != CudaNdarray_HOST_DIMS(C)[1]))\n",
-      "4151     {\n",
-      "4152         PyErr_Format(PyExc_ValueError, \"dimension mismatch in args to gemm (%i,%i)x(%i,%i)->(%i,%i)\",\n",
-      "4153                 CudaNdarray_HOST_DIMS(A)[0],\n",
-      "4154                 CudaNdarray_HOST_DIMS(A)[1],\n",
-      "4155                 CudaNdarray_HOST_DIMS(B)[0],\n",
-      "4156                 CudaNdarray_HOST_DIMS(B)[1],\n",
-      "4157                 CudaNdarray_HOST_DIMS(C)[0],\n",
-      "4158                 CudaNdarray_HOST_DIMS(C)[1]);\n",
-      "4159         return -1;\n",
-      "4160     }\n",
-      "4161 \n",
-      "4162     // If matrix A or B has non-unit size and non-unit stride in both\n",
-      "4163     // dimensions, we can make a copy.\n",
-      "4164     CudaNdarray * A_new = NULL;\n",
-      "4165     CudaNdarray * B_new = NULL;\n",
-      "4166     if (((CudaNdarray_HOST_DIMS(A)[0] > 1)\n",
-      "4167          && (CudaNdarray_HOST_STRIDES(A)[0] != 1)\n",
-      "4168          && (CudaNdarray_HOST_DIMS(A)[1] > 1)\n",
-      "4169          && (CudaNdarray_HOST_STRIDES(A)[1] != 1))\n",
-      "4170         || (CudaNdarray_HOST_STRIDES(A)[0] < 0)\n",
-      "4171         || (CudaNdarray_HOST_STRIDES(A)[1] < 0))\n",
-      "4172     {\n",
-      "4173         A_new = (CudaNdarray*) CudaNdarray_Copy(A);\n",
-      "4174         if (!A_new)\n",
-      "4175             return -1;\n",
-      "4176         A = A_new;\n",
-      "4177     }\n",
-      "4178 \n",
-      "4179     if (((CudaNdarray_HOST_DIMS(B)[0] > 1)\n",
-      "4180          && (CudaNdarray_HOST_STRIDES(B)[0] != 1)\n",
-      "4181          && (CudaNdarray_HOST_DIMS(B)[1] > 1)\n",
-      "4182          && (CudaNdarray_HOST_STRIDES(B)[1] != 1))\n",
-      "4183         || (CudaNdarray_HOST_STRIDES(B)[0] < 0)\n",
-      "4184         || (CudaNdarray_HOST_STRIDES(B)[1] < 0))\n",
-      "4185     {\n",
-      "4186         B_new = (CudaNdarray*) CudaNdarray_Copy(B);\n",
-      "4187         if (!B_new)\n",
-      "4188         {\n",
-      "4189             // If A_new is NULL, meaning A was not copied nothing happens\n",
-      "4190             Py_XDECREF(A_new);\n",
-      "4191             return -1;\n",
-      "4192         }\n",
-      "4193         B = B_new;\n",
-      "4194     }\n",
-      "4195 \n",
-      "4196     // If matrix C has non-unit size and non-unit stride in both\n",
-      "4197     // dimensions, or negative strides, we can't operate. We cannot copy\n",
-      "4198     // C either, because the calling code will expect the result to be\n",
-      "4199     // in the original C container.\n",
-      "4200     if (((CudaNdarray_HOST_DIMS(C)[0] > 1)\n",
-      "4201          && (CudaNdarray_HOST_STRIDES(C)[0] != 1)\n",
-      "4202          && (CudaNdarray_HOST_DIMS(C)[1] > 1)\n",
-      "4203          && (CudaNdarray_HOST_STRIDES(C)[1] != 1))\n",
-      "4204         || (CudaNdarray_HOST_STRIDES(C)[0] < 0)\n",
-      "4205         || (CudaNdarray_HOST_STRIDES(C)[1] < 0))\n",
-      "4206     {\n",
-      "4207         PyErr_Format(PyExc_AssertionError,\n",
-      "4208                      \"non-unit or negative stride in gemm arg C (%i,%i) of shape (%i,%i)\",\n",
-      "4209                      CudaNdarray_HOST_STRIDES(C)[0],\n",
-      "4210                      CudaNdarray_HOST_STRIDES(C)[1],\n",
-      "4211                      CudaNdarray_HOST_DIMS(C)[0],\n",
-      "4212                      CudaNdarray_HOST_DIMS(C)[1]);\n",
-      "4213         Py_XDECREF(A_new);\n",
-      "4214         Py_XDECREF(B_new);\n",
-      "4215         return -1;\n",
-      "4216     }\n",
-      "4217 \n",
-      "4218     // the unit integer is divided logically into three fields of 4 bits\n",
-      "4219     // the lowermost 4 bits encode the stride pattern of the output\n",
-      "4220     // the next higher 4 bits encode the B variable (or y)\n",
-      "4221     // the next higher 4 bits encode the C variable (or x)\n",
-      "4222     //\n",
-      "4223     // the stride pattern for each input is encoded as 0 for unit stride from col to col (Row major)\n",
-      "4224     //                                                 1 for unit stride from row to row (Col major)\n",
-      "4225 \n",
-      "4226     // a stride of 0 implies a dimension of 1 - so we can actually define\n",
-      "4227     // a stride of 0 as a 'unit' stride because gemm will never use it.\n",
-      "4228     // If a dimension is 0, its stride will not be used either, so we can\n",
-      "4229     // consider it a 'unit' stride too.\n",
-      "4230     int unit = 0;\n",
-      "4231     if (CudaNdarray_HOST_STRIDES(A)[1] == 1 || CudaNdarray_HOST_DIMS(A)[1] <= 1) {\n",
-      "4232         unit |= (0x0 << 8);\n",
-      "4233     } else if (CudaNdarray_HOST_STRIDES(A)[0] == 1 || CudaNdarray_HOST_DIMS(A)[0] <= 1) {\n",
-      "4234         unit |= (0x1 << 8);\n",
-      "4235     } else {\n",
-      "4236         unit |= (0x2 << 8);\n",
-      "4237     }\n",
-      "4238     if (CudaNdarray_HOST_STRIDES(B)[1] == 1 || CudaNdarray_HOST_DIMS(B)[1] <= 1) {\n",
-      "4239         unit |= (0x0 << 4);\n",
-      "4240     } else if (CudaNdarray_HOST_STRIDES(B)[0] == 1 || CudaNdarray_HOST_DIMS(B)[0] <= 1) {\n",
-      "4241         unit |= (0x1 << 4);\n",
-      "4242     } else {\n",
-      "4243         unit |= (0x2 << 4);\n",
-      "4244     }\n",
-      "4245     if (CudaNdarray_HOST_STRIDES(C)[1] == 1 || CudaNdarray_HOST_DIMS(C)[1] <= 1) {\n",
-      "4246         unit |= (0x0 << 0);\n",
-      "4247     } else if (CudaNdarray_HOST_STRIDES(C)[0] == 1 || CudaNdarray_HOST_DIMS(C)[0] <= 1) {\n",
-      "4248         unit |= (0x1 << 0);\n",
-      "4249     } else {\n",
-      "4250         unit |= (0x2 << 0);\n",
-      "4251     }\n",
-      "4252 \n",
-      "4253     /* create appropriate strides for malformed matrices that are row or column\n",
-      "4254      * vectors\n",
-      "4255      */\n",
-      "4256     int sa_0 = (CudaNdarray_HOST_DIMS(A)[0] > 1) ? CudaNdarray_HOST_STRIDES(A)[0] : CudaNdarray_HOST_DIMS(A)[1];\n",
-      "4257     int sa_1 = (CudaNdarray_HOST_DIMS(A)[1] > 1) ? CudaNdarray_HOST_STRIDES(A)[1] : CudaNdarray_HOST_DIMS(A)[0];\n",
-      "4258     int sb_0 = (CudaNdarray_HOST_DIMS(B)[0] > 1) ? CudaNdarray_HOST_STRIDES(B)[0] : CudaNdarray_HOST_DIMS(B)[1];\n",
-      "4259     int sb_1 = (CudaNdarray_HOST_DIMS(B)[1] > 1) ? CudaNdarray_HOST_STRIDES(B)[1] : CudaNdarray_HOST_DIMS(B)[0];\n",
-      "4260     int sc_0 = (CudaNdarray_HOST_DIMS(C)[0] > 1) ? CudaNdarray_HOST_STRIDES(C)[0] : CudaNdarray_HOST_DIMS(C)[1];\n",
-      "4261     int sc_1 = (CudaNdarray_HOST_DIMS(C)[1] > 1) ? CudaNdarray_HOST_STRIDES(C)[1] : CudaNdarray_HOST_DIMS(C)[0];\n",
-      "4262 \n",
-      "4263     float* a = CudaNdarray_DEV_DATA(A);\n",
-      "4264     float* b = CudaNdarray_DEV_DATA(B);\n",
-      "4265     float* c = CudaNdarray_DEV_DATA(C);\n",
-      "4266     cublasOperation_t N = CUBLAS_OP_N;\n",
-      "4267     cublasOperation_t T = CUBLAS_OP_T;\n",
-      "4268     //std::cerr << (unit/256) MOD 16 << (unit / 16) MOD 16 << unit MOD 16<< '\\\\n';\n",
-      "4269     // There should be no negative stride at that point\n",
-      "4270 #define CHK_STRIDE_SGEMM(T0, T1, D0, D1, D2, a, x, sx, y, sy, b, z, sz) \\\n",
-      "4271     if (sx == 0){sx = 1;}\\\n",
-      "4272     if (sy == 0){sy = 1;}\\\n",
-      "4273     if (sz == 0){sz = 1;}\\\n",
-      "4274     if ((sx > 0) && (sy > 0) && (sz > 0)) { \\\n",
-      "4275         err = cublasSgemm(handle, T0, T1, D0, D1, D2, &a, x, sx, y, sy, &b, z, sz); \\\n",
-      "4276     } else { \\\n",
-      "4277         PyErr_SetString(PyExc_AssertionError, \"negative stride to sGemm\");\\\n",
-      "4278         Py_XDECREF(A_new);\\\n",
-      "4279         Py_XDECREF(B_new);\\\n",
-      "4280         return -1; \\\n",
-      "4281     }\n",
-      "4282 \n",
-      "4283     cublasStatus_t err;\n",
-      "4284     switch(unit)\n",
-      "4285     {\n",
-      "4286         case 0x000: CHK_STRIDE_SGEMM(N, N, CudaNdarray_HOST_DIMS(C)[1], CudaNdarray_HOST_DIMS(C)[0], CudaNdarray_HOST_DIMS(A)[1], alpha, b, sb_0, a, sa_0, beta, c, sc_0); break;\n",
-      "4287         case 0x100: CHK_STRIDE_SGEMM(N, T, CudaNdarray_HOST_DIMS(C)[1], CudaNdarray_HOST_DIMS(C)[0], CudaNdarray_HOST_DIMS(A)[1], alpha, b, sb_0, a, sa_1, beta, c, sc_0); break;\n",
-      "4288         case 0x010: CHK_STRIDE_SGEMM(T, N, CudaNdarray_HOST_DIMS(C)[1], CudaNdarray_HOST_DIMS(C)[0], CudaNdarray_HOST_DIMS(A)[1], alpha, b, sb_1, a, sa_0, beta, c, sc_0); break;\n",
-      "4289         case 0x110: CHK_STRIDE_SGEMM(T, T, CudaNdarray_HOST_DIMS(C)[1], CudaNdarray_HOST_DIMS(C)[0], CudaNdarray_HOST_DIMS(A)[1], alpha, b, sb_1, a, sa_1, beta, c, sc_0); break;\n",
-      "4290         case 0x001: CHK_STRIDE_SGEMM(T, T, CudaNdarray_HOST_DIMS(C)[0], CudaNdarray_HOST_DIMS(C)[1], CudaNdarray_HOST_DIMS(A)[1], alpha, a, sa_0, b, sb_0, beta, c, sc_1); break;\n",
-      "4291         case 0x101: CHK_STRIDE_SGEMM(N, T, CudaNdarray_HOST_DIMS(C)[0], CudaNdarray_HOST_DIMS(C)[1], CudaNdarray_HOST_DIMS(A)[1], alpha, a, sa_1, b, sb_0, beta, c, sc_1); break;\n",
-      "4292         case 0x011: CHK_STRIDE_SGEMM(T, N, CudaNdarray_HOST_DIMS(C)[0], CudaNdarray_HOST_DIMS(C)[1], CudaNdarray_HOST_DIMS(A)[1], alpha, a, sa_0, b, sb_1, beta, c, sc_1); break;\n",
-      "4293         case 0x111: CHK_STRIDE_SGEMM(N, N, CudaNdarray_HOST_DIMS(C)[0], CudaNdarray_HOST_DIMS(C)[1], CudaNdarray_HOST_DIMS(A)[1], alpha, a, sa_1, b, sb_1, beta, c, sc_1); break;\n",
-      "4294         default: PyErr_Format(PyExc_ValueError, \"some matrix has no unit stride (unit=%x)\", unit);\n",
-      "4295                  return -1;\n",
-      "4296     };\n",
-      "4297     CNDA_THREAD_SYNC;\n",
-      "4298     Py_XDECREF(A_new);\n",
-      "4299     Py_XDECREF(B_new);\n",
-      "4300 \n",
-      "4301     if (CUBLAS_STATUS_SUCCESS != err)\n",
-      "4302     {\n",
-      "4303         PyErr_Format(PyExc_RuntimeError,\n",
-      "4304                      \"cublasSgemm failed (%i) %s\\n\"\n",
-      "4305                      \" unit=%x N=%d, c.dims=[%d %d], a.dim=[%d %d], alpha=%f, beta=%f, a=%p, b=%p, c=%p\"\n",
-      "4306                      \" sa_0=%d, sa_1=%d, sb_0=%d, sb_1=%d, sc_0=%d, sc_1=%d\",\n",
-      "4307                      err,  cublasGetErrorString(err),\n",
-      "4308                      unit, N,\n",
-      "4309                      CudaNdarray_HOST_DIMS(C)[0],\n",
-      "4310                      CudaNdarray_HOST_DIMS(C)[1],\n",
-      "4311                      CudaNdarray_HOST_DIMS(A)[0], CudaNdarray_HOST_DIMS(A)[1],\n",
-      "4312                      alpha, beta, a, b, c, sa_0, sa_1, sb_0, sb_1, sc_0, sc_1);\n",
-      "4313 \n",
-      "4314         return -1;\n",
-      "4315     }\n",
-      "4316     return 0;\n",
-      "4317 }\n",
-      "4318 \n",
-      "4319 int CudaNdarray_sgemv(float alpha, const CudaNdarray * A, const CudaNdarray * B, float beta, CudaNdarray * C)\n",
-      "4320 {\n",
-      "4321     /**\n",
-      "4322     * C <- alpha A B + beta C\n",
-      "4323     *    A : matrix\n",
-      "4324     *    B, C: vector\n",
-      "4325     *    alpha, beta: scalars\n",
-      "4326     */\n",
-      "4327     if (A->nd != 2) { PyErr_SetString(PyExc_ValueError, \"non-matrix arg to gemv\"); return -1; }\n",
-      "4328     if (B->nd != 1) { PyErr_SetString(PyExc_ValueError, \"non-vector arg to gemv\"); return -1; }\n",
-      "4329     if (C->nd != 1) { PyErr_SetString(PyExc_ValueError, \"non-vector arg to gemv\"); return -1; }\n",
-      "4330 \n",
-      "4331     // We must allow dimensions to be zeros.\n",
-      "4332     if ((CudaNdarray_HOST_DIMS(A)[1] != CudaNdarray_HOST_DIMS(B)[0])\n",
-      "4333             || (CudaNdarray_HOST_DIMS(A)[0] != CudaNdarray_HOST_DIMS(C)[0]))\n",
-      "4334     {\n",
-      "4335         PyErr_Format(PyExc_ValueError, \"dimension mismatch in args to gemv (%i,%i)x(%i)->(%i)\",\n",
-      "4336                 CudaNdarray_HOST_DIMS(A)[0],\n",
-      "4337                 CudaNdarray_HOST_DIMS(A)[1],\n",
-      "4338                 CudaNdarray_HOST_DIMS(B)[0],\n",
-      "4339                 CudaNdarray_HOST_DIMS(C)[0]);\n",
-      "4340         return -1;\n",
-      "4341     }\n",
-      "4342 \n",
-      "4343     // If matrix A has non-unit size and non-unit stride in both\n",
-      "4344     // dimensions, or negative strides, we cannot operate, but we can\n",
-      "4345     // make a copy.\n",
-      "4346     CudaNdarray * A_new = NULL;\n",
-      "4347     CudaNdarray * B_new = NULL;\n",
-      "4348     if (((CudaNdarray_HOST_DIMS(A)[0] > 1)\n",
-      "4349          && (CudaNdarray_HOST_STRIDES(A)[0] != 1)\n",
-      "4350          && (CudaNdarray_HOST_DIMS(A)[1] > 1)\n",
-      "4351          && (CudaNdarray_HOST_STRIDES(A)[1] != 1))\n",
-      "4352         || (CudaNdarray_HOST_STRIDES(A)[0] < 0)\n",
-      "4353         || (CudaNdarray_HOST_STRIDES(A)[1] < 0))\n",
-      "4354     {\n",
-      "4355         A_new = (CudaNdarray*) CudaNdarray_Copy(A);\n",
-      "4356         if (!A_new)\n",
-      "4357             return -1;\n",
-      "4358         A = A_new;\n",
-      "4359     }\n",
-      "4360 \n",
-      "4361     // If vector B as a negative stride, we also have to make a copy.\n",
-      "4362     if (CudaNdarray_HOST_STRIDES(B)[0] < 0)\n",
-      "4363     {\n",
-      "4364         B_new = (CudaNdarray*) CudaNdarray_Copy(B);\n",
-      "4365         if (!B_new)\n",
-      "4366         {\n",
-      "4367             // If A was not copied, A_new is NULL, and Py_XDECREF does not\n",
-      "4368             // do anything\n",
-      "4369             Py_XDECREF(A_new);\n",
-      "4370             return -1;\n",
-      "4371         }\n",
-      "4372         B = B_new;\n",
-      "4373     }\n",
-      "4374 \n",
-      "4375     // cudablas does not handle negative strides as expected\n",
-      "4376     if (   (CudaNdarray_HOST_STRIDES(A)[0] < 0)\n",
-      "4377         || (CudaNdarray_HOST_STRIDES(A)[1] < 0))\n",
-      "4378     {\n",
-      "4379         PyErr_Format(PyExc_ValueError, \"illegal strides in args to gemv (%i,%i)\",\n",
-      "4380                 CudaNdarray_HOST_STRIDES(A)[0],\n",
-      "4381                 CudaNdarray_HOST_STRIDES(A)[1]);\n",
-      "4382         Py_XDECREF(A_new);\n",
-      "4383         Py_XDECREF(B_new);\n",
-      "4384         return -1;\n",
-      "4385     }\n",
-      "4386 \n",
-      "4387     /* create appropriate strides for malformed matrices that are row or column\n",
-      "4388      * vectors\n",
-      "4389      */\n",
-      "4390     int sa_0 = (CudaNdarray_HOST_DIMS(A)[0] > 1) ? CudaNdarray_HOST_STRIDES(A)[0] : CudaNdarray_HOST_DIMS(A)[1];\n",
-      "4391     int sa_1 = (CudaNdarray_HOST_DIMS(A)[1] > 1) ? CudaNdarray_HOST_STRIDES(A)[1] : CudaNdarray_HOST_DIMS(A)[0];\n",
-      "4392     int sb_0 = (CudaNdarray_HOST_DIMS(B)[0] > 1) ? CudaNdarray_HOST_STRIDES(B)[0] : 1;\n",
-      "4393     int sc_0 = (CudaNdarray_HOST_DIMS(C)[0] > 1) ? CudaNdarray_HOST_STRIDES(C)[0] : 1;\n",
-      "4394 \n",
-      "4395     if (sa_0 == 0) sa_0 = 1;\n",
-      "4396     if (sa_1 == 0) sa_1 = 1;\n",
-      "4397 \n",
-      "4398     int used_dot = 0;\n",
-      "4399 \n",
-      "4400     // This is important because we can end up not calling Sgemv at all\n",
-      "4401     cublasStatus_t err = CUBLAS_STATUS_SUCCESS;\n",
-      "4402     if (CudaNdarray_SIZE(C)) {\n",
-      "4403         // A is row vector & alpha==1 & beta==0 -> use cublasSdot\n",
-      "4404         if (CudaNdarray_HOST_DIMS(A)[0] == 1 && alpha==1.f && beta==0.f) {\n",
-      "4405             //replace this with custom inner product kernel with alpha and beta parameter?\n",
-      "4406             cublasPointerMode_t pmode;\n",
-      "4407             //set pointer mode to make sure cublas not storing on host pointer\n",
-      "4408             cublasGetPointerMode(handle, &pmode);\n",
-      "4409             cublasSetPointerMode(handle, CUBLAS_POINTER_MODE_DEVICE);\n",
-      "4410             err = cublasSdot(\n",
-      "4411                     handle, CudaNdarray_HOST_DIMS(A)[1],\n",
-      "4412                     CudaNdarray_DEV_DATA(A), sa_1,\n",
-      "4413                     CudaNdarray_DEV_DATA(B), sb_0,\n",
-      "4414                     CudaNdarray_DEV_DATA(C));\n",
-      "4415             cublasSetPointerMode(handle, pmode);\n",
-      "4416             used_dot = 1;\n",
-      "4417         }\n",
-      "4418         // A is row-contiguous | row vector\n",
-      "4419         else if ((CudaNdarray_HOST_DIMS(A)[0] <= 1)\n",
-      "4420             || ((CudaNdarray_HOST_STRIDES(A)[0] == 1)\n",
-      "4421                 && (CudaNdarray_HOST_STRIDES(A)[1] > 0)))\n",
-      "4422         {\n",
-      "4423             err = cublasSgemv(handle, CUBLAS_OP_N,\n",
-      "4424                     CudaNdarray_HOST_DIMS(A)[0], CudaNdarray_HOST_DIMS(A)[1],\n",
-      "4425                     &alpha,\n",
-      "4426                     CudaNdarray_DEV_DATA(A), sa_1,\n",
-      "4427                     CudaNdarray_DEV_DATA(B), sb_0,\n",
-      "4428                     &beta,\n",
-      "4429                     CudaNdarray_DEV_DATA(C), sc_0);\n",
-      "4430         }\n",
-      "4431         // A is column-contiguous | column vector\n",
-      "4432         else if ((CudaNdarray_HOST_DIMS(A)[1] <= 1)\n",
-      "4433                 || ((CudaNdarray_HOST_STRIDES(A)[1] == 1)\n",
-      "4434                     && (CudaNdarray_HOST_STRIDES(A)[0] > 0)))\n",
-      "4435         {\n",
-      "4436             err = cublasSgemv(handle, CUBLAS_OP_T,\n",
-      "4437                     CudaNdarray_HOST_DIMS(A)[1], CudaNdarray_HOST_DIMS(A)[0],\n",
-      "4438                     &alpha,\n",
-      "4439                     CudaNdarray_DEV_DATA(A), sa_0,\n",
-      "4440                     CudaNdarray_DEV_DATA(B), sb_0,\n",
-      "4441                     &beta,\n",
-      "4442                     CudaNdarray_DEV_DATA(C), sc_0);\n",
-      "4443         }\n",
-      "4444         // A is non vector and have malformed strides\n",
-      "4445         else\n",
-      "4446         {\n",
-      "4447             PyErr_Format(PyExc_AssertionError,\n",
-      "4448                          \"Unexpected stride pattern in gemv: (%i, %i) x %i -> %i.\\n\"\n",
-      "4449                          \"Shapes are: (%i, %i) x %i -> %i\\n\",\n",
-      "4450                          CudaNdarray_HOST_STRIDES(A)[0],\n",
-      "4451                          CudaNdarray_HOST_STRIDES(A)[1],\n",
-      "4452                          CudaNdarray_HOST_STRIDES(B)[0],\n",
-      "4453                          CudaNdarray_HOST_STRIDES(C)[0],\n",
-      "4454                          CudaNdarray_HOST_DIMS(A)[0],\n",
-      "4455                          CudaNdarray_HOST_DIMS(A)[1],\n",
-      "4456                          CudaNdarray_HOST_DIMS(B)[0],\n",
-      "4457                          CudaNdarray_HOST_DIMS(C)[0]);\n",
-      "4458             Py_XDECREF(A_new);\n",
-      "4459             Py_XDECREF(B_new);\n",
-      "4460             return -1;\n",
-      "4461         }\n",
-      "4462     }\n",
-      "4463 \n",
-      "4464     CNDA_THREAD_SYNC;\n",
-      "4465     Py_XDECREF(A_new);\n",
-      "4466     Py_XDECREF(B_new);\n",
-      "4467 \n",
-      "4468     if (CUBLAS_STATUS_SUCCESS != err)\n",
-      "4469     {\n",
-      "4470         if (!used_dot)\n",
-      "4471         {\n",
-      "4472             PyErr_Format(PyExc_RuntimeError,\n",
-      "4473                          \"cublasSgemv failed (%i)\",\n",
-      "4474                          err);\n",
-      "4475         } else {\n",
-      "4476         PyErr_Format(PyExc_RuntimeError,\n",
-      "4477                      \"cublasSdot failed (%i)\",\n",
-      "4478                      err);\n",
-      "4479         }\n",
-      "4480         return -1;\n",
-      "4481     }\n",
-      "4482     return 0;\n",
-      "4483 }\n",
-      "4484 \n",
-      "4485 int CudaNdarray_sger(float alpha, const CudaNdarray * x, const CudaNdarray * y, CudaNdarray * A) {\n",
-      "4486     if (x->nd != 1) { PyErr_SetString(PyExc_ValueError, \"non-vector arg x to sger\"); return -1; }\n",
-      "4487     if (y->nd != 1) { PyErr_SetString(PyExc_ValueError, \"non-vector arg y to sger\"); return -1; }\n",
-      "4488     if (A->nd != 2) { PyErr_SetString(PyExc_ValueError, \"non-matrix arg A to sger\"); return -1; }\n",
-      "4489 \n",
-      "4490     if ((CudaNdarray_HOST_DIMS(A)[0] != CudaNdarray_HOST_DIMS(x)[0])\n",
-      "4491         || (CudaNdarray_HOST_DIMS(A)[1] != CudaNdarray_HOST_DIMS(y)[0])) {\n",
-      "4492         PyErr_Format(PyExc_ValueError,\n",
-      "4493                      \"dimension mismatch in args to sger (%i)x(%i)->(%i,%i)\",\n",
-      "4494                      CudaNdarray_HOST_DIMS(x)[0],\n",
-      "4495                      CudaNdarray_HOST_DIMS(y)[0],\n",
-      "4496                      CudaNdarray_HOST_DIMS(A)[0],\n",
-      "4497                      CudaNdarray_HOST_DIMS(A)[1]);\n",
-      "4498         return -1;\n",
-      "4499     }\n",
-      "4500 \n",
-      "4501     int x_strides = CudaNdarray_HOST_STRIDES(x)[0];\n",
-      "4502     CudaNdarray * x_new = NULL;\n",
-      "4503     if(x_strides == 0){\n",
-      "4504         if(CudaNdarray_HOST_DIMS(x)[0] != 1){\n",
-      "4505             PyErr_Format(PyExc_RuntimeError,\n",
-      "4506                          \"CudaNdarray_sger: Invalid input x (should not happen).\"\n",
-      "4507                          \" We received a CudaNdarray vector with a stride of 0\"\n",
-      "4508                          \" that has more than 1 element!\");\n",
-      "4509             return -1;\n",
-      "4510         }\n",
-      "4511         x_strides = 1;\n",
-      "4512     } else if(x_strides < 0){\n",
-      "4513         x_new = (CudaNdarray*) CudaNdarray_Copy(x);\n",
-      "4514         x = x_new;\n",
-      "4515         x_strides = CudaNdarray_HOST_STRIDES(x)[0];\n",
-      "4516     }\n",
-      "4517 \n",
-      "4518     int y_strides = CudaNdarray_HOST_STRIDES(y)[0];\n",
-      "4519     CudaNdarray * y_new = NULL;\n",
-      "4520     if(y_strides == 0){\n",
-      "4521         if(CudaNdarray_HOST_DIMS(y)[0] != 1){\n",
-      "4522             PyErr_Format(PyExc_RuntimeError,\n",
-      "4523                          \"CudaNdarray_sger: Invalid input y (should not happen).\"\n",
-      "4524                          \" We received a CudaNdarray vector with a stride of 0\"\n",
-      "4525                          \" that has more than 1 elements!\");\n",
-      "4526             Py_XDECREF(x_new);\n",
-      "4527             return -1;\n",
-      "4528         }\n",
-      "4529         y_strides = 1;\n",
-      "4530     } else if(y_strides < 0){\n",
-      "4531         y_new = (CudaNdarray*) CudaNdarray_Copy(y);\n",
-      "4532         y = y_new;\n",
-      "4533         y_strides = CudaNdarray_HOST_STRIDES(y)[0];\n",
-      "4534     }\n",
-      "4535 \n",
-      "4536     // Create appropriate strides if A is a row or column vector\n",
-      "4537     int sa_0 = (CudaNdarray_HOST_DIMS(A)[0] > 1) ? CudaNdarray_HOST_STRIDES(A)[0]\n",
-      "4538                                                  : CudaNdarray_HOST_DIMS(A)[1];\n",
-      "4539     int sa_1 = (CudaNdarray_HOST_DIMS(A)[1] > 1) ? CudaNdarray_HOST_STRIDES(A)[1]\n",
-      "4540                                                  : CudaNdarray_HOST_DIMS(A)[0];\n",
-      "4541 \n",
-      "4542     // This is important because we can end up not calling Sger at all\n",
-      "4543     cublasStatus_t err = CUBLAS_STATUS_SUCCESS;\n",
-      "4544     if(CudaNdarray_SIZE(A)){\n",
-      "4545         // If A is in col-major\n",
-      "4546         if ((CudaNdarray_HOST_DIMS(A)[0] <= 1)\n",
-      "4547             || ((CudaNdarray_HOST_STRIDES(A)[0] == 1)\n",
-      "4548                 && (CudaNdarray_HOST_STRIDES(A)[1] > 0)))\n",
-      "4549         {\n",
-      "4550             err = cublasSger(handle, CudaNdarray_HOST_DIMS(x)[0], CudaNdarray_HOST_DIMS(y)[0], &alpha,\n",
-      "4551                        CudaNdarray_DEV_DATA(x), x_strides,\n",
-      "4552                        CudaNdarray_DEV_DATA(y), y_strides,\n",
-      "4553                        CudaNdarray_DEV_DATA(A), sa_1);\n",
-      "4554         }\n",
-      "4555         // Since Sger expects A in col-major, we invert x and y to fake this.\n",
-      "4556         else if ((CudaNdarray_HOST_DIMS(A)[1] <= 1)\n",
-      "4557                 || ((CudaNdarray_HOST_STRIDES(A)[1] == 1)\n",
-      "4558                     && (CudaNdarray_HOST_STRIDES(A)[0] > 0)))\n",
-      "4559         {\n",
-      "4560             err = cublasSger(handle, CudaNdarray_HOST_DIMS(y)[0], CudaNdarray_HOST_DIMS(x)[0], &alpha,\n",
-      "4561                        CudaNdarray_DEV_DATA(y), y_strides,\n",
-      "4562                        CudaNdarray_DEV_DATA(x), x_strides,\n",
-      "4563                        CudaNdarray_DEV_DATA(A), sa_0);\n",
-      "4564         }\n",
-      "4565         // A has to be either c- or f-contiguous, with no negative strides\n",
-      "4566         else\n",
-      "4567         {\n",
-      "4568             PyErr_SetString(PyExc_NotImplementedError,\n",
-      "4569                             \"non-contiguous A, or negative strides, in sger\");\n",
-      "4570             Py_XDECREF(x_new);\n",
-      "4571             Py_XDECREF(y_new);\n",
-      "4572             return -1;\n",
-      "4573         }\n",
-      "4574     }\n",
-      "4575     CNDA_THREAD_SYNC;\n",
-      "4576     Py_XDECREF(x_new);\n",
-      "4577     Py_XDECREF(y_new);\n",
-      "4578 \n",
-      "4579     if (CUBLAS_STATUS_SUCCESS != err)\n",
-      "4580     {\n",
-      "4581         PyErr_Format(PyExc_RuntimeError,\n",
-      "4582                      \"cublasSger failed (%i)\",\n",
-      "4583                      err);\n",
-      "4584         return -1;\n",
-      "4585     }\n",
-      "4586 \n",
-      "4587     return 0;\n",
-      "4588 }\n",
-      "4589 \n",
-      "4590 /**\n",
-      "4591  *\n",
-      "4592  * Precondition:\n",
-      "4593  *  a->dim[d] == (dims_a[d]==0) ? (1 << log2_dims_a[d]) : dims_a[d]\n",
-      "4594  *  z->dim[d] == (z_str[d]==0) ? 1 : dims_a[d];\n",
-      "4595  *\n",
-      "4596  *  TODO: templatize this function to support other reductions.\n",
-      "4597  *  All that needs to change is the initial value for sum, and the reduction operator.\n",
-      "4598  */\n",
-      "4599 \n",
-      "4600 static __global__ void kernel_reduce_sum(const unsigned int size_z,\n",
-      "4601         const unsigned int nd,\n",
-      "4602         const int * dims_a,\n",
-      "4603         const int * log2_dims_a,\n",
-      "4604         const int * a_str,\n",
-      "4605         const float * a_data,\n",
-      "4606         const int * z_str,\n",
-      "4607         float * z_data)\n",
-      "4608 {\n",
-      "4609     const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;\n",
-      "4610     const unsigned int numThreads = blockDim.x * gridDim.x;\n",
-      "4611 \n",
-      "4612     //structure data contains the strides and dimensions of both a and z\n",
-      "4613     // a_dim[0], a_dim[1], ... a_dim[nd-1],\n",
-      "4614     // a_log2dim[0], a_log2dim[1], ... a_log2dim[nd-1],\n",
-      "4615     // a_str[0], ... a_str[nd-1],\n",
-      "4616     // z_str[0], ... z_str[nd-1]\n",
-      "4617     extern __shared__ int structure_data[];\n",
-      "4618     for (unsigned int i = threadIdx.x; i < nd; i += blockDim.x)\n",
-      "4619     {\n",
-      "4620         structure_data[i+0*nd] = dims_a[i];\n",
-      "4621         structure_data[i+1*nd] = log2_dims_a[i];\n",
-      "4622         structure_data[i+2*nd] = a_str[i];\n",
-      "4623         structure_data[i+3*nd] = z_str[i];\n",
-      "4624     }\n",
-      "4625     dims_a = structure_data;\n",
-      "4626     log2_dims_a = structure_data + nd;\n",
-      "4627     a_str = structure_data + 2*nd;\n",
-      "4628     z_str = structure_data + 3*nd;\n",
-      "4629 \n",
-      "4630     __syncthreads(); //wait for all the shared structure to be loaded\n",
-      "4631 \n",
-      "4632     for (unsigned int i = idx; i < size_z; i += numThreads)\n",
-      "4633     {\n",
-      "4634         unsigned int ii = i;\n",
-      "4635         const float * a_data_i = a_data;\n",
-      "4636         float * z_data_i = z_data;\n",
-      "4637         unsigned int n_reduce_elements = 1;\n",
-      "4638         unsigned int n_reduce_dims = 0;\n",
-      "4639         unsigned int reduce_dim0 = nd-1;\n",
-      "4640 \n",
-      "4641 \n",
-      "4642         //In this loop, we locate the initial element of the slice that we'd like to reduce with this thread\n",
-      "4643         //  At the same time, we [re]calculate the size of that slice (n_reduce_elements)\n",
-      "4644         for (unsigned int d = 0; d < nd; ++d)\n",
-      "4645         {\n",
-      "4646             if (a_str[d] && (!z_str[d])) // this means 'd' is a dimension we are reducing over\n",
-      "4647             {\n",
-      "4648                 n_reduce_elements *= dims_a[d];\n",
-      "4649                 n_reduce_dims += 1;\n",
-      "4650                 reduce_dim0 = (d < reduce_dim0) ? d : reduce_dim0;\n",
-      "4651             }\n",
-      "4652             else //'d' is not a dimension that we are reducing over\n",
-      "4653             {\n",
-      "4654                 unsigned int pos_d;\n",
-      "4655                 if (log2_dims_a[d]==-1) //TODO: when things are working, use this switch\n",
-      "4656                 {\n",
-      "4657                     // this branch is not preferred,\n",
-      "4658                     // because the manual said that integer mod and div operations are slow on gpu\n",
-      "4659                     pos_d = (ii % dims_a[d]);\n",
-      "4660                     ii = (ii / dims_a[d]);\n",
-      "4661                 }\n",
-      "4662                 else\n",
-      "4663                 {\n",
-      "4664                     pos_d = (ii & ((1 << log2_dims_a[d])-1)); //take the lower log2_dims bits\n",
-      "4665                     ii = (ii >> log2_dims_a[d]);  //shift those lower log2_dims bits off of ii\n",
-      "4666                 }\n",
-      "4667                 a_data_i += pos_d * a_str[d];\n",
-      "4668                 z_data_i += pos_d * z_str[d];\n",
-      "4669             }\n",
-      "4670         }\n",
-      "4671         // now we've got pointers a_data_i and z_data_i into element 0 of the slice over which we are reducing\n",
-      "4672         // do a similar loop\n",
-      "4673 \n",
-      "4674         float sum = 0.0f;\n",
-      "4675         switch(n_reduce_dims)\n",
-      "4676         {\n",
-      "4677             case 0:\n",
-      "4678                 {\n",
-      "4679                     sum = a_data_i[0];\n",
-      "4680                 }\n",
-      "4681                 break;\n",
-      "4682             case 1:\n",
-      "4683                 {\n",
-      "4684                     const int stride = a_str[reduce_dim0];\n",
-      "4685                     const float * a_data_i_max = a_data_i + dims_a[reduce_dim0] * stride;\n",
-      "4686                     while (a_data_i != a_data_i_max)\n",
-      "4687                     {\n",
-      "4688                         sum += a_data_i[0];\n",
-      "4689                         a_data_i += stride;\n",
-      "4690                     }\n",
-      "4691                 }\n",
-      "4692                 break;\n",
-      "4693             case 2:\n",
-      "4694                 {\n",
-      "4695                     int rd = reduce_dim0+1;\n",
-      "4696                     for (; rd < nd; ++rd)\n",
-      "4697                     {\n",
-      "4698                         if (a_str[rd] && (!z_str[rd])) // this means 'rd' is a dimension we are reducing over\n",
-      "4699                             break;\n",
-      "4700                     }\n",
-      "4701                     const int stride0 = a_str[reduce_dim0];\n",
-      "4702                     const int stride1 = a_str[rd];\n",
-      "4703                     for (int ii = 0; ii < dims_a[rd]; ++ii)\n",
-      "4704                     {\n",
-      "4705                         const float * a_data_ri = a_data_i + ii * stride1;\n",
-      "4706                         const float * a_data_ri_max = a_data_ri + dims_a[reduce_dim0] * stride0;\n",
-      "4707                         while (a_data_ri != a_data_ri_max)\n",
-      "4708                         {\n",
-      "4709                             sum += a_data_ri[0];\n",
-      "4710                             a_data_ri += stride0;\n",
-      "4711                         }\n",
-      "4712                     }\n",
-      "4713                 };\n",
-      "4714                 break;\n",
-      "4715             default:\n",
-      "4716                 {\n",
-      "4717                     for (unsigned int reduce_i = 0; reduce_i < n_reduce_elements; ++reduce_i)\n",
-      "4718                     {\n",
-      "4719                         //TODO: optimize this loop to work more like theano's Elemwise.  It's serial code.\n",
-      "4720                         unsigned int reduce_ii = reduce_i;\n",
-      "4721                         const float * a_data_ri = a_data_i;\n",
-      "4722 \n",
-      "4723                         //This loop finds the element in the a slice to add.\n",
-      "4724                         for (unsigned int rd = reduce_dim0; rd < nd; ++rd)\n",
-      "4725                         {\n",
-      "4726                             unsigned int pos_d;\n",
-      "4727                             if (a_str[rd] && (!z_str[rd])) // this means 'd' is a dimension we are reducing over\n",
-      "4728                             {\n",
-      "4729                                 if (log2_dims_a[rd]==-1)\n",
-      "4730                                 {\n",
-      "4731                                     // this branch is not preferred,\n",
-      "4732                                     // because the manual said that integer mod and div operations are slow on gpu\n",
-      "4733                                     pos_d = (reduce_ii % dims_a[rd]);\n",
-      "4734                                     reduce_ii = (reduce_ii / dims_a[rd]);\n",
-      "4735                                 }\n",
-      "4736                                 else\n",
-      "4737                                 {\n",
-      "4738                                     pos_d = (reduce_ii & ((1 << log2_dims_a[rd])-1)); //take the lower log2_dims bits\n",
-      "4739                                     reduce_ii = (reduce_ii >> log2_dims_a[rd]);  //shift those lower log2_dims bits off of ii\n",
-      "4740                                 }\n",
-      "4741                                 a_data_ri += pos_d * a_str[rd];\n",
-      "4742                             }\n",
-      "4743                         }\n",
-      "4744                         sum += a_data_ri[0];\n",
-      "4745                     }\n",
-      "4746                 }\n",
-      "4747         }\n",
-      "4748         z_data_i[0] = sum;\n",
-      "4749     }\n",
-      "4750 }\n",
-      "4751 \n",
-      "4752 static __global__ void kernel_reduce_sum_1011(\n",
-      "4753         const unsigned int d0,\n",
-      "4754         const unsigned int d1,\n",
-      "4755         const unsigned int d2,\n",
-      "4756         const unsigned int d3,\n",
-      "4757         const float *A, const int sA0, const int sA1, const int sA2, const int sA3,\n",
-      "4758         float * Z, const int sZ0)\n",
-      "4759 {\n",
-      "4760     const int threadCount = blockDim.x * blockDim.y * blockDim.z;\n",
-      "4761     const int threadNum = threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;\n",
-      "4762     extern __shared__ float buf[];\n",
-      "4763     float mysum = 0.0f;\n",
-      "4764 \n",
-      "4765     if (warpSize != 32)\n",
-      "4766     {\n",
-      "4767         return;  //TODO: set error code\n",
-      "4768     }\n",
-      "4769 \n",
-      "4770     for (int i0 = threadIdx.z; i0 < d0; i0 += blockDim.z)\n",
-      "4771     {\n",
-      "4772         float Ai = A[i0 * sA0 + blockIdx.x * sA1 + threadIdx.y * sA2 + threadIdx.x * sA3];\n",
-      "4773         mysum += Ai;\n",
-      "4774     }\n",
-      "4775     buf[threadNum] = mysum;\n",
-      "4776     __syncthreads();\n",
-      "4777 \n",
-      "4778     // rest of function is handled by one warp\n",
-      "4779     if (threadNum < warpSize)\n",
-      "4780     {\n",
-      "4781         for (int i = threadNum + warpSize; i < threadCount; i += warpSize)\n",
-      "4782         {\n",
-      "4783             mysum += buf[i];\n",
-      "4784         }\n",
-      "4785         buf[threadNum] = mysum;\n",
-      "4786         if (threadNum < 16)\n",
-      "4787         {\n",
-      "4788             //reduce so that threadNum 0 has the sum of everything\n",
-      "4789             if(threadNum + 16 < threadCount) buf[threadNum] += buf[threadNum+16];\n",
-      "4790             if(threadNum + 8 < threadCount) buf[threadNum] += buf[threadNum+8];\n",
-      "4791             if(threadNum + 4 < threadCount) buf[threadNum] += buf[threadNum+4];\n",
-      "4792             if(threadNum + 2 < threadCount) buf[threadNum] += buf[threadNum+2];\n",
-      "4793             if(threadNum + 1 < threadCount) buf[threadNum] += buf[threadNum+1];\n",
-      "4794             if (threadNum == 0)\n",
-      "4795             {\n",
-      "4796                 Z[blockIdx.x*sZ0] = buf[0];\n",
-      "4797             }\n",
-      "4798         }\n",
-      "4799     }\n",
-      "4800 }\n",
-      "4801 /**\n",
-      "4802  * Dimensions in which the self has size 1 and A has size > 1 are considered summing dimensions\n",
-      "4803  * Dimensions in which self has size > 1 and A has size > 1 are considered non-summing dimensions, and in this case their sizes must be equal.\n",
-      "4804  */\n",
-      "4805 int\n",
-      "4806 CudaNdarray_reduce_sum(CudaNdarray * self, CudaNdarray * A)\n",
-      "4807 {\n",
-      "4808     int verbose = 0;\n",
-      "4809     //check input rank\n",
-      "4810     if (self->nd != A->nd)\n",
-      "4811     {\n",
-      "4812         PyErr_Format(PyExc_TypeError, \"Rank mismatch in CudaNdarray_sum: %i vs %i\", self->nd, A->nd);\n",
-      "4813         return -1;\n",
-      "4814     }\n",
-      "4815     for (int i = 0; i < self->nd; ++i)\n",
-      "4816     {\n",
-      "4817         if ((CudaNdarray_HOST_DIMS(self)[i] > 1) && (CudaNdarray_HOST_DIMS(self)[i] != CudaNdarray_HOST_DIMS(A)[i]))\n",
-      "4818         {\n",
-      "4819             PyErr_Format(PyExc_TypeError, \"Dimension mismatch in CudaNdarray_sum: self->dim[%i] == %i , A->dim[%i] = %i\",\n",
-      "4820                     i, CudaNdarray_HOST_DIMS(self)[i], i, CudaNdarray_HOST_DIMS(A)[i]);\n",
-      "4821             return -1;\n",
-      "4822         }\n",
-      "4823     }\n",
-      "4824 \n",
-      "4825     int n_summations = (unsigned int)CudaNdarray_SIZE(self);\n",
-      "4826     if (verbose)\n",
-      "4827     {\n",
-      "4828         std::cerr << \"reduce_sum n_summations \" << n_summations  << '\\n';\n",
-      "4829         std::cerr << \"reduce_sum nd \" << self->nd  << '\\n';\n",
-      "4830         fprint_CudaNdarray(stderr, A);\n",
-      "4831         fprint_CudaNdarray(stderr, self);\n",
-      "4832     }\n",
-      "4833     if (0 && (A->nd == 4) //check to see if kernel_reduce_sum_1011 applies\n",
-      "4834             && (CudaNdarray_HOST_DIMS(self)[0] == 1)\n",
-      "4835             && (CudaNdarray_HOST_DIMS(self)[2] == 1)\n",
-      "4836             && (CudaNdarray_HOST_DIMS(self)[3] == 1)\n",
-      "4837        )\n",
-      "4838     {\n",
-      "4839         dim3 n_threads(CudaNdarray_HOST_DIMS(A)[3], CudaNdarray_HOST_DIMS(A)[2]);\n",
-      "4840         dim3 n_blocks(CudaNdarray_HOST_DIMS(A)[1]);\n",
-      "4841         while (n_threads.x * n_threads.y * n_threads.z < NUM_VECTOR_OP_THREADS_PER_BLOCK) ++n_threads.z;\n",
-      "4842         n_threads.z -= 1;\n",
-      "4843         if (n_threads.z > 64) n_threads.z = 64;\n",
-      "4844         if (n_threads.z)\n",
-      "4845         {\n",
-      "4846             if (verbose) printf(\"trying kernel_reduce_sum_1011\\n\");\n",
-      "4847             int n_shared = sizeof(float) * n_threads.x * n_threads.y * n_threads.z;\n",
-      "4848             kernel_reduce_sum_1011<<<n_blocks, n_threads, n_shared>>>(\n",
-      "4849                     CudaNdarray_HOST_DIMS(A)[0],\n",
-      "4850                     CudaNdarray_HOST_DIMS(A)[1],\n",
-      "4851                     CudaNdarray_HOST_DIMS(A)[2],\n",
-      "4852                     CudaNdarray_HOST_DIMS(A)[3],\n",
-      "4853                     CudaNdarray_DEV_DATA(A),\n",
-      "4854                     CudaNdarray_HOST_STRIDES(A)[0],\n",
-      "4855                     CudaNdarray_HOST_STRIDES(A)[1],\n",
-      "4856                     CudaNdarray_HOST_STRIDES(A)[2],\n",
-      "4857                     CudaNdarray_HOST_STRIDES(A)[3],\n",
-      "4858                     CudaNdarray_DEV_DATA(self),\n",
-      "4859                     CudaNdarray_HOST_STRIDES(self)[1]);\n",
-      "4860             CNDA_THREAD_SYNC;\n",
-      "4861             if (cudaSuccess == cudaGetLastError()) return 0;\n",
-      "4862             if (verbose) printf(\"failed, falling back to kernel_reduce_sum\\n\");\n",
-      "4863         }\n",
-      "4864     }\n",
-      "4865 \n",
-      "4866     int n_threads_per_block = std::min(n_summations,\n",
-      "4867             NUM_VECTOR_OP_THREADS_PER_BLOCK);\n",
-      "4868     int n_blocks = std::min(ceil_intdiv(n_summations,n_threads_per_block),\n",
-      "4869             NUM_VECTOR_OP_BLOCKS);\n",
-      "4870     int n_structure_cache = self->nd * 4 * sizeof(int);\n",
-      "4871 \n",
-      "4872     if (verbose)\n",
-      "4873     {\n",
-      "4874         std::cerr << \"n_blocks, n_threads_per_block \" << n_blocks << ' ' << n_threads_per_block  << '\\n';\n",
-      "4875     }\n",
-      "4876     assert (self->nd > 0);\n",
-      "4877     assert (self->nd == A->nd);\n",
-      "4878     kernel_reduce_sum<<<n_blocks, n_threads_per_block, n_structure_cache>>>(\n",
-      "4879             n_summations,\n",
-      "4880             self->nd,\n",
-      "4881             CudaNdarray_DEV_DIMS(A),\n",
-      "4882             CudaNdarray_DEV_LOG2DIMS(A),\n",
-      "4883             CudaNdarray_DEV_STRIDES(A),\n",
-      "4884             CudaNdarray_DEV_DATA(A),\n",
-      "4885             CudaNdarray_DEV_STRIDES(self),\n",
-      "4886             CudaNdarray_DEV_DATA(self));\n",
-      "4887     CNDA_THREAD_SYNC;\n",
-      "4888     cudaError_t err = cudaGetLastError();\n",
-      "4889     if (cudaSuccess != err)\n",
-      "4890     {\n",
-      "4891         PyErr_Format(PyExc_RuntimeError, \"Cuda error: %s: %s.\\n\", \"kernel_reduce_sum\", cudaGetErrorString(err));\n",
-      "4892         return -1;\n",
-      "4893     }\n",
-      "4894     return 0;\n",
-      "4895 }\n",
-      "4896 int\n",
-      "4897 CudaNdarray_reduce_prod(CudaNdarray * self, const CudaNdarray * A)\n",
-      "4898 {\n",
-      "4899     PyErr_SetString(PyExc_NotImplementedError, \"\");\n",
-      "4900     return -1;\n",
-      "4901 }\n",
-      "4902 int\n",
-      "4903 CudaNdarray_reduce_min(CudaNdarray * self, const CudaNdarray * A)\n",
-      "4904 {\n",
-      "4905     PyErr_SetString(PyExc_NotImplementedError, \"\");\n",
-      "4906     return -1;\n",
-      "4907 }\n",
-      "4908 int\n",
-      "4909 CudaNdarray_reduce_max(CudaNdarray * self, const CudaNdarray * A)\n",
-      "4910 {\n",
-      "4911     PyErr_SetString(PyExc_NotImplementedError, \"\");\n",
-      "4912     return -1;\n",
-      "4913 }\n",
-      "4914 \n",
-      "4915 \n",
-      "4916 /**\n",
-      "4917  *\n",
-      "4918  *  pattern is a permutation of [0, 1, ... self->nd-1] with the following twists:\n",
-      "4919  *  - an element 'd' of the permutation can be dropped if CudaNdarray_HOST_DIMS(self)[d] == 1\n",
-      "4920  *  - any number of '-1' elements can be in the pattern, and they will cause new ranks (with dim==1) to be inserted.\n",
-      "4921  *\n",
-      "4922  *  For example, if CudaNdarray_HOST_DIMS(self) == [4, 5, 1, 6], and pattern = [0,3,-1,-1, 1], then CudaNdarray_HOST_DIMS(self) would be modified to become:\n",
-      "4923  *     [4, 6, 1, 1, 5] (we dropped the original dim[2]==1, and inserted two singleton dimensions with the -1s.\n",
-      "4924  */\n",
-      "4925 int\n",
-      "4926 CudaNdarray_dimshuffle(CudaNdarray * self, unsigned int len, const int * pattern)\n",
-      "4927 {\n",
-      "4928     //TODO: pass a workspace pointer to avoid the internal malloc\n",
-      "4929     int * newdims = (int *)malloc(sizeof(int) * (len + len + self->nd)); //we tack on the taken buffer here for speed of not having to malloc twice.\n",
-      "4930     int * newstrides = newdims + len;\n",
-      "4931     int * dims_taken = newstrides + len;\n",
-      "4932     if (!newdims)\n",
-      "4933     {\n",
-      "4934         PyErr_SetString(PyExc_MemoryError, \"CudaNdarray_dimshuffle: Failed to allocate temporary space\");\n",
-      "4935         return -1;\n",
-      "4936     }\n",
-      "4937     for (int i = 0; i < self->nd; ++i)\n",
-      "4938     {\n",
-      "4939         dims_taken[i] = 0;\n",
-      "4940     }\n",
-      "4941     for (int i = 0; i < len; ++i)\n",
-      "4942     {\n",
-      "4943         if (pattern[i] < 0)\n",
-      "4944         {\n",
-      "4945             newdims[i] = 1;\n",
-      "4946             newstrides[i] = 0;\n",
-      "4947         }\n",
-      "4948         else if(dims_taken[pattern[i]])\n",
-      "4949         {\n",
-      "4950             PyErr_Format(PyExc_ValueError, \"Cudandarray_dimshuffle: invalid pattern for Cudandarray_dimshuffle. You used the dimensions %d multiple time\",\n",
-      "4951                          pattern[i]);\n",
-      "4952             free(newdims);\n",
-      "4953             return -1;\n",
-      "4954         }\n",
-      "4955         else if (pattern[i]>= self->nd)\n",
-      "4956         {\n",
-      "4957             PyErr_Format(PyExc_ValueError, \"Cudandarray_dimshuffle: invalid pattern for Cudandarray_dimshuffle. You asked for a dimensions that don't exist %d for a %d dims CudaNdarray\",\n",
-      "4958                          pattern[i], self->nd);\n",
-      "4959             free(newdims);\n",
-      "4960             return -1;\n",
-      "4961         }\n",
-      "4962         else\n",
-      "4963         {\n",
-      "4964             newdims[i] = CudaNdarray_HOST_DIMS(self)[pattern[i]];\n",
-      "4965             newstrides[i] = CudaNdarray_HOST_STRIDES(self)[pattern[i]];\n",
-      "4966             dims_taken[pattern[i]] = 1;\n",
-      "4967         }\n",
-      "4968     }\n",
-      "4969     //Check if we dropped not broadcastable dims\n",
-      "4970     for (int i = 0; i < self->nd; ++i)\n",
-      "4971     {\n",
-      "4972         if (dims_taken[i]==0 && CudaNdarray_HOST_DIMS(self)[i]!=1)\n",
-      "4973         {\n",
-      "4974             PyErr_SetString(PyExc_ValueError, \"Cudandarray_dimshuffle: You cannot drop a non-broadcastable dimension.\");\n",
-      "4975             free(newdims);\n",
-      "4976             return -1;\n",
-      "4977         }\n",
-      "4978     }\n",
-      "4979     //swap this structure in for the one in self, and sync to the card\n",
-      "4980     if (CudaNdarray_set_nd(self, len))\n",
-      "4981     {\n",
-      "4982         free(newdims);\n",
-      "4983         return -1;\n",
-      "4984     }\n",
-      "4985     for (int i = 0; i < len; ++i)\n",
-      "4986     {\n",
-      "4987         CudaNdarray_set_dim(self, i, newdims[i]);\n",
-      "4988         CudaNdarray_set_stride(self, i, newstrides[i]);\n",
-      "4989     }\n",
-      "4990     if (cnda_copy_structure_to_device(self))\n",
-      "4991     {\n",
-      "4992         free(newdims);\n",
-      "4993         return -1;\n",
-      "4994     }\n",
-      "4995     free(newdims);\n",
-      "4996     return 0;\n",
-      "4997 }\n",
-      "4998 \n",
-      "4999 \n",
-      "5000 \n",
-      "5001 /**\n",
-      "5002  *\n",
-      "5003  *  This is the function that bind to python.\n",
-      "5004  *  See CudaNdarray_dimshuffle to call from C.\n",
-      "5005  *  We use -1 to mean 'x' as in Tensor Dimshuffle.\n",
-      "5006  */\n",
-      "5007 PyObject *\n",
-      "5008 CudaNdarray_Dimshuffle(PyObject* _unused, PyObject* args)\n",
-      "5009 {\n",
-      "5010     PyObject * self = NULL;\n",
-      "5011     PyObject * pattern_object = NULL;\n",
-      "5012     int * pattern = NULL;\n",
-      "5013     PyObject * rval = NULL;\n",
-      "5014     int success = -1;\n",
-      "5015     //const int * dims = NULL;\n",
-      "5016 \n",
-      "5017     //args should consist of two python objects (\"OO\")\n",
-      "5018     if (! PyArg_ParseTuple(args, \"OO\", &self, &pattern_object))\n",
-      "5019         return NULL;\n",
-      "5020 \n",
-      "5021     if (!CudaNdarray_Check(self) )\n",
-      "5022     {\n",
-      "5023         PyErr_SetString(PyExc_TypeError, \"First argument to cuda_ndarray.dimshuffle must be a CudaNdarray\");\n",
-      "5024         return NULL;\n",
-      "5025     }\n",
-      "5026 \n",
-      "5027     //parse pattern_object into int * pattern\n",
-      "5028 \n",
-      "5029     Py_ssize_t pattern_dim =  PyObject_Length(pattern_object);\n",
-      "5030 \n",
-      "5031     if (pattern_dim < 0)\n",
-      "5032     {\n",
-      "5033         PyErr_SetString(PyExc_TypeError, \"Couldn't get length of third argument to cuda_ndarray.dimshuffle\");\n",
-      "5034         return NULL;\n",
-      "5035     }\n",
-      "5036 \n",
-      "5037     pattern = (int *) malloc( pattern_dim * sizeof(int));\n",
-      "5038 \n",
-      "5039     for (Py_ssize_t i = 0; i < pattern_dim; i++)\n",
-      "5040     {\n",
-      "5041         PyObject * idx = PyLong_FromSsize_t(i);\n",
-      "5042 \n",
-      "5043         if (idx == NULL)\n",
-      "5044         {\n",
-      "5045             PyErr_SetString(PyExc_Exception, \"Couldn't make long object to loop over list/tuple\");\n",
-      "5046             goto CudaNdarray_dimshuffle_fail;\n",
-      "5047         }\n",
-      "5048 \n",
-      "5049         long elem_value = 0;\n",
-      "5050 \n",
-      "5051         PyObject * elem = PyObject_GetItem(pattern_object, idx);\n",
-      "5052 \n",
-      "5053         if (elem == NULL)\n",
-      "5054         {\n",
-      "5055             Py_XDECREF( elem);\n",
-      "5056             PyErr_SetString(PyExc_ValueError, \"Third argument to dimshuffle must be list or tuple of integers\");\n",
-      "5057             goto CudaNdarray_dimshuffle_fail;\n",
-      "5058         }\n",
-      "5059 \n",
-      "5060         elem_value = PyInt_AsLong(elem);\n",
-      "5061 \n",
-      "5062         if (elem_value == -1 && PyErr_Occurred() )\n",
-      "5063         {\n",
-      "5064             Py_XDECREF(elem);\n",
-      "5065             PyErr_SetString(PyExc_ValueError, \"Third argument to dimshuffle must be list or tuple of integers\");\n",
-      "5066             goto CudaNdarray_dimshuffle_fail;\n",
-      "5067         }\n",
-      "5068 \n",
-      "5069         pattern[i] = elem_value;\n",
-      "5070 \n",
-      "5071         Py_XDECREF( elem );\n",
-      "5072         Py_XDECREF( idx );\n",
-      "5073     }\n",
-      "5074 \n",
-      "5075     //allocate rval\n",
-      "5076     rval =  (PyObject *) CudaNdarray_View((CudaNdarray *) self);\n",
-      "5077 \n",
-      "5078     if (rval == NULL)\n",
-      "5079     {\n",
-      "5080         //CudaNdarray_New should have set the exception string\n",
-      "5081         goto CudaNdarray_dimshuffle_fail;\n",
-      "5082     }\n",
-      "5083 \n",
-      "5084 \n",
-      "5085     //printf(\"pattern_dim: %d\\n\",pattern_dim);\n",
-      "5086     //printf(\"pattern: %d %d\\n\",pattern[0],pattern[1]);\n",
-      "5087     //dims = CudaNdarray_HOST_DIMS( (CudaNdarray *) self);\n",
-      "5088     //printf(\"dims before: %d %d\\n\",dims[0],dims[1]);\n",
-      "5089 \n",
-      "5090     success = CudaNdarray_dimshuffle((CudaNdarray *) rval, pattern_dim, pattern);\n",
-      "5091 \n",
-      "5092     if (success != 0)\n",
-      "5093     {\n",
-      "5094         //Exception string should already be set by CudaNdarray_dimshuffle\n",
-      "5095         goto CudaNdarray_dimshuffle_fail;\n",
-      "5096     }\n",
-      "5097 \n",
-      "5098     free(pattern);\n",
-      "5099 \n",
-      "5100     return rval;\n",
-      "5101 \n",
-      "5102     CudaNdarray_dimshuffle_fail:\n",
-      "5103 \n",
-      "5104     if (pattern != NULL)\n",
-      "5105         free(pattern);\n",
-      "5106 \n",
-      "5107     Py_XDECREF(rval);\n",
-      "5108     return NULL;\n",
-      "5109 }\n",
-      "5110 \n",
-      "5111 \n",
-      "5112 int\n",
-      "5113 cnda_structure_size(int nd)\n",
-      "5114 {\n",
-      "5115     // dim0, dim1, ...\n",
-      "5116     // str0, str1, ...\n",
-      "5117     // log2(dim0), log2(dim1), ...\n",
-      "5118     return nd + nd + nd;\n",
-      "5119 }\n",
-      "5120 \n",
-      "5121 const int *\n",
-      "5122 CudaNdarray_HOST_DIMS(const CudaNdarray * self)\n",
-      "5123 {\n",
-      "5124     return self->host_structure;\n",
-      "5125 }\n",
-      "5126 \n",
-      "5127 const int *\n",
-      "5128 CudaNdarray_HOST_STRIDES(const CudaNdarray * self)\n",
-      "5129 {\n",
-      "5130     return self->host_structure + self->nd;\n",
-      "5131 }\n",
-      "5132 const int *\n",
-      "5133 CudaNdarray_HOST_LOG2DIMS(const CudaNdarray * self)\n",
-      "5134 {\n",
-      "5135     return self->host_structure + 2*self->nd;\n",
-      "5136 }\n",
-      "5137 \n",
-      "5138 int\n",
-      "5139 CudaNdarray_EqualAndIgnore(CudaNdarray *cnda1, CudaNdarray *cnda2, int ignoreSync, int ignoreBase)\n",
-      "5140 {\n",
-      "5141     int verbose = 0;\n",
-      "5142 \n",
-      "5143     if (!ignoreSync && cnda1->dev_structure_fresh != cnda2->dev_structure_fresh)\n",
-      "5144     {\n",
-      "5145         if(verbose) fprintf(stdout, \"CUDANDARRAY_EQUAL FAILED : 1\\n\");\n",
-      "5146         return 0;\n",
-      "5147     }\n",
-      "5148 \n",
-      "5149     if (cnda1->nd != cnda2->nd)\n",
-      "5150     {\n",
-      "5151         if(verbose) fprintf(stdout, \"CUDANDARRAY_EQUAL FAILED : 2\\n\");\n",
-      "5152         return 0;\n",
-      "5153     }\n",
-      "5154 \n",
-      "5155     for (int i=0; i < 2*cnda1->nd; i++)\n",
-      "5156     {\n",
-      "5157         if (cnda1->host_structure[i] != cnda2->host_structure[i])\n",
-      "5158         {\n",
-      "5159             if(verbose)\n",
-      "5160                 fprintf(stdout, \"CUDANDARRAY_EQUAL : host_structure : %d, %d, %d\\n\", i, cnda1->host_structure[i], cnda2->host_structure[i]);\n",
-      "5161             return 0;\n",
-      "5162         }\n",
-      "5163     }\n",
-      "5164 \n",
-      "5165     if (!ignoreBase && cnda1->base != cnda2->base)\n",
-      "5166     {\n",
-      "5167         if(verbose) fprintf(stdout, \"CUDANDARRAY_EQUAL FAILED : 4\");\n",
-      "5168         return 0;\n",
-      "5169     }\n",
-      "5170     else if (cnda1->data_allocated != cnda2->data_allocated)\n",
-      "5171     {\n",
-      "5172         if(verbose) fprintf(stdout, \"CUDANDARRAY_EQUAL FAILED : 5\");\n",
-      "5173         return 0;\n",
-      "5174     }\n",
-      "5175     else if (cnda1->data_allocated && cnda1->devdata != cnda2->devdata)\n",
-      "5176     {\n",
-      "5177         if(verbose) fprintf(stdout, \"CUDANDARRAY_EQUAL FAILED : 6\");\n",
-      "5178         // no need to check devdata if data is not allocated\n",
-      "5179         return 0;\n",
-      "5180     }\n",
-      "5181 \n",
-      "5182     return 1;\n",
-      "5183 }\n",
-      "5184 \n",
-      "5185 \n",
-      "5186 int\n",
-      "5187 CudaNdarray_Equal(CudaNdarray *cnda1, CudaNdarray *cnda2)\n",
-      "5188 {\n",
-      "5189     return CudaNdarray_EqualAndIgnore(cnda1, cnda2, 0, 0);\n",
-      "5190 }\n",
-      "5191 \n",
-      "5192 int\n",
-      "5193 cnda_copy_structure_to_device(const CudaNdarray * self)\n",
-      "5194 {\n",
-      "5195     //If the device structure do not exists, create it.\n",
-      "5196     //We allocate it here as we do not need it often.\n",
-      "5197     //In fact, we need it so infrequently that we expect\n",
-      "5198     //that most object won't need it. Not allocating it\n",
-      "5199     //save a significant when creating object.\n",
-      "5200     //This speed up a benchmark by 8% with the gc.\n",
-      "5201     if (!self->dev_structure)\n",
-      "5202     {\n",
-      "5203         int struct_size = cnda_structure_size(self->nd);\n",
-      "5204         if (struct_size)\n",
-      "5205         {\n",
-      "5206             self->dev_structure = (int*)device_malloc(struct_size* sizeof(int));\n",
-      "5207             if (NULL == self->dev_structure)\n",
-      "5208             {\n",
-      "5209                 return -1;\n",
-      "5210             }\n",
-      "5211         }\n",
-      "5212     }\n",
-      "5213     if (cublasSetVector(cnda_structure_size(self->nd),\n",
-      "5214                         sizeof(int),\n",
-      "5215                         self->host_structure,\n",
-      "5216                         1,\n",
-      "5217                         self->dev_structure,\n",
-      "5218                         1) != CUBLAS_STATUS_SUCCESS)\n",
-      "5219     {\n",
-      "5220         PyErr_SetString(PyExc_RuntimeError, \"error copying structure to device memory\");\n",
-      "5221         return -1;\n",
-      "5222     }\n",
-      "5223     self->dev_structure_fresh = 1;\n",
-      "5224     return 0;\n",
-      "5225 }\n",
-      "5226 \n",
-      "5227 const int *\n",
-      "5228 CudaNdarray_DEV_DIMS(const CudaNdarray * self)\n",
-      "5229 {\n",
-      "5230     if (!self->dev_structure_fresh)\n",
-      "5231     {\n",
-      "5232         if (cnda_copy_structure_to_device(self))\n",
-      "5233             return NULL;\n",
-      "5234     }\n",
-      "5235     return self->dev_structure;\n",
-      "5236 }\n",
-      "5237 const int *\n",
-      "5238 CudaNdarray_DEV_STRIDES(const CudaNdarray * self)\n",
-      "5239 {\n",
-      "5240     if (!self->dev_structure_fresh)\n",
-      "5241     {\n",
-      "5242         if (cnda_copy_structure_to_device(self))\n",
-      "5243             return NULL;\n",
-      "5244     }\n",
-      "5245     return self->dev_structure + self->nd;\n",
-      "5246 }\n",
-      "5247 const int *\n",
-      "5248 CudaNdarray_DEV_LOG2DIMS(const CudaNdarray * self)\n",
-      "5249 {\n",
-      "5250     if (!self->dev_structure_fresh)\n",
-      "5251     {\n",
-      "5252         if (cnda_copy_structure_to_device(self))\n",
-      "5253             return NULL;\n",
-      "5254     }\n",
-      "5255     return self->dev_structure + 2*self->nd;\n",
-      "5256 }\n",
-      "5257 float *\n",
-      "5258 CudaNdarray_DEV_DATA(const CudaNdarray * self)\n",
-      "5259 {\n",
-      "5260     return self->devdata;\n",
-      "5261 }\n",
-      "5262 \n",
-      "5263 /**\n",
-      "5264  * Return the number of elements in the ndarray (product of the dimensions)\n",
-      "5265  */\n",
-      "5266 size_t\n",
-      "5267 CudaNdarray_SIZE(const CudaNdarray *self)\n",
-      "5268 {\n",
-      "5269     if (self->nd == -1) return 0;\n",
-      "5270     size_t size = 1;\n",
-      "5271     for (int i = 0; i < self->nd; ++i)\n",
-      "5272     {\n",
-      "5273         size *= CudaNdarray_HOST_DIMS(self)[i];\n",
-      "5274     }\n",
-      "5275     return size;\n",
-      "5276 }\n",
-      "5277 \n",
-      "5278 PyObject *\n",
-      "5279 CudaNdarray_SIZE_Object(const CudaNdarray *self, void *closure)\n",
-      "5280 {\n",
-      "5281     return PyInt_FromLong(CudaNdarray_SIZE(self));\n",
-      "5282 }\n",
-      "5283 \n",
-      "5284 int CudaNdarray_set_device_data(CudaNdarray * self, float * data, const CudaNdarray * base)\n",
-      "5285 {\n",
-      "5286     return CudaNdarray_set_device_data(self, data, (PyObject *) base);\n",
-      "5287 }\n",
-      "5288 \n",
-      "5289 PyObject * CudaNdarray_IS_C_Contiguous(CudaNdarray * self)\n",
-      "5290 {\n",
-      "5291     return PyBool_FromLong(CudaNdarray_is_c_contiguous(self));\n",
-      "5292 }\n",
-      "5293 \n",
-      "5294 int fprint_CudaNdarray(FILE * fd, const CudaNdarray *self)\n",
-      "5295 {\n",
-      "5296     cudaError_t err = cudaGetLastError();\n",
-      "5297     if( cudaSuccess != err)\n",
-      "5298     {\n",
-      "5299         PyErr_Format(PyExc_RuntimeError,\n",
-      "5300                      \"Cuda error: %s: %s.\",\n",
-      "5301                      \"fprint_CudaNdarray was called with an uncleared error\",\n",
-      "5302                      cudaGetErrorString(err));\n",
-      "5303         return -1;\n",
-      "5304     }\n",
-      "5305     fprintf(fd, \"CudaNdarray <%p, %p> nd=%i dev_structure_fresh=%d data_allocated=%d\\n\",\n",
-      "5306             self, self->devdata, self->nd, self->dev_structure_fresh, self->data_allocated);\n",
-      "5307     fprintf(fd, \"\\tHOST_DIMS:      \");\n",
-      "5308     for (int i = 0; i < self->nd; ++i)\n",
-      "5309     {\n",
-      "5310         fprintf(fd, \"%i\\t\", CudaNdarray_HOST_DIMS(self)[i]);\n",
-      "5311     }\n",
-      "5312     fprintf(fd, \"\\n\\tHOST_STRIDES: \");\n",
-      "5313     for (int i = 0; i < self->nd; ++i)\n",
-      "5314     {\n",
-      "5315         fprintf(fd, \"%i\\t\", CudaNdarray_HOST_STRIDES(self)[i]);\n",
-      "5316     }\n",
-      "5317 \n",
-      "5318     if (self->dev_structure)\n",
-      "5319     {\n",
-      "5320         int data=0;\n",
-      "5321         fprintf(fd, \"\\n\\tDEV_DIMS:      \");\n",
-      "5322         for (int i = 0; i < self->nd; ++i)\n",
-      "5323         {\n",
-      "5324             cublasGetVector(1, sizeof(int),\n",
-      "5325                             self->dev_structure+i, 1,\n",
-      "5326                             &data, 1);\n",
-      "5327             fprintf(fd, \"%i\\t\", data);\n",
-      "5328         }\n",
-      "5329         fprintf(fd, \"\\n\\tDEV_STRIDES: \");\n",
-      "5330         for (int i = 0; i < self->nd; ++i)\n",
-      "5331         {\n",
-      "5332             cublasGetVector(1, sizeof(int),\n",
-      "5333                             self->dev_structure + self->nd+i, 1,\n",
-      "5334                             &data, 1);\n",
-      "5335             fprintf(fd, \"%i \\t\", data);\n",
-      "5336         }\n",
-      "5337         fprintf(fd, \"\\n\");\n",
-      "5338     }\n",
-      "5339     else\n",
-      "5340     {\n",
-      "5341         fprintf(fd, \"\\n\\tdev_structure not allocated\\n\");\n",
-      "5342     }\n",
-      "5343 \n",
-      "5344     err = cudaGetLastError();\n",
-      "5345     if( cudaSuccess != err)\n",
-      "5346     {\n",
-      "5347         PyErr_Format(PyExc_RuntimeError,\n",
-      "5348                      \"Cuda error: %s: %s.\",\n",
-      "5349                      \"fprint_CudaNdarray\",\n",
-      "5350                      cudaGetErrorString(err));\n",
-      "5351         return -1;\n",
-      "5352     }\n",
-      "5353     return 0;\n",
-      "5354 }\n",
-      "5355 \n",
-      "5356 \n",
-      "5357 int CudaNdarray_prep_output(CudaNdarray ** arr, int nd,\n",
-      "5358                             const int * dims, int fortran)\n",
-      "5359 {\n",
-      "5360     bool allocated = false;\n",
-      "5361     if (*arr == NULL)\n",
-      "5362     {\n",
-      "5363         // This allocates the metadata but not the data\n",
-      "5364         *arr = (CudaNdarray *) CudaNdarray_new_nd(nd);\n",
-      "5365         if (*arr == NULL)\n",
-      "5366             return -1;\n",
-      "5367         allocated = true;\n",
-      "5368     }\n",
-      "5369 \n",
-      "5370     if (CudaNdarray_alloc_contiguous(*arr, nd, dims, fortran))\n",
-      "5371     {\n",
-      "5372         if (allocated)\n",
-      "5373         {\n",
-      "5374             Py_DECREF(*arr);\n",
-      "5375             *arr = NULL;\n",
-      "5376         }\n",
-      "5377         return -1;\n",
-      "5378     }\n",
-      "5379     return 0;\n",
-      "5380 }\n",
-      "5381 \n",
-      "5382 \n",
-      "5383 /*\n",
-      "5384   Local Variables:\n",
-      "5385   mode:c++\n",
-      "5386   c-basic-offset:4\n",
-      "5387   c-file-style:\"stroustrup\"\n",
-      "5388   indent-tabs-mode:nil\n",
-      "5389   fill-column:79\n",
-      "5390   End:\n",
-      "5391 */\n",
-      "5392 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:textwidth=79 :\n",
-      "5393 \n",
-      "===============================\n",
-      "In file included from mod.cu:10:0:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:17:0: warning: \"PyString_Check\" redefined\n",
-      " #define PyString_Check PyUnicode_Check\n",
-      " \n",
-      "In file included from /home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:11:0,\n",
-      "                 from mod.cu:10:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/npy_3kcompat.h:71:0: note: this is the location of the previous definition\n",
-      " #define PyString_Check PyBytes_Check\n",
-      " \n",
-      "In file included from mod.cu:10:0:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:18:0: warning: \"PyString_FromString\" redefined\n",
-      " #define PyString_FromString PyUnicode_FromString\n",
-      " \n",
-      "In file included from /home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:11:0,\n",
-      "                 from mod.cu:10:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/npy_3kcompat.h:73:0: note: this is the location of the previous definition\n",
-      " #define PyString_FromString PyBytes_FromString\n",
-      " \n",
-      "In file included from mod.cu:10:0:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:19:0: warning: \"PyString_AsString\" redefined\n",
-      " #define PyString_AsString PyUnicode_AsUTF8\n",
-      " \n",
-      "In file included from /home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:11:0,\n",
-      "                 from mod.cu:10:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/npy_3kcompat.h:80:0: note: this is the location of the previous definition\n",
-      " #define PyString_AsString PyBytes_AsString\n",
-      " \n",
-      "In file included from mod.cu:10:0:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:20:0: warning: \"PyString_FromStringAndSize\" redefined\n",
-      " #define PyString_FromStringAndSize PyUnicode_FromStringAndSize\n",
-      " \n",
-      "In file included from /home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:11:0,\n",
-      "                 from mod.cu:10:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/npy_3kcompat.h:74:0: note: this is the location of the previous definition\n",
-      " #define PyString_FromStringAndSize PyBytes_FromStringAndSize\n",
-      " \n",
-      "In file included from mod.cu:10:0:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:21:0: warning: \"PyString_Size\" redefined\n",
-      " #define PyString_Size PyUnicode_GET_SIZE\n",
-      " \n",
-      "In file included from /home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda/cuda_ndarray.cuh:11:0,\n",
-      "                 from mod.cu:10:\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/npy_3kcompat.h:82:0: note: this is the location of the previous definition\n",
-      " #define PyString_Size PyBytes_Size\n",
-      " \n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h(84): error: expected a \"}\"\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h(89): warning: parsing restarts here after previous syntax error\n",
-      "/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h(446): error: identifier \"NPY_NTYPES_ABI_COMPATIBLE\" is undefined\n",
-      "mod.cu(940): warning: pointless comparison of unsigned integer with zero\n",
-      "mod.cu(3000): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3003): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3005): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3008): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3010): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3013): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3016): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3019): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3021): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3024): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3026): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3029): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3031): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3034): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3037): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3040): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3042): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3045): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3047): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3050): warning: conversion from a string literal to \"char *\" is deprecated\n",
-      "mod.cu(3074): warning: statement is unreachable\n",
-      "2 errors detected in the compilation of \"/tmp/tmpxft_00005d39_00000000-8_mod.cpp1.ii\".\n",
-      "ERROR (theano.sandbox.cuda): Failed to compile cuda_ndarray.cu: ('nvcc return status', 1, 'for cmd', 'nvcc -shared -O3 -m64 -Xcompiler -DCUDA_NDARRAY_CUH=mc72d035fdf91890f3b36710688069b2e,-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION,-fPIC,-fvisibility=hidden -Xlinker -rpath,/home/adam/.theano/compiledir_Linux-4.13--generic-x86_64-with-debian-stretch-sid-x86_64-3.6.4-64/cuda_ndarray -I/home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda -I/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include -I/home/adam/anaconda3/include/python3.6m -I/home/adam/anaconda3/lib/python3.6/site-packages/theano/gof -L/home/adam/anaconda3/lib -o /home/adam/.theano/compiledir_Linux-4.13--generic-x86_64-with-debian-stretch-sid-x86_64-3.6.4-64/cuda_ndarray/cuda_ndarray.so mod.cu -lcublas -lpython3.6m -lcudart')\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "['nvcc', '-shared', '-O3', '-m64', '-Xcompiler', '-DCUDA_NDARRAY_CUH=mc72d035fdf91890f3b36710688069b2e,-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION,-fPIC,-fvisibility=hidden', '-Xlinker', '-rpath,/home/adam/.theano/compiledir_Linux-4.13--generic-x86_64-with-debian-stretch-sid-x86_64-3.6.4-64/cuda_ndarray', '-I/home/adam/anaconda3/lib/python3.6/site-packages/theano/sandbox/cuda', '-I/home/adam/anaconda3/lib/python3.6/site-packages/numpy/core/include', '-I/home/adam/anaconda3/include/python3.6m', '-I/home/adam/anaconda3/lib/python3.6/site-packages/theano/gof', '-L/home/adam/anaconda3/lib', '-o', '/home/adam/.theano/compiledir_Linux-4.13--generic-x86_64-with-debian-stretch-sid-x86_64-3.6.4-64/cuda_ndarray/cuda_ndarray.so', 'mod.cu', '-lcublas', '-lpython3.6m', '-lcudart']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os,sys\n",
     "import matplotlib.pyplot as plt\n",
@@ -5534,19 +41,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
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kMpUjk2m/W3FubUUmU5vkEwAA5SGbaB9bU+ew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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5558,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
@@ -5567,12 +74,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "We observed 637 heads out of 1000\n",
+      "We observed 625 heads out of 1000\n",
       "The expected distribution for a fair coin is mu=500.0, sd=15.8113883008\n",
       "Simulation p-value - 0.0\n",
-      "Binomial test - 3.70856293833e-18\n",
-      "Maximum likelihood 0.637\n",
-      "Bootstrap CI: (0.6070, 0.6670)\n"
+      "Binomial test - 2.47210119204e-15\n",
+      "Maximum likelihood 0.625\n",
+      "Bootstrap CI: (0.5950, 0.6550)\n"
      ]
     }
    ],
@@ -5618,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -5626,23 +133,23 @@
      "output_type": "stream",
      "text": [
       "n = 1000\n",
-      "h = 637\n"
+      "h = 625\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "logp = -41.258, ||grad|| = 137: 100%|██████████| 7/7 [00:00<00:00, 2522.78it/s]\n",
-      "100%|██████████| 1500/1500 [00:00<00:00, 9432.75it/s]\n"
+      "logp = -35.231, ||grad|| = 125: 100%|██████████| 7/7 [00:00<00:00, 335.06it/s]\n",
+      "100%|██████████| 1500/1500 [00:00<00:00, 3495.69it/s]\n"
      ]
     }
    ],
    "source": [
     "print(\"n = %s\"%n)\n",
     "print(\"h = %s\"%h)\n",
-    "alpha = 2\n",
-    "beta = 2\n",
+    "alpha = 1\n",
+    "beta = 1\n",
     "\n",
     "niter = 1000\n",
     "with pm.Model() as model: # context management\n",
@@ -5660,16 +167,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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f6ya9HGFaA/D93vt7AdwH4G3OuYcA/G8Afst7fzeAJQBn+jfmzqzPzSDd1ISTuvBRE07q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\nn16+TwDgVwH8JoCd/+6P7Jdeuvz3AH7Xe78EAN7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o4okufmjiiS5+opsUsTDVarXoEVBBE0908UMTT3TxE92kiIVpfn4+egRU0MQT\nXfzQxBNd/EQ3KWJhGhkZiR4BFTTxRBc/NPFEFz/RTYpYmKK3TrSjiSe6+KGJJ7r4iW5SxMK0trYW\nPQIqaOKJLn5o4okufqKbFLEwRZ/NgHY08UQXPzTxRBc/0U2KWJiiz2ZAO5p4oosfmniii5/oJkUs\nTD09PdEjoIImnujihyae6OInukkRC1NHR0f0CKigiSe6+KGJJ7r4iW5SxMK0sLAQPQIqaOKJLn5o\n4okufqKbFLEwjY6ORo+ACpp4oosfmniii5/oJkUsTLOzs9EjoIImnujihyae6OInukkRC1POOXoE\nVNDEE1380MQTXfxENyliYYp+mg7taOKJLn5o4okufqKbFLEwTU5ORo+ACpp4oosfmniii5/oJkUs\nTL29vdEjoIImnujihyae6OInukkRCxMAAMB+KmJhWlpaih4BFTTxRBc/NPFEFz/RTYpYmMbGxqJH\nQAVNPNHFD0080cVPdJMiFqapqanoEVBBE0908UMTT3TxE92kiIUppRQ9Aipo4okufmjiiS5+opsU\nsTANDQ1Fj4AKmniiix+aeKKLn+gmRSxM0U/ToR1NPNHFD0080cVPdJMiFqb+/v7oEVBBE0908UMT\nT3TxE92kiIWp1WpFj4AKmniiix+aeKKLn+gmRSxMy8vL0SOggiae6OKHJp7o4ie6SREL0/j4ePQI\nqKCJJ7r4oYknuviJblLEwtRoNKJHQAVNPNHFD0080cVPdJMiFqaurq7oEVBBE0908UMTT3TxE92k\niIVpYGAgegRU0MQTXfzQxBNd/EQ3KWJhmp6ejh4BFTTxRBc/NPFEFz/RTYpYmKK3TrSjiSe6+KGJ\nJ7r4iW5SxMLUbDajR0AFTTzRxQ9NPNHFT3STztB73yMrKyvRI6CCJp7o4qfa5DPPNdRcz5KkWiff\nADYK14qf6CZFLEzRZzOgHU080cVPtUlzPeuhu44GTYOXcK34iW5SxJfkos9mQDuaeKKLH5p4oouf\n6CbbLkwppU+mlM6llL532cd+K6X0Ykrp2xs/3rm/Y15drVaLvHtsgSae6OKHJp7o4ie6yU6eYfqU\npPu2+Pi/yjnfufHjyb0d69r09fVF3j22QBNPdPFDE0908RPdZNuFKef8TUmzBzDLdZuZmYkeARU0\n8UQXPzTxRBc/0U1286LvR1NKD0n6d5J+I+c8V/0N586d04kTJ9TZ2alWq6UHHnhAJ0+eVKPRUE9P\njzo6OrQNyky6AAAcT0lEQVSwsKDR0VHNzs4q56zR0VFNTk6qt7dXkrS0tKSxsTFNTU0ppaShoSFN\nTU2pv79frVZLy8vL6u3t1cTEhLq6ujQwMKDp6WkNDAyo2WxqZWVF4+PjajQaqtVq6uvr08zMjAYH\nB7WysqLV1dXN27u7u1Wv1zU3N6fh4WEtLi6q2Wxu3l6v11Wr1TQ/P6+RkRHNz89rbW1t8/a9fEwv\nfc4b9THV63VNTEwU9ZhK6HThwgWtrq4W9Zhu9E5ra2taXV3dfExHWsuamJjY1WMaXJvVhQtDdNrF\nY+rs7NTExERRj+lG79RqtTQxMbGvj+lqUs55280opfRqSX+ec/7HG78ekzQtKUv6bUlHc87vr/65\nU6dO5WPHjm37+XdrcnJSY2Nj+34/2DmaeKKLn2qTx0+f3fW75Pbic9zsuFb8HESTM2fOnD5+/Pib\nt7rtut4ll3OezDm3cs4XJf1rST+7mwF3a3V1NfLusQWaeKKLH5p4oouf6CbXtTCllC7/q8u7JH3v\nSr/3IESfzYB2NPFEFz808UQXP9FNdnKswJ9IOiXp9SmlF1JKJyT9Xkrpuyml70j6OUn/3T7PeVXR\nZzOgHU080cUPTTzRxU90k21f9J1z/uUtPvzYPsxy3bq7u6NHQAVNPNHFD0080cVPdJMiTvqu1+vR\nI6CCJp7o4ocmnujiJ7pJEQvT3FzbiQYIRhNPdPFDE0908RPdpIiFaXh4OHoEVNDEE1380MQTXfxE\nNyliYVpcXIweARU08UQXPzTxRBc/0U2KWJiazWb0CKigiSe6+KGJJ7r4iW5SxMIUfTYD2tHEE138\n0MQTXfxENyliYYo+mwHtaOKJLn5o4okufqKbFLEwRb/VEO1o4okufmjiiS5+opsUsTDVarXoEVBB\nE0908UMTT3TxE92kiIVpfn4+egRU0MQTXfzQxBNd/EQ3KWJhGhkZiR4BFTTxRBc/NPFEFz/RTYpY\nmKK3TrSjiSe6+KGJJ7r4iW5SxMK0trYWPQIqaOKJLn5o4okufqKbFLEwRZ/NgHY08UQXPzTxRBc/\n0U2KWJiiz2ZAO5p4oosfmniii5/oJkUsTD09PdEjoIImnujihyae6OInukkRC1NHR0f0CKigiSe6\n+KGJJ7r4iW5SxMK0sLAQPQIqaOKJLn5o4okufqKbFLEwjY6ORo+ACpp4oosfmniii5/oJkUsTLOz\ns9EjoIImnujihyae6OInukkRC1POOXoEVNDEE1380MQTXfxENyliYYp+mg7taOKJLn5o4okufqKb\nFLEwTU5ORo+ACpp4oosfmniii5/oJkUsTL29vdEjoIImnujihyae6OInukkRCxMAAMB+KmJhWlpa\nih4BFTTxRBc/NPFEFz/RTYpYmMbGxqJHQAVNPNHFD0080cVPdJMiFqapqanoEVBBE0908UMTT3Tx\nE92kiIUppRQ9Aipo4okufmjiiS5+opsUsTANDQ1Fj4AKmniiix+aeKKLn+gmRSxM0U/ToR1NPNHF\nD0080cVPdJMiFqb+/v7oEVBBE0908UMTT3TxE92kiIWp1WpFj4AKmniiix+aeKKLn+gmRSxMy8vL\n0SOggiae6OKHJp7o4ie6SREL0/j4ePQIqKCJJ7r4oYknuviJblLEwtRoNKJHQAVNPNHFD0080cVP\ndJMiFqaurq7oEVBBE0908UMTT3TxE92kiIVpYGAgegRU0MQTXfzQxBNd/EQ3KWJhmp6ejh4BFTTx\nRBc/NPFEFz/RTYpYmKK3TrSjiSe6+KGJJ7r4iW5SxMLUbDajR0AFTTzRxQ9NPNHFT3STIhamlZWV\n6BFQQRNPdPFDE0908RPdpIiFKfpsBrSjiSe6+KGJJ7r4iW5SxMIUfTYD2tHEE1380MQTXfxENyli\nYarVatEjoIImnujihyae6OInukkRC1NfX1/0CKigiSe6+KGJJ7r4iW5SxMI0MzMTPQIqaOKJLn5o\n4okufqKbFLEwDQ4ORo+ACpp4oosfmniii5/oJkUsTNFvNUQ7mniiix+aeKKLn+gmRSxMq6ur0SOg\ngiae6OKHJp7o4ie6SRELU/TZDGhHE0908UMTT3TxE92kiIUp+mwGtKOJJ7r4oYknuviJblLEwtTd\n3R09Aipo4okufmjiiS5+opsUsTDV6/XoEVBBE0908UMTT3TxE92kiIVpbm4uegRU0MQTXfzQxBNd\n/EQ3KWJhGh4ejh4BFTTxRBc/NPFEFz/RTYpYmBYXF6NHQAVNPNHFD0080cVPdJMiFqZmsxk9Aipo\n4okufmjiiS5+opsUsTBFn82AdjTxRBc/NPFEFz/RTbZdmFJKn0wpnUspfe+yjw2llL6aUvrhxj9D\nv8FL9NkMaEcTT3TxQxNPdPET3WQnzzB9StJ9lY99UNLXc863S/r6xq/DRL/VEO1o4okufmjiiS5+\noptsuzDlnL8pabby4fslfXrj55+W9M/3eK5rUqvVIu8eW6CJJ7r4oYknuviJbtJ5nX9uLOd8duPn\nDUljW/2mc+fO6cSJE+rs7FSr1dIDDzygkydPqtFoqKenRx0dHVpYWNDo6KhmZ2eVc9bo6KgmJyfV\n29srSVpaWtLY2JimpqaUUtLQ0JCmpqbU39+vVqul5eVltVotzc/Pq6urSwMDA5qentbAwICazaZW\nVlY0Pj6uRqOhWq2mvr4+zczMaHBwUCsrK1pdXd28vbu7W/V6XXNzcxoeHtbi4qKazebm7fV6XbVa\nTfPz8xoZGdH8/LzW1tY2b9/Lx/TS57xRH9Pq6qrm5+eLekwldHrxxRfV3d1d1GO60TvNzc2pu7t7\n8zEdaS1rYmJiV49pcG1WFy4M0WkXj2l6elrz8/NFPaYbvdMLL7yg+fn5fX1MV5NyzttuRymlV0v6\n85zzP9749U9zzq+47Pa5nHPb65hOnTqVjx07tu3n363l5WX19PTs+/1g52jiiS5+qk0eP31WD911\ndFefcy8+x82Oa8XPQTQ5c+bM6ePHj795q9uu911ykymlo5K08c9z1zvcXpifn4+8e2yBJp7o4ocm\nnujiJ7rJ9S5MX5T0vo2fv0/SF/ZmnOuztrYWeffYAk080cUPTTzRxU90k50cK/Ankk5Jen1K6YWU\n0glJH5X09pTSDyX9/Mavw0SfzYB2NPFEFz808UQXP9FNdvIuuV/OOR/NOXflnP9RzvmxnPNMzvl4\nzvn2nPPP55yr76I7UNFnM6AdTTzRxQ9NPNHFT3STIk765oV5fmjiiS5+aOKJLn6imxSxMHV0dESP\ngAqaeKKLH5p4oouf6CZFLEwLCwvRI6CCJp7o4ocmnujiJ7pJEQvT6Oho9AiooIknuvihiSe6+Ilu\nUsTCNDsb+ppzbIEmnujihyae6OInukkRC9NOTivHwaKJJ7r4oYknuviJblLEwhT9NB3a0cQTXfzQ\nxBNd/EQ3KWJhmpycjB4BFTTxRBc/NPFEFz/RTYpYmF76jsTwQRNPdPFDE0908RPdpIiFCQAAYD8V\nsTAtLS1Fj4AKmniiix+aeKKLn+gmRSxMY2Nj0SOggiae6OKHJp7o4ie6SREL09TUVPQIqKCJJ7r4\noYknuviJblLEwpRSih4BFTT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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f16887ca630>"
+       "<matplotlib.figure.Figure at 0x10d4bb3c8>"
       ]
      },
      "metadata": {},
@@ -5699,17 +206,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
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kk06qc1Ex/e8+++yT1QWHBx54oF6LiemFkxde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\nABTqy9czpEkuUl5ykWzJRcpv9uzZcdhhh8Xq1asjovPN54MHD47bbrv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rIpIkSZIkTVWui7guIkmSJEmSJGlqswBdknRJt956a7++7u5u6urqhjVea2srr732Wu8Hd5fa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ZURi8VCTSNJkqThnEeeOv39/Tz22GOsXLmS1tbWjEduBf4B+Hfge6RXob8euNBn51jw+JeD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\nLgzr5dDb28vnP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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5721,14 +228,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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G926De7fBvS9POX+Z2d3b4N5tiKP3FRvpIlILXAL8s6p+pPwhLcXzpNsRx5yi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\nZa1DqErcuw3u3YbR0VHrEKoWr/M2uHc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+5UIcc4qWA+7dBvdug3u3w93b4N5tiKP3qGPS/0NEnlvSSJahurraYrcO0NDQ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\niIiImvkLCNvMA+N+eksAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd70f945710>"
+       "<matplotlib.figure.Figure at 0x10d6e8dd8>"
       ]
      },
      "metadata": {},
@@ -5794,14 +301,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 15000/15000 [00:04<00:00, 3385.44it/s]\n"
+      "100%|██████████| 15000/15000 [00:13<00:00, 1104.52it/s]\n"
      ]
     }
    ],
@@ -5834,14 +341,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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pb+SV+/1YB9OED7dyzzwNyjyeMo+nzOMp83jKPBuiGnnr4znv9bgSmAHMAw4B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\nciFvZmZmZtZA/wU0fNS6W2j2VwAAAABJRU5ErkJggg==\n",
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V1NfXs3XrViZNmkRXVxfd3d17l48fP56amhpWr17Nsccey86dO8nlcnuX19TU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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd70c0d9198>"
+       "<matplotlib.figure.Figure at 0x10e9f1c88>"
       ]
      },
      "metadata": {},
@@ -5903,14 +410,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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991zS09N57LHHuPrqqykuLqa6upqLLrqIvn378uCDDzJ58mRuvfVW0tLSePTR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\nGufr7gVpzL3ZQfprgV8H7gf2A18Cfg94fZvq2jYwoBOAoix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LwYMH07t3b/r27dtsR7/97W8ZM2YM69ev55RTTuHAAw8EqOONUArX33Q7PfZo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\nTBH5IxSvPnqLiJzpWVVl1td5YouVhNQ/NzE1DnO3wdxtLCw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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd71161f160>"
+       "<matplotlib.figure.Figure at 0x10f2d6128>"
       ]
      },
      "metadata": {},
@@ -5954,7 +461,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
@@ -5975,7 +484,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
diff --git a/slides/probabilistic-programming-intro.pdf b/slides/probabilistic-programming-intro.pdf
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diff --git a/slides/probabilistic-programming-intro.tex b/slides/probabilistic-programming-intro.tex
index e45f969..a6459d8 100644
--- a/slides/probabilistic-programming-intro.tex
+++ b/slides/probabilistic-programming-intro.tex
@@ -368,7 +368,7 @@ \subsection{}
 \begin{code}
 import pymc3 as pm
 
-n,h,alpha,beta,niter = 100,61,2,2,1000
+n,h,alpha,beta,niter = 100,61,1,1,1000
 
 # context management
 with pm.Model() as model: 
@@ -663,4 +663,4 @@ \subsection{}
 \end{tiny}
 }
 
-\end{document}
\ No newline at end of file
+\end{document}