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提交 a0427809 authored 作者: Frederic Bastien's avatar Frederic Bastien
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if the kernel shape is gived, unroll the GpuConv by the kernel wid. Otherwise don't unroll.

上级 500edda8
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正在显示 包含 23 行增加 和 212 行删除
theano/sandbox/cuda/__init__.py
浏览文件 @ a0427809
...@@ -77,8 +77,7 @@ if compile_cuda_ndarray: ...@@ -77,8 +77,7 @@ if compile_cuda_ndarray:
os.makedirs(cuda_ndarray_loc) os.makedirs(cuda_ndarray_loc)
nvcc_compiler.nvcc_module_compile_str('cuda_ndarray', code, location = cuda_ndarray_loc, nvcc_compiler.nvcc_module_compile_str('cuda_ndarray', code, location = cuda_ndarray_loc,
include_dirs=[cuda_path], libs=['cublas'], include_dirs=[cuda_path], libs=['cublas'])
preargs=['-DDONT_UNROLL', '-O3'])
from cuda_ndarray.cuda_ndarray import * from cuda_ndarray.cuda_ndarray import *
......
theano/sandbox/cuda/blas.py
浏览文件 @ a0427809
...@@ -132,7 +132,8 @@ class GpuConv(Op): ...@@ -132,7 +132,8 @@ class GpuConv(Op):
logical_kern_hw=None, logical_kern_hw=None,
logical_kern_align_top=True, logical_kern_align_top=True,
version=-1, version=-1,
verbose=0): verbose=0,
kshp=None):
self.border_mode = border_mode self.border_mode = border_mode
self.subsample = subsample self.subsample = subsample
if logical_img_hw is not None: if logical_img_hw is not None:
...@@ -152,6 +153,7 @@ class GpuConv(Op): ...@@ -152,6 +153,7 @@ class GpuConv(Op):
self.logical_kern_align_top = logical_kern_align_top self.logical_kern_align_top = logical_kern_align_top
self.version=version self.version=version
self.verbose=verbose self.verbose=verbose
self.kshp = kshp
def __eq__(self, other): def __eq__(self, other):
return type(self) == type(other) \ return type(self) == type(other) \
...@@ -187,13 +189,16 @@ class GpuConv(Op): ...@@ -187,13 +189,16 @@ class GpuConv(Op):
return Apply(self, [img, kern], [CudaNdarrayType(broadcastable)()]) return Apply(self, [img, kern], [CudaNdarrayType(broadcastable)()])
def c_compile_args(self): def c_compile_args(self):
return ['-DDONT_UNROLL'] nb = 0
if self.kshp is not None:
nb = self.kshp[1]
return ['-DTHEANO_KERN_WID='+str(nb)]
def c_headers(self): def c_headers(self):
return ['cuda_ndarray.cuh','<stdio.h>'] return ['cuda_ndarray.cuh','<stdio.h>']
def c_code_cache_version(self): def c_code_cache_version(self):
return (0,1) return (0,2)
def c_support_code_apply(self, node, nodename): def c_support_code_apply(self, node, nodename):
return open(os.path.join(os.path.split(__file__)[0],'conv_kernel.cu')).read()+\ return open(os.path.join(os.path.split(__file__)[0],'conv_kernel.cu')).read()+\
......
theano/sandbox/cuda/conv.cu
浏览文件 @ a0427809
enum { ConvMode_FULL, ConvMode_VALID }; enum { ConvMode_FULL, ConvMode_VALID };
PyObject * CudaNdarray_Conv(CudaNdarray *img, CudaNdarray * kern, CudaNdarray * out, const int mode, const int subsample_rows, const int subsample_cols, const int version, const int verbose); PyObject * CudaNdarray_Conv(CudaNdarray *img, CudaNdarray * kern, CudaNdarray * out, const int mode, const int subsample_rows, const int subsample_cols, const int version, const int verbose);
bool msgdisplayed_conv_patch__kern_width = false;
bool msgdisplayed_conv_patch_stack__kern_width = false;
bool msgdisplayed_conv_rows__kern_width = false;
bool msgdisplayed_conv_rows_stack__kern_width = false;
bool msgdisplayed_conv_rows_stack2__kern_width = false;
bool msgdisplayed_conv_patch_stack_reduce__kern_width = false;
bool msgdisplayed_conv_full_patch_stack__kern_width = false;
/* /*
* version: -1, autodetect, >=0 a specific version to use. * version: -1, autodetect, >=0 a specific version to use.
* If it can't be executed, we revert to the reference implementation * If it can't be executed, we revert to the reference implementation
...@@ -78,6 +69,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern, ...@@ -78,6 +69,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern,
const int img_size_byte = img_size*sizeof(float); const int img_size_byte = img_size*sizeof(float);
const int kern_size_byte = kern_size*sizeof(float); const int kern_size_byte = kern_size*sizeof(float);
const int out_size_byte = out_size*sizeof(float); const int out_size_byte = out_size*sizeof(float);
assert((THEANO_KERN_WID == CudaNdarray_HOST_DIMS(kern)[3]) || (THEANO_KERN_WID==0));
bool subsample = subsample_rows!=1 || subsample_cols!=1; bool subsample = subsample_rows!=1 || subsample_cols!=1;
bool img_contiguous = CudaNdarray_is_c_contiguous(img); bool img_contiguous = CudaNdarray_is_c_contiguous(img);
...@@ -150,24 +142,8 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern, ...@@ -150,24 +142,8 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern,
if(threads.y==out_len) f=conv_patch<true,kern_wid,false>;\ if(threads.y==out_len) f=conv_patch<true,kern_wid,false>;\
else f=conv_patch<true,kern_wid,true>; else f=conv_patch<true,kern_wid,true>;
switch(kern_wid){ CONV_PATCH_SPECIAL(THEANO_KERN_WID);
#ifdef UNROLL_LOOP
case 1: CONV_PATCH_SPECIAL(1); break;//test_conv.py:test_valid
case 2: CONV_PATCH_SPECIAL(2); break;//test_conv.py:test_valid
case 3: CONV_PATCH_SPECIAL(3); break;//test_conv.py:test_valid
case 4: CONV_PATCH_SPECIAL(4); break;
case 5: CONV_PATCH_SPECIAL(5); break;
case 6: CONV_PATCH_SPECIAL(6); break;
case 7: CONV_PATCH_SPECIAL(7); break;
case 10: CONV_PATCH_SPECIAL(10); break;
#endif
default:
if(!msgdisplayed_conv_patch__kern_width) {
printf("OPTIMISATION WARNING: conv_patch template default add kern_wid=%d in %s at line %i to have an optimized version for your kern_wid\n", kern_wid, __FILE__, __LINE__);
msgdisplayed_conv_patch__kern_width=true;
}
CONV_PATCH_SPECIAL(0);
}
f<<< grid, threads, shared_size>>> f<<< grid, threads, shared_size>>>
(img->devdata, kern->devdata, out->devdata, (img->devdata, kern->devdata, out->devdata,
img_len, img_wid, kern_len, kern_wid, nkern, nstack); img_len, img_wid, kern_len, kern_wid, nkern, nstack);
...@@ -231,36 +207,8 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern, ...@@ -231,36 +207,8 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern,
if(img_contiguous_2d && !kern_contiguous_2d) f=conv_patch_stack<true,false,kern_wid,true,false,true,false>;\ if(img_contiguous_2d && !kern_contiguous_2d) f=conv_patch_stack<true,false,kern_wid,true,false,true,false>;\
if(!img_contiguous_2d && kern_contiguous_2d) f=conv_patch_stack<true,false,kern_wid,false,true,true,false>;\ if(!img_contiguous_2d && kern_contiguous_2d) f=conv_patch_stack<true,false,kern_wid,false,true,true,false>;\
if(!img_contiguous_2d && !kern_contiguous_2d) f=conv_patch_stack<true,false,kern_wid,false,false,true,false>; if(!img_contiguous_2d && !kern_contiguous_2d) f=conv_patch_stack<true,false,kern_wid,false,false,true,false>;
switch(kern_wid){
#ifdef UNROLL_LOOP CONV_PATCH_STACK_SPECIAL(THEANO_KERN_WID);
case 1: CONV_PATCH_STACK_SPECIAL(1); break;
case 2: CONV_PATCH_STACK_SPECIAL(2); break;
case 3: CONV_PATCH_STACK_SPECIAL(3); break;
case 4: CONV_PATCH_STACK_SPECIAL(4); break;
case 5: CONV_PATCH_STACK_SPECIAL(5); break;
case 6: CONV_PATCH_STACK_SPECIAL(6); break;
case 7: CONV_PATCH_STACK_SPECIAL(7); break;
case 8: CONV_PATCH_STACK_SPECIAL(8); break;
case 9: CONV_PATCH_STACK_SPECIAL(9); break;
case 10: CONV_PATCH_STACK_SPECIAL(10); break;
//////// Special cases
case 12: CONV_PATCH_STACK_SPECIAL(12); break;//on cifar10
case 21: CONV_PATCH_STACK_SPECIAL(21); break;//on cifar10
case 23: CONV_PATCH_STACK_SPECIAL(23); break;//test_nnet.py:test_lenet_64
case 24: CONV_PATCH_STACK_SPECIAL(24); break;//on cifar10
case 25: CONV_PATCH_STACK_SPECIAL(25); break;//on cifar10
case 28: CONV_PATCH_STACK_SPECIAL(28); break;
case 32: CONV_PATCH_STACK_SPECIAL(32); break;// Alex speed example
case 45: CONV_PATCH_STACK_SPECIAL(45); break;//used by test_nnet.py:test_lenet_108
#endif
//////// default case
default:
if(!msgdisplayed_conv_patch_stack__kern_width) {
printf("OPTIMISATION HINT: conv_patch_stack template default add kern_wid=%d in %s at line %i to have an optimized version for your kern_wid\n", kern_wid, __FILE__, __LINE__);
msgdisplayed_conv_patch_stack__kern_width = true;
}
CONV_PATCH_STACK_SPECIAL(0);
}
f<<< grid, threads, shared_size>>> f<<< grid, threads, shared_size>>>
(img->devdata, kern->devdata, out->devdata, (img->devdata, kern->devdata, out->devdata,
img_len, img_wid, kern_len, kern_wid, nkern, nstack, img_len, img_wid, kern_len, kern_wid, nkern, nstack,
...@@ -308,30 +256,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern, ...@@ -308,30 +256,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern,
if(!img_contiguous_2d || !kern_contiguous_2d) f = conv_rows<kern_wid, false>;\ if(!img_contiguous_2d || !kern_contiguous_2d) f = conv_rows<kern_wid, false>;\
else f = conv_rows<kern_wid, true>;\ else f = conv_rows<kern_wid, true>;\
switch(kern_wid){ CONV_ROWS_SPECIAL(THEANO_KERN_WID);
#ifdef UNROLL_LOOP
case 1: CONV_ROWS_SPECIAL(1); break;//test_conv.py:test_valid
case 2: CONV_ROWS_SPECIAL(2); break;//test_conv.py:test_valid
case 3: CONV_ROWS_SPECIAL(3); break;//test_conv.py:test_valid
case 4: CONV_ROWS_SPECIAL(4); break;//test_conv.py:test_valid
case 5: CONV_ROWS_SPECIAL(5); break;//test_conv.py:test_valid
// case 6: CONV_ROWS_SPECIAL(6); break;
case 7: CONV_ROWS_SPECIAL(7); break;//used by test_nnet.py:test_lenet_108
// case 8: CONV_ROWS_SPECIAL(8); break;
case 9: CONV_ROWS_SPECIAL(9); break;//used by test_nnet.py:test_lenet_256
case 10: CONV_ROWS_SPECIAL(10); break;//test_conv.py:test_valid
//////// Special cases
case 28: CONV_ROWS_SPECIAL(28); break;
#endif
//////// default case
default:
if(!msgdisplayed_conv_rows__kern_width){
printf("OPTIMISATION HINT: conv_rows template default add kern_wid=%d in %s at line %i to have an optimized version for your kern_wid\n", kern_wid, __FILE__, __LINE__);
msgdisplayed_conv_rows__kern_width = true;
}
CONV_ROWS_SPECIAL(0);
}
f<<< grid, threads, shared_size >>> f<<< grid, threads, shared_size >>>
(img->devdata, kern->devdata, out->devdata, (img->devdata, kern->devdata, out->devdata,
img_len, img_wid, kern_len, kern_wid, nkern, nstack, img_len, img_wid, kern_len, kern_wid, nkern, nstack,
...@@ -383,34 +308,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern, ...@@ -383,34 +308,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern,
#define CONV_ROWS_STACK_SPECIAL(kern_wid) \ #define CONV_ROWS_STACK_SPECIAL(kern_wid) \
if(!img_contiguous_2d || !kern_contiguous_2d) f = conv_rows_stack<kern_wid, false>;\ if(!img_contiguous_2d || !kern_contiguous_2d) f = conv_rows_stack<kern_wid, false>;\
else f = conv_rows_stack<kern_wid, true>;\ else f = conv_rows_stack<kern_wid, true>;\
CONV_ROWS_STACK_SPECIAL(THEANO_KERN_WID);
switch(kern_wid){
#ifdef UNROLL_LOOP
case 1: CONV_ROWS_STACK_SPECIAL(1); break;//test_conv.py:test_valid
case 2: CONV_ROWS_STACK_SPECIAL(2); break;
case 3: CONV_ROWS_STACK_SPECIAL(3); break;//test_conv.py:test_valid
case 4: CONV_ROWS_STACK_SPECIAL(4); break;//test_conv.py:test_valid
case 5: CONV_ROWS_STACK_SPECIAL(5); break;//test_conv.py:test_valid
case 6: CONV_ROWS_STACK_SPECIAL(6); break;//test_conv.py:test_valid
case 7: CONV_ROWS_STACK_SPECIAL(7); break;//test_nnet.py:test_lenet_108
case 8: CONV_ROWS_STACK_SPECIAL(8); break;//test_conv.py:test_valid
case 9: CONV_ROWS_STACK_SPECIAL(9); break;//test_nnet.py:test_lenet_256
case 10: CONV_ROWS_STACK_SPECIAL(10); break;//test_conv.py:test_valid
//////// Special cases
case 23: CONV_ROWS_STACK_SPECIAL(23); break;//test_conv.py:test_valid
case 24: CONV_ROWS_STACK_SPECIAL(24); break;//test_conv.py:test_valid
case 28: CONV_ROWS_STACK_SPECIAL(28); break;//test_conv.py:test_valid
case 45: CONV_ROWS_STACK_SPECIAL(45); break;//test_nnet.py:test_lenet_64
case 102: CONV_ROWS_STACK_SPECIAL(102); break;//test_nnet.py:test_lenet_108
#endif
//////// default case
default:
if(!msgdisplayed_conv_rows_stack__kern_width){
printf("OPTIMISATION HINT: conv_rows_stack template default add kern_wid=%d in %s at line %i to have an optimized version for your kern_wid\n", kern_wid, __FILE__, __LINE__);
msgdisplayed_conv_rows_stack__kern_width = true;
}
CONV_ROWS_STACK_SPECIAL(0);
}
f<<< grid, threads, shared_size >>> f<<< grid, threads, shared_size >>>
(img->devdata, (img->devdata,
...@@ -482,40 +380,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern, ...@@ -482,40 +380,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern,
else if(version==9) f = conv_rows_stack2<kern_wid, true,true>;\ else if(version==9) f = conv_rows_stack2<kern_wid, true,true>;\
else if(!img_contiguous_2d || !kern_contiguous_2d) f = conv_rows_stack2<kern_wid, false, false>;\ else if(!img_contiguous_2d || !kern_contiguous_2d) f = conv_rows_stack2<kern_wid, false, false>;\
else f = conv_rows_stack2<kern_wid, true, false>;\ else f = conv_rows_stack2<kern_wid, true, false>;\
CONV_ROWS_STACK2_SPECIAL(THEANO_KERN_WID);
switch(kern_wid){
#ifdef UNROLL_LOOP
case 1: CONV_ROWS_STACK2_SPECIAL(1); break;//test_conv.py:test_valid
case 2: CONV_ROWS_STACK2_SPECIAL(2); break;
case 3: CONV_ROWS_STACK2_SPECIAL(3); break;//test_conv.py:test_valid
case 4: CONV_ROWS_STACK2_SPECIAL(4); break;//test_conv.py:test_valid
case 5: CONV_ROWS_STACK2_SPECIAL(5); break;//test_conv.py:test_valid
case 6: CONV_ROWS_STACK2_SPECIAL(6); break;//test_conv.py:test_valid
case 7: CONV_ROWS_STACK2_SPECIAL(7); break;//test_nnet.py:test_lenet_108
case 8: CONV_ROWS_STACK2_SPECIAL(8); break;//test_conv.py:test_valid
// case 9: CONV_ROWS_STACK2_SPECIAL(9); break;
case 10: CONV_ROWS_STACK2_SPECIAL(10); break;//test_conv.py:test_valid
//////// Special cases
case 23: CONV_ROWS_STACK2_SPECIAL(23); break;//test_conv.py:test_valid
case 24: CONV_ROWS_STACK2_SPECIAL(24); break;//test_conv.py:test_valid
case 28: CONV_ROWS_STACK2_SPECIAL(28); break;//test_conv.py:test_valid
case 45: CONV_ROWS_STACK2_SPECIAL(45); break;//test_nnet.py:test_lenet_108
case 58: CONV_ROWS_STACK2_SPECIAL(58); break;//test_nnet.py:test_lenet_108
case 70: CONV_ROWS_STACK2_SPECIAL(70); break;//mobahi_2009.py
case 102: CONV_ROWS_STACK2_SPECIAL(102); break;//test_nnet.py:test_lenet_108
case 116: CONV_ROWS_STACK2_SPECIAL(116); break;//test_nnet.py:test_lenet_256
case 248: CONV_ROWS_STACK2_SPECIAL(248); break;//test_nnet.py:test_lenet_256
#endif
//////// default case
default:
if(!msgdisplayed_conv_rows_stack2__kern_width){
printf("OPTIMISATION HINT: conv_rows_stack{2,3} template default add"
" kern_wid=%d in %s at line %i to have an optimized version"
" for your kern_wid\n", kern_wid, __FILE__, __LINE__);
msgdisplayed_conv_rows_stack2__kern_width = true;
}
CONV_ROWS_STACK2_SPECIAL(0);
}
f<<< grid, threads, shared_size >>> f<<< grid, threads, shared_size >>>
(img->devdata, (img->devdata,
...@@ -615,33 +480,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern, ...@@ -615,33 +480,7 @@ CudaNdarray_conv_valid(const CudaNdarray *img, const CudaNdarray * kern,
/*else if(!kern_flipped && !ccontig && !split && !full_kern) f=conv_patch_stack_reduce<false,kern_wid,false, false, false>;*/\ /*else if(!kern_flipped && !ccontig && !split && !full_kern) f=conv_patch_stack_reduce<false,kern_wid,false, false, false>;*/\
else if(!kern_flipped && ccontig && split && !full_kern) f=conv_patch_stack_reduce<false,kern_wid,true, true, false>;\ else if(!kern_flipped && ccontig && split && !full_kern) f=conv_patch_stack_reduce<false,kern_wid,true, true, false>;\
else if(!kern_flipped && !ccontig && split && !full_kern) f=conv_patch_stack_reduce<false,kern_wid,false, true, false>; else if(!kern_flipped && !ccontig && split && !full_kern) f=conv_patch_stack_reduce<false,kern_wid,false, true, false>;
switch(kern_wid){ CONV_PATCH_STACK_REDUCE_SPECIAL(THEANO_KERN_WID);
#ifdef UNROLL_LOOP
case 1: CONV_PATCH_STACK_REDUCE_SPECIAL(1); break;
case 2: CONV_PATCH_STACK_REDUCE_SPECIAL(2); break;
case 3: CONV_PATCH_STACK_REDUCE_SPECIAL(3); break;
case 4: CONV_PATCH_STACK_REDUCE_SPECIAL(4); break;
case 5: CONV_PATCH_STACK_REDUCE_SPECIAL(5); break;
case 6: CONV_PATCH_STACK_REDUCE_SPECIAL(6); break;
case 7: CONV_PATCH_STACK_REDUCE_SPECIAL(7); break;
case 8: CONV_PATCH_STACK_REDUCE_SPECIAL(8); break;
case 9: CONV_PATCH_STACK_REDUCE_SPECIAL(9); break;
case 10: CONV_PATCH_STACK_REDUCE_SPECIAL(10); break;
//////// Special cases
case 20: CONV_PATCH_STACK_REDUCE_SPECIAL(20); break;
case 23: CONV_PATCH_STACK_REDUCE_SPECIAL(23); break;//test_nnet.py:test_lenet64
case 24: CONV_PATCH_STACK_REDUCE_SPECIAL(24); break;
case 28: CONV_PATCH_STACK_REDUCE_SPECIAL(28); break;
case 32: CONV_PATCH_STACK_REDUCE_SPECIAL(32); break;//Alex speed demonstration
#endif
//////// default case
default:
if(!msgdisplayed_conv_patch_stack_reduce__kern_width) {
printf("OPTIMISATION HINT: conv_patch_stack_reduce template default add kern_wid=%d in %s at line %i to have an optimized version for your kern_wid\n", kern_wid, __FILE__, __LINE__);
msgdisplayed_conv_patch_stack_reduce__kern_width = true;
}
CONV_PATCH_STACK_REDUCE_SPECIAL(0);
}
if (verbose) printf("INFO: using 'conv_patch_stack_reduce' version nb_split=%d, preload_full_kern=%d\n", if (verbose) printf("INFO: using 'conv_patch_stack_reduce' version nb_split=%d, preload_full_kern=%d\n",
nb_split,full_kern); nb_split,full_kern);
...@@ -834,7 +673,7 @@ CudaNdarray_conv_full(const CudaNdarray *img, const CudaNdarray * kern, CudaNdar ...@@ -834,7 +673,7 @@ CudaNdarray_conv_full(const CudaNdarray *img, const CudaNdarray * kern, CudaNdar
const int nkern=CudaNdarray_HOST_DIMS(kern)[0]; const int nkern=CudaNdarray_HOST_DIMS(kern)[0];
const int img_wid=CudaNdarray_HOST_DIMS(img)[3]; const int img_wid=CudaNdarray_HOST_DIMS(img)[3];
const int img_len=CudaNdarray_HOST_DIMS(img)[2]; const int img_len=CudaNdarray_HOST_DIMS(img)[2];
const int kern_wid=CudaNdarray_HOST_DIMS(kern)[3]; const int kern_wid=CudaNdarray_HOST_DIMS(img)[3];
const int kern_len=CudaNdarray_HOST_DIMS(kern)[2]; const int kern_len=CudaNdarray_HOST_DIMS(kern)[2];
const int out_wid=CudaNdarray_HOST_DIMS(out)[3]; const int out_wid=CudaNdarray_HOST_DIMS(out)[3];
const int out_len=CudaNdarray_HOST_DIMS(out)[2]; const int out_len=CudaNdarray_HOST_DIMS(out)[2];
...@@ -861,6 +700,7 @@ CudaNdarray_conv_full(const CudaNdarray *img, const CudaNdarray * kern, CudaNdar ...@@ -861,6 +700,7 @@ CudaNdarray_conv_full(const CudaNdarray *img, const CudaNdarray * kern, CudaNdar
//const int out_size_byte = out_size*sizeof(float); // unused //const int out_size_byte = out_size*sizeof(float); // unused
assert((THEANO_KERN_WID == CudaNdarray_HOST_DIMS(kern)[3]) || (THEANO_KERN_WID==0));
bool subsample = subsample_rows!=1 || subsample_cols!=1; bool subsample = subsample_rows!=1 || subsample_cols!=1;
bool img_contiguous = CudaNdarray_is_c_contiguous(img); bool img_contiguous = CudaNdarray_is_c_contiguous(img);
...@@ -968,31 +808,7 @@ CudaNdarray_conv_full(const CudaNdarray *img, const CudaNdarray * kern, CudaNdar ...@@ -968,31 +808,7 @@ CudaNdarray_conv_full(const CudaNdarray *img, const CudaNdarray * kern, CudaNdar
else if(version==5) f=conv_full_patch_stack_padded<false,kern_wid,false,false,true>;\ else if(version==5) f=conv_full_patch_stack_padded<false,kern_wid,false,false,true>;\
else assert(false); else assert(false);
switch(kern_wid){ CONV_FULL_PATCH_STACK_PADDED_SPECIAL(THEANO_KERN_WID);
#ifdef UNROLL_LOOP
case 1: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(1); break;
case 2: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(2); break;
case 3: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(3); break;
case 4: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(4); break;
case 5: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(5); break;//test_conv.py:test_full
case 6: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(6); break;//test_conv.py:test_full
case 7: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(7); break;//test_nnet.py:test_lenet_64
case 8: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(8); break;//test_conv.py:test_full
case 9: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(9); break;//test_nnet.py:test_lenet_256
case 10: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(10); break;//test_conv.py:test_full
case 12: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(12); break;//test_conv.py:test_full
//////// Special cases
case 28: CONV_FULL_PATCH_STACK_PADDED_SPECIAL(28); break;
#endif
//////// default case
default:
if(!msgdisplayed_conv_full_patch_stack__kern_width){
printf("OPTIMISATION HINT: conv_full_patch_stack_padded template default add kern_wid=%d in %s at line %i to have an optimized version for your kern_wid\n", kern_wid, __FILE__, __LINE__);
msgdisplayed_conv_full_patch_stack__kern_width = true;
}
CONV_FULL_PATCH_STACK_PADDED_SPECIAL(0);
}
f<<< grid, threads, shared_size>>> f<<< grid, threads, shared_size>>>
(img->devdata, kern_data_unflipped, out->devdata, (img->devdata, kern_data_unflipped, out->devdata,
...@@ -1069,17 +885,7 @@ CudaNdarray_conv_full(const CudaNdarray *img, const CudaNdarray * kern, CudaNdar ...@@ -1069,17 +885,7 @@ CudaNdarray_conv_full(const CudaNdarray *img, const CudaNdarray * kern, CudaNdar
int, int, int, int, int, int, int, int, int, int, int, int,
int, int, int, int, int, int, int, int) = conv_full_load_everything<0>; int, int, int, int, int, int, int, int) = conv_full_load_everything<0>;
switch(nstack) f = conv_full_load_everything<THEANO_KERN_WID>;
{
#ifdef UNROLL_LOOP
case 1: f = conv_full_load_everything<1>; break;
//case 10: f = conv_full_load_everything<10>; break;
//case 30: f = conv_full_load_everything<30>; break; //This is actually slower than the general version??
#endif
default:
printf("OPTIMISATION HINT: conv_full_load_everything template default add kern_wid=%d in %s at line %i to have an optimized version for your kern_wid\n", kern_wid, __FILE__, __LINE__);
f = conv_full_load_everything<0>;
};
f<<< grid, threads, shared_size>>> f<<< grid, threads, shared_size>>>
(img->devdata, (img->devdata,
......
theano/sandbox/cuda/opt.py
浏览文件 @ a0427809
...@@ -347,6 +347,7 @@ def local_gpu_conv(node): ...@@ -347,6 +347,7 @@ def local_gpu_conv(node):
logical_img_hw=logical_img_hw, logical_img_hw=logical_img_hw,
logical_kern_hw=op.kshp_logical, logical_kern_hw=op.kshp_logical,
logical_kern_align_top=op.kshp_logical_top_aligned, logical_kern_align_top=op.kshp_logical_top_aligned,
kshp=op.kshp,
version=op.version, version=op.version,
verbose=op.verbose verbose=op.verbose
) )
......
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