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提交 5fc89c03 authored 作者: Frederic's avatar Frederic
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new GpuConv compile, but give wrong version in some cases!

上级 baf12f54
全部展开 隐藏空白字符变更
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正在显示 包含 59 行增加 和 35 行删除
theano/sandbox/gpuarray/conv.py
浏览文件 @ 5fc89c03
import copy
import os
import theano
from theano import gof
from theano import config, gof
from theano.sandbox.cuda.nvcc_compiler import NVCC_compiler
from theano.sandbox.gpuarray.type import GpuArrayType
class GpuConv(gof.Op):
......@@ -114,6 +119,9 @@ class GpuConv(gof.Op):
str(self.kshp))
def make_node(self, img, kern):
if img.dtype != "float32" or kern.dtype != "float32":
raise NotImplementedError("GpuConv currently only work"
" with float32 dtype")
if img.type.ndim != 4:
raise TypeError('img must be 4D tensor')
if kern.type.ndim != 4:
......@@ -121,7 +129,8 @@ class GpuConv(gof.Op):
broadcastable = [img.type.broadcastable[0], kern.type.broadcastable[0],
False, False]
return Apply(self, [img, kern], [CudaNdarrayType(broadcastable)()])
out = GpuArrayType(img.dtype, broadcastable)()
return gof.Apply(self, [img, kern], [out])
def flops(self, inputs, outputs):
""" Useful with the hack in profilemode to print the MFlops"""
......@@ -145,6 +154,8 @@ class GpuConv(gof.Op):
def make_thunk(self, node, storage_map, compute_map, no_recycling):
node_ = copy.copy(node)
assert node.op is node_.op
if config.gpuarray.sync:
raise NotImplementedError("GpuConv do not implement gpuarray.sync Theano flag")
if node_.op.max_threads_dim0 is None:
cuda = theano.sandbox.cuda
device_id = cuda.use.device_number
......@@ -169,20 +180,30 @@ class GpuConv(gof.Op):
return ['-DTHEANO_KERN_WID=' + str(nb)] # ,'-g','-G']
def c_headers(self):
return ['cuda_ndarray.cuh', '<stdio.h>']
return ['<stdio.h>', 'cuda.h',
'<compyte/extension.h>', '<compyte/numpy_compat.h>']
def c_code_cache_version(self):
# raise this whenever modifying any of the support_code_files
return (0, 20)
def c_init_code(self):
return ['cuda_get_ptr_raw = (CUdeviceptr (*)(gpudata *g))compyte_get_extension("cuda_get_ptr");']
def c_support_code_apply(self, node, nodename):
# REMEMBER TO RAISE c_code_cache_version when changing any of
# these files
files = ['conv_kernel.cu', 'conv_full_kernel.cu', 'conv.cu']
codes = [open(os.path.join(os.path.split(__file__)[0], f)).read()
for f in files]
codes = ["CUdeviceptr (*cuda_get_ptr_raw)(gpudata *g);",
"float* cuda_get_ptr(PyGpuArrayObject * o){return (float*) cuda_get_ptr_raw(o->ga.data);}",
"const float* cuda_get_ptr(const PyGpuArrayObject * o){return (float*) cuda_get_ptr_raw(o->ga.data);}"]
codes += [open(os.path.join(os.path.split(__file__)[0], f)).read()
for f in files]
return reduce(str.__add__, codes)
def c_compiler(self):
return NVCC_compiler
def c_code(self, node, nodename, inp, out_, sub):
img, kern = inp
out, = out_
......@@ -226,7 +247,8 @@ class GpuConv(gof.Op):
}
// TODO, make out be decref before we alloc out2!
CudaNdarray * out2 = (CudaNdarray *)CudaNdarray_Conv(%(img)s, %(kern)s,
PyGpuArrayObject * out2 = (PyGpuArrayObject *)PyGpuArray_Conv(
%(img)s, %(kern)s,
%(out)s, mode,
dx, dy,
version, verbose,
......
theano/sandbox/gpuarray/conv_full_kernel.cu
浏览文件 @ 5fc89c03
......@@ -4,7 +4,8 @@
//grid block size=batch_id
//dynamic shared memory: img_len*img_wid+kern_len*kern_wid
__global__ void
conv_full_patch_split( float* img, float* kern, float* out, int img_len, int img_wid, int kern_len, int kern_wid, int nb_split)
conv_full_patch_split(const float* img, const float* kern, float* out,
int img_len, int img_wid, int kern_len, int kern_wid, int nb_split)
{
int __shared__ out_len, out_wid, nb_thread_id;
out_len = img_len + kern_len - 1;
......@@ -60,7 +61,7 @@ conv_full_patch_split( float* img, float* kern, float* out, int img_len, int img
//grid block size=batch_id, nkern
//dynamic shared memory: img_len*img_wid+kern_len*kern_wid
__global__ void
conv_full_patch( float* img, float* kern, float* out,
conv_full_patch( const float* img, const float* kern, float* out,
int img_len, int img_wid,
int kern_len, int kern_wid, int nkern, int nstack)
{
......@@ -122,7 +123,7 @@ conv_full_patch( float* img, float* kern, float* out,
template<bool img_c_contiguous_2d, bool kern_c_contiguous_2d>
__global__ void
conv_full_patch_stack( float* img, float* kern, float* out,
conv_full_patch_stack( const float* img, const float* kern, float* out,
int img_len, int img_wid,
int kern_len, int kern_wid, int nkern, int nstack,
int img_stride_col, int img_stride_row,
......@@ -133,7 +134,7 @@ conv_full_patch_stack( float* img, float* kern, float* out,
out_len = img_len + kern_len - 1;
out_wid = img_wid + kern_wid - 1;
nb_thread_id = blockDim.y*blockDim.x;//blockDim.z*
float __shared__ *kern_, *img_;
const float __shared__ *kern_, *img_;
extern __shared__ float s_data[];
const int batch_id = blockIdx.x;
......@@ -201,7 +202,7 @@ conv_full_patch_stack( float* img, float* kern, float* out,
*/
template<bool flipped_kern, int KERN_WIDTH, bool c_contiguous, bool split, bool low_mem >
__global__ void
conv_full_patch_stack_padded( float* img, float* kern, float* out,
conv_full_patch_stack_padded( const float* img, const float* kern, float* out,
const int img_len, const int img_wid,
const int kern_len, const int kern_wid,
const int nkern, const int nstack,
......@@ -365,7 +366,7 @@ template <> __device__ float everything_dot<1>(const float * x, const int sx, co
}
template<int NSTACK>
__global__ void
conv_full_load_everything( float* img, float* kern, float* out,
conv_full_load_everything( const float* img, const float* kern, float* out,
int img_len, int img_wid,
int kern_len, int kern_wid, int nkern, int nstack,
int img_stride_col, int img_stride_row,
......
theano/sandbox/gpuarray/conv_kernel.cu
浏览文件 @ 5fc89c03
......@@ -221,7 +221,7 @@ __device__ void store_or_accumulate(float& dst,const float value ){
*/
template<bool flipped_kern, int KERN_WIDTH, bool split>
__global__ void
conv_patch( float* img, float* kern, float* out,
conv_patch( const float* img, const float* kern, float* out,
int img_len, int img_wid, int kern_len, int kern_wid,
int nkern, int nstack)
{
......@@ -304,7 +304,7 @@ conv_patch( float* img, float* kern, float* out,
*/
template<bool flipped_kern, bool accumulate, int KERN_WIDTH, bool img_c_contiguous_2d, bool kern_c_contiguous_2d, bool split, bool preload_full_kern, bool subsample>
__global__ void
conv_patch_stack( float* img, float* kern, float* out,
conv_patch_stack( const float* img, const float* kern, float* out,
int img_len, int img_wid, int kern_len, int kern_wid,
int out_len, int out_wid,
int nkern, int nstack, int img_stride_col,int img_stride_row,
......@@ -375,7 +375,7 @@ conv_patch_stack( float* img, float* kern, float* out,
out_row*out_wid+out_col],sum);
}else{
float __shared__ *kern_, *img_;
const float __shared__ *kern_, *img_;
int __shared__ out_len_max;
kern_=kern+kern_stride_nkern*kern_id;//the good nkern
......@@ -456,7 +456,7 @@ conv_patch_stack( float* img, float* kern, float* out,
*/
template<bool flipped_kern, int KERN_WIDTH, bool c_contiguous, bool split, bool preload_full_kern>
__global__ void
conv_patch_stack_reduce( float* img, float* kern, float* out,
conv_patch_stack_reduce( const float* img, const float* kern, float* out,
int img_len, int img_wid, int kern_len, int kern_wid,
int nkern, int nstack, int img_stride_col,int img_stride_row,
int img_stride_stack, int img_stride_batch,
......@@ -572,7 +572,7 @@ conv_patch_stack_reduce( float* img, float* kern, float* out,
*/
template<int KERN_WIDTH, bool c_contiguous>
__global__ void
conv_rows( float* img, float* kern, float* out,
conv_rows( const float* img, const float* kern, float* out,
int img_len, int img_wid, int kern_len, int kern_wid,
int nkern, int nstack,
int img_stride_col, int img_stride_row,
......@@ -633,7 +633,7 @@ conv_rows( float* img, float* kern, float* out,
*/
template<int KERN_WIDTH, bool c_contiguous>
__global__ void
conv_rows_stack( float* img, float* kern, float* out,
conv_rows_stack( const float* img, const float* kern, float* out,
const int img_len, const int img_wid, const int kern_len, const int kern_wid,
const int nkern, const int nstack,
const int img_stride_col, const int img_stride_row,
......@@ -731,7 +731,7 @@ conv_rows_stack( float* img, float* kern, float* out,
*/
template<int KERN_WIDTH, bool c_contiguous, bool preload_full_kern>
__global__ void
conv_rows_stack2( float* img, float* kern, float* out,
conv_rows_stack2(const float* img, const float* kern, float* out,
const int img_len, const int img_wid, const int kern_len, const int kern_wid,
const int nkern, const int nstack,
const int img_stride_col, const int img_stride_row,
......@@ -831,8 +831,8 @@ conv_valid_row_reduce(int nB, int nK, int stacklen,
int img_len, int img_wid,
int kern_len, int kern_wid,
int out_len, int out_wid, //physical
float *img, int img_str_B, int img_str_S, int img_str_R, int img_str_C,
float *kern, int kern_str_K, int kern_str_S, int kern_str_R, int kern_str_C,
const float *img, int img_str_B, int img_str_S, int img_str_R, int img_str_C,
const float *kern, int kern_str_K, int kern_str_S, int kern_str_R, int kern_str_C,
float *out, int out_str_B, int out_str_K, int out_str_R, int out_str_C ,
int subsample_rows, int subsample_cols,
const int initial_reduce_boundary)
......@@ -859,8 +859,8 @@ conv_valid_row_reduce(int nB, int nK, int stacklen,
float sum = 0.0f;
if(stack_loop){
for (; ss < stacklen; ss+=blockDim.x){
float * kk_0 = kern + iK*kern_str_K + ss*kern_str_S + rr*kern_str_R;
float * ii_0 = img + iB*img_str_B + ss*img_str_S + img_rr*img_str_R + (iC_logical + kern_wid - 1)*img_str_C;
const float * kk_0 = kern + iK*kern_str_K + ss*kern_str_S + rr*kern_str_R;
const float * ii_0 = img + iB*img_str_B + ss*img_str_S + img_rr*img_str_R + (iC_logical + kern_wid - 1)*img_str_C;
for (int cc = 0; cc < kern_wid; ++cc)
{
sum += kk_0[0] * ii_0[0];
......@@ -869,8 +869,8 @@ conv_valid_row_reduce(int nB, int nK, int stacklen,
}
}
}else{
float * kk_0 = kern + iK*kern_str_K + ss*kern_str_S + rr*kern_str_R;
float * ii_0 = img + iB*img_str_B + ss*img_str_S + img_rr*img_str_R + (iC_logical + kern_wid - 1)*img_str_C;
const float * kk_0 = kern + iK*kern_str_K + ss*kern_str_S + rr*kern_str_R;
const float * ii_0 = img + iB*img_str_B + ss*img_str_S + img_rr*img_str_R + (iC_logical + kern_wid - 1)*img_str_C;
for (int cc = 0; cc < kern_wid; ++cc)
{
sum += kk_0[0] * ii_0[0];
......@@ -925,8 +925,8 @@ conv_reference_valid(int nB, int nK, int stacklen,
int img_len, int img_wid,
int kern_len, int kern_wid,
int out_len, int out_wid, //physical
float *img, int img_str_B, int img_str_S, int img_str_R, int img_str_C,
float *kern, int kern_str_K, int kern_str_S, int kern_str_R, int kern_str_C,
const float *img, int img_str_B, int img_str_S, int img_str_R, int img_str_C,
const float *kern, int kern_str_K, int kern_str_S, int kern_str_R, int kern_str_C,
float *out, int out_str_B, int out_str_K, int out_str_R, int out_str_C ,
int subsample_rows, int subsample_cols)
{
......@@ -984,8 +984,8 @@ conv_reference_full(int nB, int nK, int stacklen,
int img_len, int img_wid,
int kern_len, int kern_wid,
int out_len, int out_wid, //physical dimensions
float *img, int img_str_B, int img_str_S, int img_str_R, int img_str_C,
float *kern, int kern_str_K, int kern_str_S, int kern_str_R, int kern_str_C,
const float *img, int img_str_B, int img_str_S, int img_str_R, int img_str_C,
const float *kern, int kern_str_K, int kern_str_S, int kern_str_R, int kern_str_C,
float *out, int out_str_B, int out_str_K, int out_str_R, int out_str_C,
int subsample_rows, int subsample_cols)
{
......
theano/sandbox/gpuarray/tests/test_conv_cuda_ndarray.py
浏览文件 @ 5fc89c03
......@@ -25,6 +25,7 @@ from theano.tests.unittest_tools import seed_rng
from theano.sandbox.gpuarray.tests.test_basic_ops import (mode_with_gpu,
mode_without_gpu)
from theano.sandbox.gpuarray.type import GpuArrayType
from theano.sandbox.gpuarray.conv import GpuConv
import pygpu
gftensor4 = GpuArrayType('float32', [False] * 4)
......@@ -159,11 +160,11 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
t1 = time.time()
i = gftensor4()
k = gftensor4()
op = theano.sandbox.cuda.blas.GpuConv(border_mode=mode,
subsample=subsample,
version=version,
verbose=verbose,
kshp=compile_kshp)(i, k)
op = GpuConv(border_mode=mode,
subsample=subsample,
version=version,
verbose=verbose,
kshp=compile_kshp)(i, k)
f = theano.function([i, k], op, mode=mode_with_gpu)
gpuval = f(img, kern)
t2 = time.time()
......@@ -731,7 +732,7 @@ class TestConv2DGPU(unittest.TestCase):
func = theano.function([a, A], image_estimate, mode=mode_with_gpu)
#theano.printing.debugprint(func,)
assert any([isinstance(node.op, theano.sandbox.cuda.blas.GpuConv)
assert any([isinstance(node.op, GpuConv)
for node in func.maker.fgraph.toposort()])
a_in = numpy.random.randn(*featshp).astype("float32")
......
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