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提交 42927b2b authored 作者: James Bergstra's avatar James Bergstra
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theano/sandbox/cuda/basic_ops.py
浏览文件 @ 42927b2b
......@@ -441,7 +441,7 @@ class GpuSum(Op):
j = 0
for i in xrange(nd_in):
if not self.reduce_mask[i]:
if not self.reduce_mask[i]:
print >> sio, 'new_dims[%(j)s] = CudaNdarray_HOST_DIMS(%(x)s)[%(i)s];' % locals()
j += 1
......@@ -453,11 +453,10 @@ class GpuSum(Op):
PyErr_Format(PyExc_RuntimeError, "Failed to allocate output");
%(fail)s;
}
}
""" %locals()
# \begin bracket the reduction in a check that there is actually work to do
# \begin bracket the reduction in a check that there is actually work to do
print >> sio, """
if (CudaNdarray_SIZE(%(z)s))
{
......@@ -472,12 +471,10 @@ class GpuSum(Op):
#TODO: check if we are ccontiguous when we un-dimshuffle
#TODO: if only some dims are ccontiguous, call version with less dims.
print >> sio, 'if(CudaNdarray_is_c_contiguous(%(x)s)){'%locals()
self.c_code_reduce_ccontig(sio, node, name, x, z, fail)
print >> sio, "}else{"
getattr(self, 'c_code_reduce_%s'%(''.join(str(i) for i in self.reduce_mask)))(sio, node, name, x, z, fail)
print >> sio, "}"
else:
getattr(self, 'c_code_reduce_%s'%(''.join(str(i) for i in self.reduce_mask)))(sio, node, name, x, z, fail)
......@@ -826,8 +823,16 @@ class GpuSum(Op):
dim3 n_threads(
std::min(CudaNdarray_HOST_DIMS(%(x)s)[0],
NUM_VECTOR_OP_THREADS_PER_BLOCK));
dim3 n_blocks(1,CudaNdarray_HOST_DIMS(%(x)s)[1]);
if (verbose) printf("running kernel_reduce_sum_10_%(name)s\\n");
dim3 n_blocks(1,
std::min(CudaNdarray_HOST_DIMS(%(x)s)[1],
NUM_VECTOR_OP_BLOCKS));
if (verbose) {
fprintf(stderr,
"running kernel_reduce_sum_10_%(name)s n_blocks=(%%i,%%i)\\n",
n_blocks.x,
n_blocks.y);
}
assert( CudaNdarray_HOST_DIMS(%(x)s)[1] == CudaNdarray_HOST_DIMS(%(z)s)[0]);
int n_shared = sizeof(float) * n_threads.x;
kernel_reduce_sum_010_%(name)s<<<n_blocks, n_threads, n_shared>>>(
1,
......@@ -843,7 +848,7 @@ class GpuSum(Op):
);
CNDA_THREAD_SYNC;
cudaError_t sts = cudaGetLastError();
if (cudaSuccess != sts)
if (cudaSuccess != sts)
{
PyErr_Format(PyExc_RuntimeError, "Cuda error: %%s: %%s. (grid: %%i x %%i; block: %%i x %%i x %%i)\\n",
"kernel_reduce_sum_010_%(name)s",
......@@ -1175,9 +1180,7 @@ class GpuSum(Op):
""" %locals()
def c_code_cache_version(self):
#return ()
return (19,)
return (20,)
def c_support_code_apply(self, node, nodename):
sio = StringIO.StringIO()
......
theano/sandbox/cuda/blas.py
浏览文件 @ 42927b2b
......@@ -363,9 +363,10 @@ class GpuConv(Op):
return ['cuda_ndarray.cuh','<stdio.h>']
def c_code_cache_version(self):
return (0,8)
return (0,9) # raise this whenever modifying any of the support_code_files
def c_support_code_apply(self, node, nodename):
# REMEMBER TO RAISE c_code_cache_version when changing any of these files
return open(os.path.join(os.path.split(__file__)[0],'conv_kernel.cu')).read()+\
open(os.path.join(os.path.split(__file__)[0],'conv_full_kernel.cu')).read()+\
open(os.path.join(os.path.split(__file__)[0],'conv.cu')).read()
......@@ -405,8 +406,7 @@ class GpuConv(Op):
CudaNdarray * out2 = (CudaNdarray *)CudaNdarray_Conv(%(img)s, %(kern)s, %(out)s,
mode, dx, dy, version, verbose);
if(%(out)s && %(out)s==out2)
Py_DECREF(out2);//CudaNdarray_Conv incremented the count to out
Py_XDECREF(%(out)s);
%(out)s = out2;
"""%sub
......
theano/sandbox/cuda/conv_kernel.cu
浏览文件 @ 42927b2b
// REMEMBER TO RAISE c_code_cache_version when changing this file
//
//implement the valid convolution only
/*
......@@ -38,6 +40,8 @@ for (int iter_m=0; iter_m < Os[0]; iter_m++) {
#define BS(i, j) Bs[i][j]
#endif
*/
#define MAX(a,b) ((a)>(b)?(a):(b))
#define MIN(a,b) ((a)<(b)?(a):(b))
const unsigned long int COALESCED_ALIGN = 0xFFFFFFFFFFFFFF00; // zero-out the trailing bits of pointers
#define MASKED_OFFSET(src) (((int)((unsigned long int)src - (((unsigned long int)src) & COALESCED_ALIGN))) / sizeof(float))
......@@ -45,8 +49,9 @@ const unsigned long int COALESCED_ALIGN = 0xFFFFFFFFFFFFFF00; // zero-out the tr
__device__ void load_to_shared(float * dst, const float * src, const int thread_id, int nb_thread, const int N, const bool flipped=false){
if (nb_thread < 64)
{
if(flipped)
//TODO very slow on device before 1.3. make access to kern sequential and access to d_kern flipped.
if(flipped)
//TODO very slow on device before 1.3.
// make access to kern sequential and access to d_kern flipped.
for(int i=thread_id;i<N;i+=nb_thread)
dst[i]=src[N - 1 - i];
//dst[N-1-i]=src[i];
......@@ -88,10 +93,9 @@ __device__ void load_to_shared(float * dst, const float * src, const int thread_
const bool flipped=false, const bool c_contiguous=true){
if(flipped && ! c_contiguous){
for(int i=thread_id;i<nb_row*nb_col;i+=nb_thread)
dst[nb_row*nb_col-1-i]=src[i/nb_col*stride_row+i%nb_col*stride_col];
dst[nb_row*nb_col-1-i]=src[(i/nb_col)*stride_row+(i%nb_col)*stride_col];
}else if(c_contiguous){
load_to_shared(dst, src, thread_id, nb_thread, nb_col*nb_row, flipped);
}else if(flipped){//c_contiguous==true
//TODO very slow on device before 1.3. make access to kern sequential and access to d_kern flipped.
int N=nb_col*nb_row;
......@@ -440,10 +444,12 @@ conv_patch_stack_reduce( float* img, float* kern, float* out,
int kern_stride_col, int kern_stride_row,
int kern_stride_stack, int kern_stride_nkern)
{
int __shared__ out_len, out_wid, nb_thread_id;
out_len = img_len - kern_len + 1;
out_wid = img_wid - kern_wid + 1;
nb_thread_id = blockDim.z*blockDim.y*blockDim.x;
//int __shared__ out_len, out_wid, nb_thread_id;
//out_len = img_len - kern_len + 1;
//out_wid = img_wid - kern_wid + 1;
const int out_wid = blockDim.x;
const int out_len = blockDim.y;
const int nb_thread_id = blockDim.z*blockDim.y*blockDim.x;
extern __shared__ float s_data[];
......@@ -458,9 +464,16 @@ conv_patch_stack_reduce( float* img, float* kern, float* out,
int out_row = ty;//output row
const int thread_id = tz*blockDim.y*blockDim.x+ty*blockDim.x+tx;
float * d_img=&s_data[0];//size of [IMAGE_LEN * IMAGE_WID];
float * d_kern=&s_data[img_len * img_wid];//size of [(preload_full_kern?KERNEL_LEN:blockDim.z) * KERNEL_WID];
float * d_reduce=&s_data[img_len*img_wid+(preload_full_kern?kern_len:blockDim.z)*kern_wid];
//d_img size [IMAGE_LEN * IMAGE_WID];
float * d_img=&s_data[0];
//d_kern size[(preload_full_kern?KERNEL_LEN:blockDim.z) * KERNEL_WID]
float * d_kern=&s_data[img_len * img_wid];
//d_reduce size [n_threads]
//N.B. this overlaps with d_img and d_kern!
float * d_reduce=&s_data[0];
float sum = 0.0f;
kern+=kern_stride_nkern*blockIdx.y;//the good nkern
......@@ -471,30 +484,31 @@ conv_patch_stack_reduce( float* img, float* kern, float* out,
__syncthreads();
load_to_shared(d_img, img, thread_id, nb_thread_id, img_wid, img_len,
img_stride_col, img_stride_row, false, c_contiguous);
if(!(split && ! preload_full_kern))
load_to_shared(d_kern, kern, thread_id, nb_thread_id, kern_wid, kern_len,
kern_stride_col, kern_stride_row, flipped_kern, c_contiguous);
__syncthreads();
if(split && ! preload_full_kern){
for(int first_row=0, row=tz;first_row<kern_len;row+=blockDim.z, first_row+=blockDim.z){
int idx3;
//TODO: test/check for flipped_kern
if(flipped_kern)
idx3=(kern_len-(first_row)-blockDim.z);//the current last row flipped
else
idx3=first_row;
for(int first_row=0;first_row<kern_len;first_row+=blockDim.z){
//N.B. - Jan 30, 2011 with CUDA 3.2 I found that without the explicit cast to
// (int)blockDim.z, idx3 would sometimes be negative. I'm rusty on my signed vs. unsigned
// details, but that seemed really weird. tricky bug to find too.
int idx3 = flipped_kern
? max((kern_len - (int)blockDim.z - first_row),0)
: first_row;
int len3 = min(blockDim.z, kern_len - first_row);
__syncthreads();
load_to_shared(d_kern, kern+idx3*kern_stride_row, thread_id, nb_thread_id, kern_wid, blockDim.z,
load_to_shared(d_kern, kern+idx3*kern_stride_row, thread_id, nb_thread_id, kern_wid, len3,
kern_stride_col, kern_stride_row, flipped_kern, c_contiguous);
__syncthreads();
const float* idx_kern=&d_kern[tz*kern_stride_row];
const float* idx_in=&d_img[(row+out_row)*img_wid+out_col];
const float* idx_kern=&d_kern[tz*kern_wid];
const float* idx_in=&d_img[(first_row+tz+out_row)*img_wid+out_col];
float sum2 = 0;
if(row<kern_len)
if(tz<len3)
convolutionRowNoFlip<KERN_WIDTH>(sum2,idx_in,idx_kern,kern_wid);
sum+=sum2;
}
}else if(split){
load_to_shared(d_kern, kern, thread_id, nb_thread_id, kern_wid, kern_len,
kern_stride_col, kern_stride_row, flipped_kern, c_contiguous);
__syncthreads();
for(int row=tz;row<kern_len;row+=blockDim.z){
const float* idx_kern=&d_kern[row*kern_wid];
const float* idx_in=&d_img[(row+out_row)*img_wid+out_col];
......@@ -504,18 +518,21 @@ conv_patch_stack_reduce( float* img, float* kern, float* out,
int row = tz;//The row of the kernel.
const float* idx_kern=&d_kern[row*kern_wid];
const float* idx_in=&d_img[(row+out_row)*img_wid+out_col];
load_to_shared(d_kern, kern, thread_id, nb_thread_id, kern_wid, kern_len,
kern_stride_col, kern_stride_row, flipped_kern, c_contiguous);
__syncthreads();
convolutionRowNoFlip<KERN_WIDTH>(sum,idx_in,idx_kern,kern_wid);
}
__syncthreads(); // ensure calculations have completed before any thread starts changing the shared memory
}
//reduce
//reduce no sync because previous loop ends with sync
d_reduce[thread_id]=sum;
__syncthreads();
if(thread_id<out_len*out_wid){
sum=0;
for(int i=0;i<blockDim.z;i++){
sum+=d_reduce[thread_id+i*blockDim.x*blockDim.y];
if(thread_id<out_len*out_wid){ // blockDim.x==out_wid, blockDim.y==out_len
//sum=0;
for(int i=1;i<blockDim.z;i++){
sum+=d_reduce[thread_id+i*out_wid*out_len];
}
out[batch_id*out_wid*out_len*nkern+//the good batch
out_wid*out_len*blockIdx.y+//the output image
......
theano/sandbox/cuda/cuda_ndarray.cu
浏览文件 @ 42927b2b
......@@ -134,7 +134,9 @@ CudaNdarray_uninit(CudaNdarray*self)
assert(self->devdata);
if (device_free(self->devdata))
{
std::cerr << "!!!! error freeing device memory\n";
fprintf(stderr,
"!!!! error freeing device memory %p (self=%p)\n",
self->devdata, self);
rval = -1;
}
self->devdata = NULL;
......@@ -144,7 +146,9 @@ CudaNdarray_uninit(CudaNdarray*self)
{
if (device_free(self->dev_structure))
{
std::cerr << "!!!! error freeing device memory\n";
fprintf(stderr,
"!!!! error freeing dev_structure memory %p (self=%p)\n",
self->dev_structure, self);
rval = -1;
}
self->dev_structure = NULL;
......@@ -1848,6 +1852,8 @@ CudaNdarray_ptr_int_size(PyObject* _unused, PyObject* args)
}
get_gpu_ptr_size<<<1,1>>>(gpu_data);
if (cudaSuccess != cublasGetError()){
device_free(gpu_data);
return PyErr_Format(PyExc_RuntimeError,
"CudaNdarray_ptr_int_size: error when calling the gpu code.");
}
......
theano/sandbox/cuda/cuda_ndarray.cuh
浏览文件 @ 42927b2b
......@@ -403,6 +403,11 @@ int CudaNdarray_alloc_contiguous(CudaNdarray *self, const int nd, const inttype
self->devdata = 0;
return -1;
}
if (0)
fprintf(stderr,
"Allocated devdata %p (self=%p)\n",
self->devdata,
self);
self->data_allocated = size;
}
return 0;
......
theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py
浏览文件 @ 42927b2b
......@@ -84,14 +84,36 @@ def py_conv_scipy(img, kern, mode, subsample):
def _params_allgood_header():
print "ishape kshape #Mflops CPU Mflops GPU Mflops Speedup"
def _params_allgood(ishape, kshape, mode, subsample=(1,1), img_stride=(1,1), kern_stride=(1,1), version=-1, verbose=0, random=True, print_=None, id=None, rtol=1e-5, atol = 1e-8, nb_iter=0, ones=False):
def test_example():
# Test a specific configuration that was failing in one of the big unit-tests
# This configuration information was read from one of the 'FAIL' lines printed by
# _params_allgood during a nosetest run
#
# now it can be tested directly by nosetests test_conv_cuda_ndarray.py:test_example
assert _params_allgood(
(1,1,4,4),
(1,1,3,2),
'valid',
version=13,
random=False)
def _params_allgood(ishape, kshape, mode, subsample=(1,1), img_stride=(1,1),
kern_stride=(1,1), version=-1, verbose=0, random=True, print_=None,
id=None, rtol=1e-5, atol = 1e-8, nb_iter=0, ones=False):
#
# This function is the core of several of the big unit-test drivers,
# but it can also be used very directly on its own to test a specific
# kind of convolution.
#
# See `test_example` (above) for an example of how to use this directly.
#
if ones:
assert not random
npy_img = theano._asarray(numpy.ones(ishape), dtype='float32')
npy_kern = -theano._asarray(numpy.ones(kshape), dtype='float32')
elif random:
npy_img = theano._asarray(numpy.random.rand(*ishape), dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape), dtype='float32')
npy_img = theano._asarray(numpy.random.rand(*ishape)+1, dtype='float32')
npy_kern = theano._asarray(numpy.random.rand(*kshape)-2, dtype='float32')
else:
npy_img = theano._asarray(numpy.arange(numpy.prod(ishape)).reshape(ishape), dtype='float32')+1
npy_kern = -(theano._asarray(numpy.arange(numpy.prod(kshape)).reshape(kshape), dtype='float32')+1)
......@@ -155,8 +177,6 @@ def _params_allgood(ishape, kshape, mode, subsample=(1,1), img_stride=(1,1), ker
print "max absolute diff:",diffabs.max(),"avg abs diff:",numpy.average(diffabs)
print "median abs diff:", numpy.median(diffabs), "nb close:",nb_close, "/", diff.size
print "max relatif diff:",pr_diff.max(), "avg rel diff:", numpy.average(pr_diff)
print rval
if not rval and print_!=False:
if npy_img.shape[0]>5:
print "img",npy_img[0]
......@@ -185,9 +205,19 @@ def exec_conv(version, shapes, verbose, random, mode, print_=None, rtol=1e-5, on
for id,(ishape, kshape, subshape, istride, kstride) in enumerate(shapes):
ret=False
try:
ret = _params_allgood(ishape, kshape, mode,
subsample=subshape, img_stride=istride, kern_stride=kstride,
version=ver, verbose=verbose, random=random, id=id,print_=print_,rtol=rtol,ones=ones)
ret = _params_allgood(ishape,
kshape,
mode,
subsample=subshape,
img_stride=istride,
kern_stride=kstride,
version=ver,
verbose=verbose,
random=random,
id=id,
print_=print_,
rtol=rtol,
ones=ones)
except Exception, e:
print ver, id,(ishape, kshape, subshape, istride, kstride)
print e
......
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