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pytensor
提交 85ee3d3e authored 作者: Ian Goodfellow's avatar Ian Goodfellow
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removed 'sum' from name of reduce kernels

上级 1ca702ef
隐藏空白字符变更
内嵌 并排
正在显示 包含 27 行增加 和 25 行删除
theano/sandbox/cuda/basic_ops.py
浏览文件 @ 85ee3d3e
......@@ -687,9 +687,9 @@ class GpuCAReduce(GpuOp):
.. code-block:: c
if (verbose)
printf("running kernel_reduce_sum_10_%(name)s\\n");
printf("running kernel_reduce_10_%(name)s\\n");
int n_shared = sizeof(float) * n_threads.x;
kernel_reduce_sum_10_%(name)s<<<n_blocks, n_threads,
kernel_reduce_10_%(name)s<<<n_blocks, n_threads,
n_shared>>>(
CudaNdarray_HOST_DIMS(%(x)s)[0],
CudaNdarray_HOST_DIMS(%(x)s)[1],
......@@ -713,7 +713,7 @@ class GpuCAReduce(GpuOp):
nd_out = ndim - sum(self.reduce_mask)
print >> sio, """
if (verbose)
printf("running kernel_reduce_sum_%(pattern)s_%(name)s\\n");
printf("running kernel_reduce_%(pattern)s_%(name)s\\n");
int n_shared = sizeof(float) * n_threads.x * n_threads.y * n_threads.z;
if (verbose>1)
printf("n_threads.x=%%d, n_threads.y=%%d, n_threads.z=%%d,"
......@@ -723,7 +723,7 @@ class GpuCAReduce(GpuOp):
n_threads.x*n_threads.y*n_threads.z,
n_blocks.x,n_blocks.y,
n_blocks.x*n_blocks.y, n_shared);
kernel_reduce_sum_%(pattern)s_%(name)s<<<n_blocks, n_threads, n_shared>>>(
kernel_reduce_%(pattern)s_%(name)s<<<n_blocks, n_threads, n_shared>>>(
""" % locals()
for i in xrange(ndim):
print >> sio, """
......@@ -752,7 +752,7 @@ class GpuCAReduce(GpuOp):
PyErr_Format(PyExc_RuntimeError,
"Cuda error: %%s: %%s."
" (grid: %%i x %%i; block: %%i x %%i x %%i)\\n",
"kernel_reduce_sum_%(pattern)s_%(name)s",
"kernel_reduce_%(pattern)s_%(name)s",
cudaGetErrorString(sts),
n_blocks.x,
n_blocks.y,
......@@ -769,9 +769,11 @@ class GpuCAReduce(GpuOp):
self._op_guard()
"""Return a string to declare a kernel function
The result will look something like this:
.. code-block:: c
static __global__ void kernel_reduce_sum_110_%(nodename)s(
static __global__ void kernel_reduce_110_%(nodename)s(
const int d0,
const int d1,
const int d2,
......@@ -792,7 +794,7 @@ class GpuCAReduce(GpuOp):
sio = StringIO.StringIO()
print >> sio, """
static __global__ void kernel_reduce_sum_%(pattern)s_%(nodename)s(
static __global__ void kernel_reduce_%(pattern)s_%(nodename)s(
""" % locals()
for i in xrange(ndim):
print >> sio, """
......@@ -965,11 +967,11 @@ class GpuCAReduce(GpuOp):
std::min(CudaNdarray_SIZE(%(x)s),
NUM_VECTOR_OP_THREADS_PER_BLOCK));
dim3 n_blocks(1);
if (verbose) printf("running kernel_reduce_sum_ccontig_%(name)s"
if (verbose) printf("running kernel_reduce_ccontig_%(name)s"
" n_threads.x=%%d, size=%%d, ndim=%%d\\n",
n_threads.x,CudaNdarray_SIZE(%(x)s),%(x)s->nd);
int n_shared = sizeof(float) * n_threads.x;
kernel_reduce_sum_ccontig_%(name)s<<<n_blocks, n_threads, n_shared>>>(
kernel_reduce_ccontig_%(name)s<<<n_blocks, n_threads, n_shared>>>(
CudaNdarray_SIZE(%(x)s),
CudaNdarray_DEV_DATA(%(x)s),
CudaNdarray_DEV_DATA(%(z)s));
......@@ -980,7 +982,7 @@ class GpuCAReduce(GpuOp):
PyErr_Format(PyExc_RuntimeError,
"Cuda error: %%s: %%s."
" (grid: %%i x %%i; block: %%i x %%i x %%i)\\n",
"kernel_reduce_sum_ccontig_%(name)s",
"kernel_reduce_ccontig_%(name)s",
cudaGetErrorString(sts),
n_blocks.x,
n_blocks.y,
......@@ -1031,9 +1033,9 @@ class GpuCAReduce(GpuOp):
:param N: the number of 1 in the pattern N=1 -> 01, N=2 -> 011 N=3 ->0111
Work for N=1,2,3
"""
self._op_guard()
assert N in [1, 2, 3]
makecall = self._makecall(node, name, x, z, fail)
self._op_guard()
N_pattern = ''.join(['1'] * N)
param_dim = ",".join(["CudaNdarray_HOST_DIMS(%(x)s)[%(i)s]" % locals()
for i in xrange(N + 1)])
......@@ -1100,13 +1102,13 @@ class GpuCAReduce(GpuOp):
NUM_VECTOR_OP_BLOCKS));
if (verbose) {
fprintf(stderr,
"running kernel_reduce_sum_10_%(name)s n_blocks=(%%i,%%i)\\n",
"running kernel_reduce_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>>>(
kernel_reduce_010_%(name)s<<<n_blocks, n_threads, n_shared>>>(
1,
CudaNdarray_HOST_DIMS(%(x)s)[0],
CudaNdarray_HOST_DIMS(%(x)s)[1],
......@@ -1125,7 +1127,7 @@ class GpuCAReduce(GpuOp):
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",
"kernel_reduce_010_%(name)s",
cudaGetErrorString(sts),
n_blocks.x,
n_blocks.y,
......@@ -1177,7 +1179,7 @@ class GpuCAReduce(GpuOp):
if (n_blocks.x > NUM_VECTOR_OP_BLOCKS) n_blocks.x = NUM_VECTOR_OP_BLOCKS;
if (n_blocks.x*n_blocks.y > NUM_VECTOR_OP_BLOCKS) n_blocks.y = NUM_VECTOR_OP_BLOCKS/n_blocks.x;
int n_shared = 0;
kernel_reduce_sum_010_AD_%(name)s<<<n_blocks, n_threads, n_shared>>>(
kernel_reduce_010_AD_%(name)s<<<n_blocks, n_threads, n_shared>>>(
A,B,C,D,
CudaNdarray_DEV_DATA(%(x)s),
CudaNdarray_HOST_STRIDES(%(x)s)[0],
......@@ -1194,7 +1196,7 @@ class GpuCAReduce(GpuOp):
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",
"kernel_reduce_010_%(name)s",
cudaGetErrorString(sts),
n_blocks.x,
n_blocks.y,
......@@ -1247,7 +1249,7 @@ class GpuCAReduce(GpuOp):
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_%(pattern)s_%(name)s",
"kernel_reduce_%(pattern)s_%(name)s",
cudaGetErrorString(sts),
n_blocks.x,
n_blocks.y,
......@@ -1487,7 +1489,7 @@ class GpuCAReduce(GpuOp):
# it only runs on ONE multiprocessor
reducebuf = self._k_reduce_buf('Z[0]')
print >> sio, """
static __global__ void kernel_reduce_sum_ccontig_%(nodename)s(
static __global__ void kernel_reduce_ccontig_%(nodename)s(
const unsigned int d0,
const float *A,
float * Z)
......@@ -1514,7 +1516,7 @@ class GpuCAReduce(GpuOp):
# it only runs on ONE multiprocessor
reducebuf = self._k_reduce_buf('Z[0]')
print >> sio, """
static __global__ void kernel_reduce_sum_1_%(nodename)s(
static __global__ void kernel_reduce_1_%(nodename)s(
const unsigned int d0,
const float *A, const int sA0,
float * Z)
......@@ -1542,7 +1544,7 @@ class GpuCAReduce(GpuOp):
# it only runs on ONE multiprocessor
reducebuf = self._k_reduce_buf('Z[0]')
print >> sio, """
static __global__ void kernel_reduce_sum_11_%(nodename)s(
static __global__ void kernel_reduce_11_%(nodename)s(
const int d0,
const int d1,
const float *A, const int sA0, const int sA1,
......@@ -1623,7 +1625,7 @@ class GpuCAReduce(GpuOp):
# memory (a segment of a column).
reducebuf = self._k_reduce_buf('Z[i0 * sZ0 + i2*sZ1]')
print >> sio, """
static __global__ void kernel_reduce_sum_010_%(nodename)s(
static __global__ void kernel_reduce_010_%(nodename)s(
const int d0,
const int d1,
const int d2,
......@@ -1658,7 +1660,7 @@ class GpuCAReduce(GpuOp):
""" % locals()
if self.reduce_mask == (0, 1, 0):
print >> sio, """
static __global__ void kernel_reduce_sum_010_AD_%(nodename)s(
static __global__ void kernel_reduce_010_AD_%(nodename)s(
const int A,
const int B,
const int C,
......@@ -1746,7 +1748,7 @@ class GpuCAReduce(GpuOp):
# memory (a segment of a column).
reducebuf = self._k_reduce_buf('Z[blockIdx.x * sZ0]')
print >> sio, """
static __global__ void kernel_reduce_sum_110_%(nodename)s(
static __global__ void kernel_reduce_110_%(nodename)s(
const int d0,
const int d1,
const int d2,
......@@ -1827,7 +1829,7 @@ class GpuCAReduce(GpuOp):
# threads per block for each element per row.
reducebuf = self._k_reduce_buf('Z[i0 * sZ0 + i1 * sZ1]')
print >> sio, """
static __global__ void kernel_reduce_sum_001_%(nodename)s(
static __global__ void kernel_reduce_001_%(nodename)s(
const int d0,
const int d1,
const int d2,
......@@ -1940,7 +1942,7 @@ class GpuCAReduce(GpuOp):
if self.reduce_mask == (1, 0, 1, 1):
reducebuf = self._k_reduce_buf('Z[blockIdx.x*sZ0]')
print >> sio, """
static __global__ void kernel_reduce_sum_1011_%(nodename)s(
static __global__ void kernel_reduce_1011_%(nodename)s(
const unsigned int d0,
const unsigned int d1,
const unsigned int d2,
......
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