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pytensor
提交 6df1cadc authored 作者: Frederic's avatar Frederic
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Add support to gpu reduce for acc and output dtype.

上级 dcc8ea72
隐藏空白字符变更
内嵌 并排
正在显示 包含 126 行增加 和 103 行删除
theano/sandbox/gpuarray/elemwise.py
浏览文件 @ 6df1cadc
......@@ -542,7 +542,7 @@ class GpuDimShuffle(HideC, DimShuffle):
return (4,)
class GpuCAReduceCuda(HideC, CAReduce):
class GpuCAReduceCuda(HideC, CAReduceDtype):
"""GpuCAReduceCuda is a Reduction along some dimensions by a scalar op.
The dimensions along which to reduce is specified by the
......@@ -577,7 +577,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
"""
def __init__(self, scalar_op, axis=None,
reduce_mask=None):
reduce_mask=None, dtype=None, acc_dtype=None):
if reduce_mask is not None:
reduce_mask = tuple(reduce_mask)
self.reduce_mask = reduce_mask
......@@ -585,18 +585,23 @@ class GpuCAReduceCuda(HideC, CAReduce):
# used to make sure that calls to scalar op
# have unique name arguments
self._n_scalar_op_calls = 0
CAReduce.__init__(self, scalar_op, axis=axis)
CAReduceDtype.__init__(self, scalar_op, axis=axis,
dtype=dtype, acc_dtype=acc_dtype)
def __eq__(self, other):
return (type(self) == type(other) and
self.axis == other.axis and
self.reduce_mask == other.reduce_mask and
self.dtype == other.dtype and
self.acc_dtype == other.acc_dtype and
self.scalar_op == other.scalar_op)
def __hash__(self):
return (hash(type(self)) ^
hash(self.axis) ^
hash(self.reduce_mask) ^
hash(self.dtype) ^
hash(self.acc_dtype) ^
hash(type(self.scalar_op)))
def __str__(self):
......@@ -622,7 +627,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
if (x.type.ndim != len(self.reduce_mask)):
raise TypeError("x must have rank %i" % len(self.reduce_mask))
return Apply(self, [x], [GpuArrayType(x.dtype,
return Apply(self, [x], [GpuArrayType(ret.outputs[0].dtype,
ret.outputs[0].type.broadcastable)()])
"""
......@@ -692,8 +697,8 @@ class GpuCAReduceCuda(HideC, CAReduce):
nd_in = node.inputs[0].type.ndim
nd_out = node.outputs[0].type.ndim
dtype = "npy_" + node.outputs[0].dtype
assert node.inputs[0].dtype == node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
assert nd_in - nd_out == sum(self.reduce_mask)
sio = StringIO()
......@@ -757,7 +762,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
if not self.reduce_mask[i]:
print >> sio, 'new_dims[%(j)s] = PyGpuArray_DIMS(%(x)s)[%(i)s];' % locals()
j += 1
out_typecode = dtype_to_typecode(node.outputs[0].dtype)
out_typecode = dtype_to_typecode(out_dtype[4:])
print >> sio, """
Py_XDECREF(%(z)s);
%(z)s = pygpu_empty(%(nd_out)s, new_dims,
......@@ -775,7 +780,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
# \begin bracket the reduction in a check that there is
# actually work to do
if getattr(self.scalar_op, 'identity', None) == 0:
zero_shp = "cudaMemset((%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset), 0, PyGpuArray_SIZE(%(z)s) * sizeof(%(dtype)s))" % locals()
zero_shp = "cudaMemset((%(out_dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset), 0, PyGpuArray_SIZE(%(z)s) * sizeof(%(out_dtype)s))" % locals()
#TODO: elif getattr(self.scalar_op, 'identity', None) == 1:
else:
scalar_op = self.scalar_op
......@@ -827,16 +832,16 @@ class GpuCAReduceCuda(HideC, CAReduce):
if (verbose)
printf("running kernel_reduce_10_%(name)s\\n");
int n_shared = sizeof(%(dtype)s) * n_threads.x * n_threads.y * n_threads.z;
int n_shared = sizeof(%(acc_dtype)s) * n_threads.x * n_threads.y * n_threads.z;
kernel_reduce_10_%(name)s<<<n_blocks, n_threads,
n_shared>>>(
PyGpuArray_DIMS(%(x)s)[0],
PyGpuArray_DIMS(%(x)s)[1],
(%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(dtype)s),
(%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset),
PyGpuArray_STRIDES(%(z)s)[0]/sizeof(%(dtype)s)
(%(in_dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(in_dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(in_dtype)s),
(%(out_dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset),
PyGpuArray_STRIDES(%(z)s)[0]/sizeof(%(out_dtype)s)
);
[
if config.gpuarray.sync:
......@@ -848,7 +853,9 @@ class GpuCAReduceCuda(HideC, CAReduce):
%(fail)s;
}
"""
dtype = "npy_" + node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
acc_dtype = "npy_" + self._acc_dtype(node.inputs[0].dtype)
sio = StringIO()
if pattern is None:
pattern = ''.join(str(c) for c in self.reduce_mask)
......@@ -861,7 +868,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
print >> sio, """
if (verbose)
printf("running kernel_reduce_%(pattern)s_%(name)s\\n");
int n_shared = sizeof(%(dtype)s) * n_threads.x * n_threads.y * n_threads.z;
int n_shared = sizeof(%(acc_dtype)s) * 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,"
" nb_threads=%%d, n_blocks.x=%%d, n_blocks.y=%%d,"
......@@ -877,18 +884,18 @@ class GpuCAReduceCuda(HideC, CAReduce):
PyGpuArray_DIMS(%(x)s)[%(i)s],
""" % locals()
print >> sio, """
(%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset)
(%(in_dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset)
""" % locals()
for i in xrange(ndim):
print >> sio, """
,PyGpuArray_STRIDES(%(x)s)[%(i)s]/sizeof(%(dtype)s)
,PyGpuArray_STRIDES(%(x)s)[%(i)s]/sizeof(%(in_dtype)s)
""" % locals()
print >> sio, """
,(%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset)
,(%(out_dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset)
""" % locals()
for i in xrange(nd_out):
print >> sio, """
,PyGpuArray_STRIDES(%(z)s)[%(i)s]/sizeof(%(dtype)s)
,PyGpuArray_STRIDES(%(z)s)[%(i)s]/sizeof(%(out_dtype)s)
""" % locals()
sync = ""
if config.gpuarray.sync:
......@@ -928,18 +935,19 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d0,
const int d1,
const int d2,
const %(dtype)s *A,
const %(in_dtype)s *A,
const int sA0,
const int sA1,
const int sA2,
%(dtype)s * Z,
%(out_dtype)s * Z,
const int sZ0)
Since the nodename is unique, we don't need to put the name
of the scalar_op in here.
"""
dtype = "npy_" + node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
if reduce_mask is None:
reduce_mask = self.reduce_mask
if ndim is None:
......@@ -956,14 +964,14 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d%(i)s,
""" % locals()
print >> sio, """
const %(dtype)s *A,
const %(in_dtype)s *A,
""" % locals()
for i in xrange(ndim):
print >> sio, """
const int sA%(i)s,
""" % locals()
print >> sio, """
%(dtype)s * Z
%(out_dtype)s * Z
""" % locals()
for i in xrange(ndim - sum(reduce_mask)):
print >> sio, """
......@@ -973,13 +981,14 @@ class GpuCAReduceCuda(HideC, CAReduce):
return sio.getvalue()
def _k_init(self, node, nodename):
dtype = "npy_" + node.outputs[0].dtype
acc_dtype = "npy_" + self._acc_dtype(node.inputs[0].dtype)
return """
const int threadCount = blockDim.x * blockDim.y * blockDim.z;
const int threadNum = threadIdx.z * blockDim.x * blockDim.y
+ threadIdx.y * blockDim.x + threadIdx.x;
extern __shared__ %(dtype)s buf[];
%(dtype)s myresult = 0.0f;
extern __shared__ %(acc_dtype)s buf[];
%(acc_dtype)s myresult = 0.0f;
//This is caught in cuda/init.py when we init the gpu. I keep
//it here to ease finding code that rely on this.
......@@ -1019,11 +1028,11 @@ class GpuCAReduceCuda(HideC, CAReduce):
result to left."""
x, = node.inputs
in_dtype = x.dtype
out_dtype = node.outputs[0].dtype
dtype = x.dtype
dummy_left = Scalar(dtype=dtype)()
dummy_right = Scalar(dtype=dtype)()
dummy_left = Scalar(dtype=out_dtype)()
dummy_right = Scalar(dtype=in_dtype)()
dummy_node = self.scalar_op.make_node(dummy_left, dummy_right)
......@@ -1040,7 +1049,9 @@ class GpuCAReduceCuda(HideC, CAReduce):
node, name, sub: these should be passed through from the original
call to c_code
"""
dtype = "npy_" + node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
acc_dtype = "npy_" + self._acc_dtype(node.inputs[0].dtype)
# This code (the code in new_version) is currently ignored.
# Code produced later in this function is returned instead.
......@@ -1073,7 +1084,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
if (threadNum < halfPoint)
{
// Get the shared value stored by another thread
%(dtype)s temp = buf[threadNum + halfPoint];
%(acc_dtype)s temp = buf[threadNum + halfPoint];
"""
new_version += self._assign_reduce(node, name,
......@@ -1180,9 +1191,10 @@ class GpuCAReduceCuda(HideC, CAReduce):
is for the case where we are reducing on all axes and x is
C contiguous.
"""
dtype = "npy_" + node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
if getattr(self.scalar_op, 'identity', None) == 0:
zero_shp = "cudaMemset((%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset), 0, PyGpuArray_SIZE(%(z)s) * sizeof(%(dtype)s))" % locals()
zero_shp = "cudaMemset((%(out_dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset), 0, PyGpuArray_SIZE(%(z)s) * sizeof(%(out_dtype)s))" % locals()
#TODO: elif getattr(self.scalar_op, 'identity', None) == 1:
else:
zero_shp = """
......@@ -1191,7 +1203,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
%(fail)s;
""" % locals()
dtype = "npy_" + node.outputs[0].dtype
acc_dtype = "npy_" + self._acc_dtype(node.inputs[0].dtype)
sync = ""
if config.gpuarray.sync:
sync = """GpuArray_sync(&%(z)s->ga);""" % locals()
......@@ -1209,11 +1221,11 @@ class GpuCAReduceCuda(HideC, CAReduce):
" n_threads.x=%%d, size=%%d, ndim=%%d\\n",
n_threads.x,PyGpuArray_SIZE(%(x)s),
PyGpuArray_NDIM(%(x)s));
int n_shared = sizeof(%(dtype)s) * n_threads.x;
int n_shared = sizeof(%(acc_dtype)s) * n_threads.x;
kernel_reduce_ccontig_%(name)s<<<n_blocks, n_threads, n_shared>>>(
PyGpuArray_SIZE(%(x)s),
(%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
(%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset));
(%(in_dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
(%(out_dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset));
%(sync)s
cudaError_t sts = cudaGetLastError();
if (cudaSuccess != sts)
......@@ -1272,13 +1284,14 @@ class GpuCAReduceCuda(HideC, CAReduce):
"""
assert N in [1, 2, 3]
dtype = "npy_" + node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
makecall = self._makecall(node, name, x, z, fail)
N_pattern = ''.join(['1'] * N)
param_dim = ",".join(["PyGpuArray_DIMS(%s)[%d]" % (x, i)
for i in xrange(N + 1)])
strides_dim = ",".join(["PyGpuArray_STRIDES(%s)[%d]/sizeof(%s)"
% (x, i, dtype) for i in xrange(N + 1)])
% (x, i, in_dtype) for i in xrange(N + 1)])
threads_y = """
//get as many y threads as we can fit
......@@ -1334,7 +1347,9 @@ class GpuCAReduceCuda(HideC, CAReduce):
self.c_code_reduce_01X(sio, node, name, x, z, fail, 3)
def c_code_reduce_10(self, sio, node, name, x, z, fail):
dtype = "npy_" + node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
acc_dtype = "npy_" + self._acc_dtype(node.inputs[0].dtype)
sync = ""
if config.gpuarray.sync:
sync = """GpuArray_sync(&%(z)s->ga);""" % locals()
......@@ -1354,18 +1369,18 @@ class GpuCAReduceCuda(HideC, CAReduce):
n_blocks.y);
}
assert( PyGpuArray_DIMS(%(x)s)[1] == PyGpuArray_DIMS(%(z)s)[0]);
int n_shared = sizeof(%(dtype)s) * n_threads.x;
int n_shared = sizeof(%(acc_dtype)s) * n_threads.x;
kernel_reduce_010_%(name)s<<<n_blocks, n_threads, n_shared>>>(
1,
PyGpuArray_DIMS(%(x)s)[0],
PyGpuArray_DIMS(%(x)s)[1],
(%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
(%(in_dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
1,
PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(dtype)s),
(%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset),
PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(in_dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(in_dtype)s),
(%(out_dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset),
1,
PyGpuArray_STRIDES(%(z)s)[0]/sizeof(%(dtype)s)
PyGpuArray_STRIDES(%(z)s)[0]/sizeof(%(out_dtype)s)
);
%(sync)s
cudaError_t sts = cudaGetLastError();
......@@ -1391,7 +1406,8 @@ class GpuCAReduceCuda(HideC, CAReduce):
makecall_inner = self._makecall(node, name, x, z, fail,
pattern="010_inner")
pattern = ''.join(str(i) for i in self.reduce_mask)
dtype = "npy_" + node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
sync = ""
if config.gpuarray.sync:
sync = """GpuArray_sync(&%(z)s->ga);""" % locals()
......@@ -1431,13 +1447,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
int n_shared = 0;
kernel_reduce_010_AD_%(name)s<<<n_blocks, n_threads, n_shared>>>(
A,B,C,D,
(%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[2]/sizeof(%(dtype)s),
(%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset),
PyGpuArray_STRIDES(%(z)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(z)s)[1]/sizeof(%(dtype)s)
(%(in_dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(in_dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(in_dtype)s),
PyGpuArray_STRIDES(%(x)s)[2]/sizeof(%(in_dtype)s),
(%(out_dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset),
PyGpuArray_STRIDES(%(z)s)[0]/sizeof(%(out_dtype)s),
PyGpuArray_STRIDES(%(z)s)[1]/sizeof(%(out_dtype)s)
);
%(sync)s
cudaError_t sts = cudaGetLastError();
......@@ -1474,10 +1490,10 @@ class GpuCAReduceCuda(HideC, CAReduce):
(size_t)n_threads.x),
(size_t)(4096 / n_blocks.x)
);
if(std::min(std::min(PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(dtype)s)),
PyGpuArray_STRIDES(%(x)s)[2]/sizeof(%(dtype)s))
==PyGpuArray_STRIDES(%(x)s)[2]/sizeof(%(dtype)s)
if(std::min(std::min(PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(in_dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(in_dtype)s)),
PyGpuArray_STRIDES(%(x)s)[2]/sizeof(%(in_dtype)s))
==PyGpuArray_STRIDES(%(x)s)[2]/sizeof(%(in_dtype)s)
&& n_blocks.y==ceil_intdiv(PyGpuArray_DIMS(%(x)s)[2],
(size_t)n_threads.x)){
if(verbose>1)
......@@ -1633,7 +1649,9 @@ class GpuCAReduceCuda(HideC, CAReduce):
def c_code_reduce_0011(self, sio, node, name, x, z, fail):
makecall = self._makecall(node, name, x, z, fail)
dtype = "npy_" + node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
acc_dtype = "npy_" + self._acc_dtype(node.inputs[0].dtype)
print >> sio, """
{
int verbose = 0;
......@@ -1653,7 +1671,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
(size_t) 256));
while (n_threads.x * n_threads.y <= 256
&& n_threads.y < PyGpuArray_DIMS(%(x)s)[2]
&& n_threads.x * n_threads.y * sizeof(%(dtype)s) <=(15*1024-200))
&& n_threads.x * n_threads.y * sizeof(%(acc_dtype)s) <=(15*1024-200))
{
n_threads.y += 1;
}
......@@ -1722,7 +1740,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
""" % locals()
def c_code_cache_version_apply(self, node):
version = [10] # the version corresponding to the c code in this Op
version = [11] # the version corresponding to the c code in this Op
# now we insert versions for the ops on which we depend...
scalar_node = Apply(self.scalar_op,
......@@ -1739,7 +1757,10 @@ class GpuCAReduceCuda(HideC, CAReduce):
def c_support_code_apply(self, node, nodename):
sio = StringIO()
nd_in = len(self.reduce_mask)
dtype = "npy_" + node.outputs[0].dtype
in_dtype = "npy_" + node.inputs[0].dtype
out_dtype = "npy_" + node.outputs[0].dtype
acc_dtype = "npy_" + self._acc_dtype(node.inputs[0].dtype)
if all(i == 1 for i in self.reduce_mask):
#this kernel is ok for up to a few thousand elements, but
# it only runs on ONE multiprocessor
......@@ -1751,13 +1772,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
print >> sio, """
static __global__ void kernel_reduce_ccontig_%(nodename)s(
const unsigned int d0,
const %(dtype)s *A,
%(dtype)s * Z)
const %(in_dtype)s *A,
%(out_dtype)s * Z)
{
const int threadCount = blockDim.x;
const int threadNum = threadIdx.x;
extern __shared__ %(dtype)s buf[];
%(dtype)s myresult = %(reduce_init)s;
extern __shared__ %(acc_dtype)s buf[];
%(acc_dtype)s myresult = %(reduce_init)s;
if (warpSize != 32)
{
......@@ -1782,13 +1803,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
print >> sio, """
static __global__ void kernel_reduce_1_%(nodename)s(
const unsigned int d0,
const %(dtype)s *A, const int sA0,
%(dtype)s * Z)
const %(in_dtype)s *A, const int sA0,
%(out_dtype)s * Z)
{
const int threadCount = blockDim.x;
const int threadNum = threadIdx.x;
extern __shared__ %(dtype)s buf[];
%(dtype)s myresult = %(reduce_init)s;
extern __shared__ %(acc_dtype)s buf[];
%(acc_dtype)s myresult = %(reduce_init)s;
if (warpSize != 32)
{
......@@ -1815,13 +1836,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
static __global__ void kernel_reduce_11_%(nodename)s(
const int d0,
const int d1,
const %(dtype)s *A, const int sA0, const int sA1,
%(dtype)s * Z)
const %(in_dtype)s *A, const int sA0, const int sA1,
%(out_dtype)s * Z)
{
const int threadCount = blockDim.x * blockDim.y;
const int threadNum = threadIdx.y*blockDim.x + threadIdx.x;
extern __shared__ %(dtype)s buf[];
%(dtype)s myresult = %(reduce_init)s;
extern __shared__ %(acc_dtype)s buf[];
%(acc_dtype)s myresult = %(reduce_init)s;
if (warpSize != 32)
{
......@@ -1927,13 +1948,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d0,
const int d1,
const int d2,
const %(dtype)s *A, const int sA0,
const %(in_dtype)s *A, const int sA0,
const int sA1, const int sA2,
%(dtype)s * Z, const int sZ0, const int sZ1)
%(out_dtype)s * Z, const int sZ0, const int sZ1)
{
const int threadCount = blockDim.x;
const int threadNum = threadIdx.x;
extern __shared__ %(dtype)s buf[];
extern __shared__ %(acc_dtype)s buf[];
if (warpSize != 32)
{
......@@ -1945,7 +1966,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
{
for (int i2 = blockIdx.y; i2 < d2; i2 += gridDim.y)
{
%(dtype)s myresult = %(reduce_init)s;
%(acc_dtype)s myresult = %(reduce_init)s;
for (int i1 = threadIdx.x; i1 < d1; i1 += blockDim.x)
{
%(reduce_fct)s;
......@@ -1968,13 +1989,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int C,
const int D,
//const int E, // THIS is 32
const %(dtype)s *X, const int sX0,
const %(in_dtype)s *X, const int sX0,
const int sX1, const int sX2,
%(dtype)s * Z, const int sZ0, const int sZ1)
%(out_dtype)s * Z, const int sZ0, const int sZ1)
{
const int threadCount = blockDim.x;
const int threadNum = threadIdx.x;
%(dtype)s myresult = 0.0f;
%(acc_dtype)s myresult = 0.0f;
if (warpSize != 32)
{
......@@ -2062,14 +2083,14 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d0,
const int d1,
const int d2,
const %(dtype)s *A, const int sA0,
const %(in_dtype)s *A, const int sA0,
const int sA1, const int sA2,
%(dtype)s * Z, const int sZ0)
%(out_dtype)s * Z, const int sZ0)
{
const int threadCount = blockDim.x * blockDim.y;
const int threadNum = threadIdx.y * blockDim.x + threadIdx.x;
extern __shared__ %(dtype)s buf[];
%(dtype)s myresult = %(reduce_init)s;
extern __shared__ %(acc_dtype)s buf[];
%(acc_dtype)s myresult = %(reduce_init)s;
if (warpSize != 32)
{
......@@ -2157,13 +2178,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d0,
const int d1,
const int d2,
const %(dtype)s *A, const int sA0,
const %(in_dtype)s *A, const int sA0,
const int sA1, const int sA2,
%(dtype)s * Z, const int sZ0, const int sZ1)
%(out_dtype)s * Z, const int sZ0, const int sZ1)
{
const int threadCount = blockDim.x;
const int threadNum = threadIdx.x;
extern __shared__ %(dtype)s buf[];
extern __shared__ %(acc_dtype)s buf[];
if (warpSize != 32)
{
......@@ -2174,7 +2195,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
{
for (int i1 = blockIdx.y; i1 < d1; i1 += gridDim.y)
{
%(dtype)s myresult = %(reduce_init)s;
%(acc_dtype)s myresult = %(reduce_init)s;
for (int i2 = threadIdx.x; i2 < d2; i2 += blockDim.x)
{
%(reduce_fct)s;
......@@ -2204,7 +2225,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
{
for (int i1 = blockIdx.y; i1 < d1; i1 += gridDim.y)
{
%(dtype)s myresult = %(reduce_init)s;
%(acc_dtype)s myresult = %(reduce_init)s;
for (int i2 = threadIdx.y; i2 < d2; i2 += blockDim.y)
{
for (int i3 = threadIdx.x; i3 < d3; i3 += blockDim.x)
......@@ -2237,7 +2258,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
{
for (int i2 = blockIdx.y; i2 < d2; i2 += gridDim.y)
{
%(dtype)s myresult = %(reduce_init)s;
%(acc_dtype)s myresult = %(reduce_init)s;
for (int i1 = threadIdx.y; i1 < d1; i1 += blockDim.y)
{
for (int i3 = threadIdx.x; i3 < d3; i3 += blockDim.x)
......@@ -2291,14 +2312,14 @@ class GpuCAReduceCuda(HideC, CAReduce):
const unsigned int d1,
const unsigned int d2,
const unsigned int d3,
const %(dtype)s *A, const int sA0, const int sA1,
const %(in_dtype)s *A, const int sA0, const int sA1,
const int sA2, const int sA3,
%(dtype)s * Z, const int sZ0)
%(out_dtype)s * Z, const int sZ0)
{
const int threadCount = blockDim.x * blockDim.y * blockDim.z;
const int threadNum = threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
extern __shared__ %(dtype)s buf[];
%(dtype)s myresult = %(reduce_init)s;
extern __shared__ %(acc_dtype)s buf[];
%(acc_dtype)s myresult = %(reduce_init)s;
if (warpSize != 32)
{
......
theano/sandbox/gpuarray/opt.py
浏览文件 @ 6df1cadc
......@@ -311,9 +311,10 @@ def local_gpua_careduce(node):
if isinstance(node.op.scalar_op, (scalar.Add, scalar.Mul,
scalar.Maximum, scalar.Minimum)):
x, = node.inputs
greduce = GpuCAReduceCuda(node.op.scalar_op, axis=node.op.axis)
if x.dtype != "float32":
return
greduce = GpuCAReduceCuda(
node.op.scalar_op, axis=node.op.axis,
dtype=getattr(node.op, 'dtype', None),
acc_dtype=getattr(node.op, 'acc_dtype', None))
gvar = greduce(x)
#We need to have the make node called, otherwise the mask can
#be None
......
theano/sandbox/gpuarray/tests/test_elemwise.py
浏览文件 @ 6df1cadc
......@@ -68,9 +68,10 @@ class test_GpuCAReduceCPY(test_CAReduce):
class test_GpuCAReduceCuda(test_GpuCAReduceCPY):
dtypes = ["float32"]
dtypes = ["float32", "int64"]
dtypes = []
bin_dtypes = ["uint8", "int8"]
bin_dtypes = []
cases = [((5, 6), None),
((5, 6), (0, 1)),
((5, 6), (0, )),
......
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