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pytensor
提交 3943db3c authored 作者: Frederic's avatar Frederic
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Make GpuCAReduce support more dtype, but the acc_dtype is the same as input/outputs.

上级 26d91309
隐藏空白字符变更
内嵌 并排
正在显示 包含 92 行增加 和 81 行删除
theano/sandbox/gpuarray/elemwise.py
浏览文件 @ 3943db3c
...@@ -607,7 +607,6 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -607,7 +607,6 @@ class GpuCAReduceCuda(HideC, CAReduce):
def make_node(self, x): def make_node(self, x):
x = as_gpuarray_variable(x) x = as_gpuarray_variable(x)
assert x.dtype == "float32"
ret = super(GpuCAReduceCuda, self).make_node(x) ret = super(GpuCAReduceCuda, self).make_node(x)
self = copy.copy(self) self = copy.copy(self)
self.axis = ret.op.axis self.axis = ret.op.axis
...@@ -693,7 +692,8 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -693,7 +692,8 @@ class GpuCAReduceCuda(HideC, CAReduce):
nd_in = node.inputs[0].type.ndim nd_in = node.inputs[0].type.ndim
nd_out = node.outputs[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
assert nd_in - nd_out == sum(self.reduce_mask) assert nd_in - nd_out == sum(self.reduce_mask)
sio = StringIO() sio = StringIO()
...@@ -775,7 +775,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -775,7 +775,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
# \begin bracket the reduction in a check that there is # \begin bracket the reduction in a check that there is
# actually work to do # actually work to do
if getattr(self.scalar_op, 'identity', None) == 0: if getattr(self.scalar_op, 'identity', None) == 0:
zero_shp = "cudaMemset((float *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset), 0, PyGpuArray_SIZE(%(z)s) * sizeof(float))" % locals() 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()
#TODO: elif getattr(self.scalar_op, 'identity', None) == 1: #TODO: elif getattr(self.scalar_op, 'identity', None) == 1:
else: else:
scalar_op = self.scalar_op scalar_op = self.scalar_op
...@@ -827,16 +827,16 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -827,16 +827,16 @@ class GpuCAReduceCuda(HideC, CAReduce):
if (verbose) if (verbose)
printf("running kernel_reduce_10_%(name)s\\n"); printf("running kernel_reduce_10_%(name)s\\n");
int n_shared = sizeof(float) * n_threads.x * n_threads.y * n_threads.z; int n_shared = sizeof(%(dtype)s) * n_threads.x * n_threads.y * n_threads.z;
kernel_reduce_10_%(name)s<<<n_blocks, n_threads, kernel_reduce_10_%(name)s<<<n_blocks, n_threads,
n_shared>>>( n_shared>>>(
PyGpuArray_DIMS(%(x)s)[0], PyGpuArray_DIMS(%(x)s)[0],
PyGpuArray_DIMS(%(x)s)[1], PyGpuArray_DIMS(%(x)s)[1],
(float *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset), (%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
PyGpuArray_STRIDES(%(x)s)[0]/4, PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/4, PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(dtype)s),
(float *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset), (%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset),
PyGpuArray_STRIDES(%(z)s)[0]/4 PyGpuArray_STRIDES(%(z)s)[0]/sizeof(%(dtype)s)
); );
[ [
if config.gpuarray.sync: if config.gpuarray.sync:
...@@ -848,6 +848,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -848,6 +848,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
%(fail)s; %(fail)s;
} }
""" """
dtype = "npy_" + node.outputs[0].dtype
sio = StringIO() sio = StringIO()
if pattern is None: if pattern is None:
pattern = ''.join(str(c) for c in self.reduce_mask) pattern = ''.join(str(c) for c in self.reduce_mask)
...@@ -860,7 +861,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -860,7 +861,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
print >> sio, """ print >> sio, """
if (verbose) if (verbose)
printf("running kernel_reduce_%(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; int n_shared = sizeof(%(dtype)s) * n_threads.x * n_threads.y * n_threads.z;
if (verbose>1) if (verbose>1)
printf("n_threads.x=%%d, n_threads.y=%%d, n_threads.z=%%d," printf("n_threads.x=%%d, n_threads.y=%%d, n_threads.z=%%d,"
" nb_threads=%%d, n_blocks.x=%%d, n_blocks.y=%%d," " nb_threads=%%d, n_blocks.x=%%d, n_blocks.y=%%d,"
...@@ -876,18 +877,18 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -876,18 +877,18 @@ class GpuCAReduceCuda(HideC, CAReduce):
PyGpuArray_DIMS(%(x)s)[%(i)s], PyGpuArray_DIMS(%(x)s)[%(i)s],
""" % locals() """ % locals()
print >> sio, """ print >> sio, """
(float *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset) (%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset)
""" % locals() """ % locals()
for i in xrange(ndim): for i in xrange(ndim):
print >> sio, """ print >> sio, """
,PyGpuArray_STRIDES(%(x)s)[%(i)s]/4 ,PyGpuArray_STRIDES(%(x)s)[%(i)s]/sizeof(%(dtype)s)
""" % locals() """ % locals()
print >> sio, """ print >> sio, """
,(float *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset) ,(%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset)
""" % locals() """ % locals()
for i in xrange(nd_out): for i in xrange(nd_out):
print >> sio, """ print >> sio, """
,PyGpuArray_STRIDES(%(z)s)[%(i)s]/4 ,PyGpuArray_STRIDES(%(z)s)[%(i)s]/sizeof(%(dtype)s)
""" % locals() """ % locals()
sync = "" sync = ""
if config.gpuarray.sync: if config.gpuarray.sync:
...@@ -927,17 +928,18 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -927,17 +928,18 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d0, const int d0,
const int d1, const int d1,
const int d2, const int d2,
const float *A, const %(dtype)s *A,
const int sA0, const int sA0,
const int sA1, const int sA1,
const int sA2, const int sA2,
float * Z, %(dtype)s * Z,
const int sZ0) const int sZ0)
Since the nodename is unique, we don't need to put the name Since the nodename is unique, we don't need to put the name
of the scalar_op in here. of the scalar_op in here.
""" """
dtype = "npy_" + node.outputs[0].dtype
if reduce_mask is None: if reduce_mask is None:
reduce_mask = self.reduce_mask reduce_mask = self.reduce_mask
if ndim is None: if ndim is None:
...@@ -954,14 +956,14 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -954,14 +956,14 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d%(i)s, const int d%(i)s,
""" % locals() """ % locals()
print >> sio, """ print >> sio, """
const float *A, const %(dtype)s *A,
""" % locals() """ % locals()
for i in xrange(ndim): for i in xrange(ndim):
print >> sio, """ print >> sio, """
const int sA%(i)s, const int sA%(i)s,
""" % locals() """ % locals()
print >> sio, """ print >> sio, """
float * Z %(dtype)s * Z
""" % locals() """ % locals()
for i in xrange(ndim - sum(reduce_mask)): for i in xrange(ndim - sum(reduce_mask)):
print >> sio, """ print >> sio, """
...@@ -970,13 +972,14 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -970,13 +972,14 @@ class GpuCAReduceCuda(HideC, CAReduce):
print >> sio, ")" print >> sio, ")"
return sio.getvalue() return sio.getvalue()
def _k_init(self, *args): def _k_init(self, node, nodename):
dtype = "npy_" + node.outputs[0].dtype
return """ return """
const int threadCount = blockDim.x * blockDim.y * blockDim.z; const int threadCount = blockDim.x * blockDim.y * blockDim.z;
const int threadNum = threadIdx.z * blockDim.x * blockDim.y const int threadNum = threadIdx.z * blockDim.x * blockDim.y
+ threadIdx.y * blockDim.x + threadIdx.x; + threadIdx.y * blockDim.x + threadIdx.x;
extern __shared__ float buf[]; extern __shared__ %(dtype)s buf[];
float myresult = 0.0f; %(dtype)s myresult = 0.0f;
//This is caught in cuda/init.py when we init the gpu. I keep //This is caught in cuda/init.py when we init the gpu. I keep
//it here to ease finding code that rely on this. //it here to ease finding code that rely on this.
...@@ -986,7 +989,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -986,7 +989,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
return; return;
} }
""" """ % locals()
def _assign_init(self, first_item): def _assign_init(self, first_item):
""" """
...@@ -1037,6 +1040,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1037,6 +1040,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
node, name, sub: these should be passed through from the original node, name, sub: these should be passed through from the original
call to c_code call to c_code
""" """
dtype = "npy_" + node.outputs[0].dtype
# This code (the code in new_version) is currently ignored. # This code (the code in new_version) is currently ignored.
# Code produced later in this function is returned instead. # Code produced later in this function is returned instead.
...@@ -1068,7 +1072,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1068,7 +1072,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
if (threadNum < halfPoint) if (threadNum < halfPoint)
{ {
// Get the shared value stored by another thread // Get the shared value stored by another thread
float temp = buf[threadNum + halfPoint]; %(dtype)s temp = buf[threadNum + halfPoint];
""" """
new_version += self._assign_reduce(node, name, new_version += self._assign_reduce(node, name,
...@@ -1175,8 +1179,9 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1175,8 +1179,9 @@ class GpuCAReduceCuda(HideC, CAReduce):
is for the case where we are reducing on all axes and x is is for the case where we are reducing on all axes and x is
C contiguous. C contiguous.
""" """
dtype = "npy_" + node.outputs[0].dtype
if getattr(self.scalar_op, 'identity', None) == 0: if getattr(self.scalar_op, 'identity', None) == 0:
zero_shp = "cudaMemset((float *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset), 0, PyGpuArray_SIZE(%(z)s) * sizeof(float))" % locals() 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()
#TODO: elif getattr(self.scalar_op, 'identity', None) == 1: #TODO: elif getattr(self.scalar_op, 'identity', None) == 1:
else: else:
zero_shp = """ zero_shp = """
...@@ -1185,6 +1190,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1185,6 +1190,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
%(fail)s; %(fail)s;
""" % locals() """ % locals()
dtype = "npy_" + node.outputs[0].dtype
sync = "" sync = ""
if config.gpuarray.sync: if config.gpuarray.sync:
sync = """GpuArray_sync(&%(z)s->ga);""" % locals() sync = """GpuArray_sync(&%(z)s->ga);""" % locals()
...@@ -1202,11 +1208,11 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1202,11 +1208,11 @@ class GpuCAReduceCuda(HideC, CAReduce):
" n_threads.x=%%d, size=%%d, ndim=%%d\\n", " n_threads.x=%%d, size=%%d, ndim=%%d\\n",
n_threads.x,PyGpuArray_SIZE(%(x)s), n_threads.x,PyGpuArray_SIZE(%(x)s),
PyGpuArray_NDIM(%(x)s)); PyGpuArray_NDIM(%(x)s));
int n_shared = sizeof(float) * n_threads.x; int n_shared = sizeof(%(dtype)s) * n_threads.x;
kernel_reduce_ccontig_%(name)s<<<n_blocks, n_threads, n_shared>>>( kernel_reduce_ccontig_%(name)s<<<n_blocks, n_threads, n_shared>>>(
PyGpuArray_SIZE(%(x)s), PyGpuArray_SIZE(%(x)s),
(float *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset), (%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
(float *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset)); (%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset));
%(sync)s %(sync)s
cudaError_t sts = cudaGetLastError(); cudaError_t sts = cudaGetLastError();
if (cudaSuccess != sts) if (cudaSuccess != sts)
...@@ -1265,12 +1271,13 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1265,12 +1271,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
""" """
assert N in [1, 2, 3] assert N in [1, 2, 3]
dtype = "npy_" + node.outputs[0].dtype
makecall = self._makecall(node, name, x, z, fail) makecall = self._makecall(node, name, x, z, fail)
N_pattern = ''.join(['1'] * N) N_pattern = ''.join(['1'] * N)
param_dim = ",".join(["PyGpuArray_DIMS(%s)[%d]" % (x, i) param_dim = ",".join(["PyGpuArray_DIMS(%s)[%d]" % (x, i)
for i in xrange(N + 1)]) for i in xrange(N + 1)])
strides_dim = ",".join(["PyGpuArray_STRIDES(%s)[%d]/4" strides_dim = ",".join(["PyGpuArray_STRIDES(%s)[%d]/sizeof(%s)"
% (x, i) for i in xrange(N + 1)]) % (x, i, dtype) for i in xrange(N + 1)])
threads_y = """ threads_y = """
//get as many y threads as we can fit //get as many y threads as we can fit
...@@ -1326,6 +1333,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1326,6 +1333,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
self.c_code_reduce_01X(sio, node, name, x, z, fail, 3) self.c_code_reduce_01X(sio, node, name, x, z, fail, 3)
def c_code_reduce_10(self, sio, node, name, x, z, fail): def c_code_reduce_10(self, sio, node, name, x, z, fail):
dtype = "npy_" + node.outputs[0].dtype
sync = "" sync = ""
if config.gpuarray.sync: if config.gpuarray.sync:
sync = """GpuArray_sync(&%(z)s->ga);""" % locals() sync = """GpuArray_sync(&%(z)s->ga);""" % locals()
...@@ -1345,18 +1353,18 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1345,18 +1353,18 @@ class GpuCAReduceCuda(HideC, CAReduce):
n_blocks.y); n_blocks.y);
} }
assert( PyGpuArray_DIMS(%(x)s)[1] == PyGpuArray_DIMS(%(z)s)[0]); assert( PyGpuArray_DIMS(%(x)s)[1] == PyGpuArray_DIMS(%(z)s)[0]);
int n_shared = sizeof(float) * n_threads.x; int n_shared = sizeof(%(dtype)s) * n_threads.x;
kernel_reduce_010_%(name)s<<<n_blocks, n_threads, n_shared>>>( kernel_reduce_010_%(name)s<<<n_blocks, n_threads, n_shared>>>(
1, 1,
PyGpuArray_DIMS(%(x)s)[0], PyGpuArray_DIMS(%(x)s)[0],
PyGpuArray_DIMS(%(x)s)[1], PyGpuArray_DIMS(%(x)s)[1],
(float *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset), (%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
1, 1,
PyGpuArray_STRIDES(%(x)s)[0]/4, PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/4, PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(dtype)s),
(float *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset), (%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset),
1, 1,
PyGpuArray_STRIDES(%(z)s)[0]/4 PyGpuArray_STRIDES(%(z)s)[0]/sizeof(%(dtype)s)
); );
%(sync)s %(sync)s
cudaError_t sts = cudaGetLastError(); cudaError_t sts = cudaGetLastError();
...@@ -1382,6 +1390,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1382,6 +1390,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
makecall_inner = self._makecall(node, name, x, z, fail, makecall_inner = self._makecall(node, name, x, z, fail,
pattern="010_inner") pattern="010_inner")
pattern = ''.join(str(i) for i in self.reduce_mask) pattern = ''.join(str(i) for i in self.reduce_mask)
dtype = "npy_" + node.outputs[0].dtype
sync = "" sync = ""
if config.gpuarray.sync: if config.gpuarray.sync:
sync = """GpuArray_sync(&%(z)s->ga);""" % locals() sync = """GpuArray_sync(&%(z)s->ga);""" % locals()
...@@ -1421,13 +1430,13 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1421,13 +1430,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
int n_shared = 0; int n_shared = 0;
kernel_reduce_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, A,B,C,D,
(float *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset), (%(dtype)s *)(((char *)cuda_get_ptr(%(x)s->ga.data))+%(x)s->ga.offset),
PyGpuArray_STRIDES(%(x)s)[0]/4, PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/4, PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[2]/4, PyGpuArray_STRIDES(%(x)s)[2]/sizeof(%(dtype)s),
(float *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset), (%(dtype)s *)(((char *)cuda_get_ptr(%(z)s->ga.data))+%(z)s->ga.offset),
PyGpuArray_STRIDES(%(z)s)[0]/4, PyGpuArray_STRIDES(%(z)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(z)s)[1]/4 PyGpuArray_STRIDES(%(z)s)[1]/sizeof(%(dtype)s)
); );
%(sync)s %(sync)s
cudaError_t sts = cudaGetLastError(); cudaError_t sts = cudaGetLastError();
...@@ -1464,10 +1473,10 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1464,10 +1473,10 @@ class GpuCAReduceCuda(HideC, CAReduce):
(size_t)n_threads.x), (size_t)n_threads.x),
(size_t)(4096 / n_blocks.x) (size_t)(4096 / n_blocks.x)
); );
if(std::min(std::min(PyGpuArray_STRIDES(%(x)s)[0]/4, if(std::min(std::min(PyGpuArray_STRIDES(%(x)s)[0]/sizeof(%(dtype)s),
PyGpuArray_STRIDES(%(x)s)[1]/4), PyGpuArray_STRIDES(%(x)s)[1]/sizeof(%(dtype)s)),
PyGpuArray_STRIDES(%(x)s)[2]/4) PyGpuArray_STRIDES(%(x)s)[2]/sizeof(%(dtype)s))
==PyGpuArray_STRIDES(%(x)s)[2]/4 ==PyGpuArray_STRIDES(%(x)s)[2]/sizeof(%(dtype)s)
&& n_blocks.y==ceil_intdiv(PyGpuArray_DIMS(%(x)s)[2], && n_blocks.y==ceil_intdiv(PyGpuArray_DIMS(%(x)s)[2],
(size_t)n_threads.x)){ (size_t)n_threads.x)){
if(verbose>1) if(verbose>1)
...@@ -1623,6 +1632,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1623,6 +1632,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
def c_code_reduce_0011(self, sio, node, name, x, z, fail): def c_code_reduce_0011(self, sio, node, name, x, z, fail):
makecall = self._makecall(node, name, x, z, fail) makecall = self._makecall(node, name, x, z, fail)
dtype = "npy_" + node.outputs[0].dtype
print >> sio, """ print >> sio, """
{ {
int verbose = 0; int verbose = 0;
...@@ -1642,7 +1652,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1642,7 +1652,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
(size_t) 256)); (size_t) 256));
while (n_threads.x * n_threads.y <= 256 while (n_threads.x * n_threads.y <= 256
&& n_threads.y < PyGpuArray_DIMS(%(x)s)[2] && n_threads.y < PyGpuArray_DIMS(%(x)s)[2]
&& n_threads.x * n_threads.y * sizeof(float) <=(15*1024-200)) && n_threads.x * n_threads.y * sizeof(%(dtype)s) <=(15*1024-200))
{ {
n_threads.y += 1; n_threads.y += 1;
} }
...@@ -1711,7 +1721,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1711,7 +1721,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
""" % locals() """ % locals()
def c_code_cache_version_apply(self, node): def c_code_cache_version_apply(self, node):
version = [9] # the version corresponding to the c code in this Op version = [10] # the version corresponding to the c code in this Op
# now we insert versions for the ops on which we depend... # now we insert versions for the ops on which we depend...
scalar_node = Apply(self.scalar_op, scalar_node = Apply(self.scalar_op,
...@@ -1728,6 +1738,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1728,6 +1738,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
def c_support_code_apply(self, node, nodename): def c_support_code_apply(self, node, nodename):
sio = StringIO() sio = StringIO()
nd_in = len(self.reduce_mask) nd_in = len(self.reduce_mask)
dtype = "npy_" + node.outputs[0].dtype
if all(i == 1 for i in self.reduce_mask): if all(i == 1 for i in self.reduce_mask):
#this kernel is ok for up to a few thousand elements, but #this kernel is ok for up to a few thousand elements, but
# it only runs on ONE multiprocessor # it only runs on ONE multiprocessor
...@@ -1739,13 +1750,13 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1739,13 +1750,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
print >> sio, """ print >> sio, """
static __global__ void kernel_reduce_ccontig_%(nodename)s( static __global__ void kernel_reduce_ccontig_%(nodename)s(
const unsigned int d0, const unsigned int d0,
const float *A, const %(dtype)s *A,
float * Z) %(dtype)s * Z)
{ {
const int threadCount = blockDim.x; const int threadCount = blockDim.x;
const int threadNum = threadIdx.x; const int threadNum = threadIdx.x;
extern __shared__ float buf[]; extern __shared__ %(dtype)s buf[];
float myresult = %(reduce_init)s; %(dtype)s myresult = %(reduce_init)s;
if (warpSize != 32) if (warpSize != 32)
{ {
...@@ -1770,13 +1781,13 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1770,13 +1781,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
print >> sio, """ print >> sio, """
static __global__ void kernel_reduce_1_%(nodename)s( static __global__ void kernel_reduce_1_%(nodename)s(
const unsigned int d0, const unsigned int d0,
const float *A, const int sA0, const %(dtype)s *A, const int sA0,
float * Z) %(dtype)s * Z)
{ {
const int threadCount = blockDim.x; const int threadCount = blockDim.x;
const int threadNum = threadIdx.x; const int threadNum = threadIdx.x;
extern __shared__ float buf[]; extern __shared__ %(dtype)s buf[];
float myresult = %(reduce_init)s; %(dtype)s myresult = %(reduce_init)s;
if (warpSize != 32) if (warpSize != 32)
{ {
...@@ -1803,13 +1814,13 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1803,13 +1814,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
static __global__ void kernel_reduce_11_%(nodename)s( static __global__ void kernel_reduce_11_%(nodename)s(
const int d0, const int d0,
const int d1, const int d1,
const float *A, const int sA0, const int sA1, const %(dtype)s *A, const int sA0, const int sA1,
float * Z) %(dtype)s * Z)
{ {
const int threadCount = blockDim.x * blockDim.y; const int threadCount = blockDim.x * blockDim.y;
const int threadNum = threadIdx.y*blockDim.x + threadIdx.x; const int threadNum = threadIdx.y*blockDim.x + threadIdx.x;
extern __shared__ float buf[]; extern __shared__ %(dtype)s buf[];
float myresult = %(reduce_init)s; %(dtype)s myresult = %(reduce_init)s;
if (warpSize != 32) if (warpSize != 32)
{ {
...@@ -1915,13 +1926,13 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1915,13 +1926,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d0, const int d0,
const int d1, const int d1,
const int d2, const int d2,
const float *A, const int sA0, const %(dtype)s *A, const int sA0,
const int sA1, const int sA2, const int sA1, const int sA2,
float * Z, const int sZ0, const int sZ1) %(dtype)s * Z, const int sZ0, const int sZ1)
{ {
const int threadCount = blockDim.x; const int threadCount = blockDim.x;
const int threadNum = threadIdx.x; const int threadNum = threadIdx.x;
extern __shared__ float buf[]; extern __shared__ %(dtype)s buf[];
if (warpSize != 32) if (warpSize != 32)
{ {
...@@ -1933,7 +1944,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1933,7 +1944,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
{ {
for (int i2 = blockIdx.y; i2 < d2; i2 += gridDim.y) for (int i2 = blockIdx.y; i2 < d2; i2 += gridDim.y)
{ {
float myresult = %(reduce_init)s; %(dtype)s myresult = %(reduce_init)s;
for (int i1 = threadIdx.x; i1 < d1; i1 += blockDim.x) for (int i1 = threadIdx.x; i1 < d1; i1 += blockDim.x)
{ {
%(reduce_fct)s; %(reduce_fct)s;
...@@ -1956,13 +1967,13 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -1956,13 +1967,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int C, const int C,
const int D, const int D,
//const int E, // THIS is 32 //const int E, // THIS is 32
const float *X, const int sX0, const %(dtype)s *X, const int sX0,
const int sX1, const int sX2, const int sX1, const int sX2,
float * Z, const int sZ0, const int sZ1) %(dtype)s * Z, const int sZ0, const int sZ1)
{ {
const int threadCount = blockDim.x; const int threadCount = blockDim.x;
const int threadNum = threadIdx.x; const int threadNum = threadIdx.x;
float myresult = 0.0f; %(dtype)s myresult = 0.0f;
if (warpSize != 32) if (warpSize != 32)
{ {
...@@ -2050,14 +2061,14 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -2050,14 +2061,14 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d0, const int d0,
const int d1, const int d1,
const int d2, const int d2,
const float *A, const int sA0, const %(dtype)s *A, const int sA0,
const int sA1, const int sA2, const int sA1, const int sA2,
float * Z, const int sZ0) %(dtype)s * Z, const int sZ0)
{ {
const int threadCount = blockDim.x * blockDim.y; const int threadCount = blockDim.x * blockDim.y;
const int threadNum = threadIdx.y * blockDim.x + threadIdx.x; const int threadNum = threadIdx.y * blockDim.x + threadIdx.x;
extern __shared__ float buf[]; extern __shared__ %(dtype)s buf[];
float myresult = %(reduce_init)s; %(dtype)s myresult = %(reduce_init)s;
if (warpSize != 32) if (warpSize != 32)
{ {
...@@ -2145,13 +2156,13 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -2145,13 +2156,13 @@ class GpuCAReduceCuda(HideC, CAReduce):
const int d0, const int d0,
const int d1, const int d1,
const int d2, const int d2,
const float *A, const int sA0, const %(dtype)s *A, const int sA0,
const int sA1, const int sA2, const int sA1, const int sA2,
float * Z, const int sZ0, const int sZ1) %(dtype)s * Z, const int sZ0, const int sZ1)
{ {
const int threadCount = blockDim.x; const int threadCount = blockDim.x;
const int threadNum = threadIdx.x; const int threadNum = threadIdx.x;
extern __shared__ float buf[]; extern __shared__ %(dtype)s buf[];
if (warpSize != 32) if (warpSize != 32)
{ {
...@@ -2162,7 +2173,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -2162,7 +2173,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
{ {
for (int i1 = blockIdx.y; i1 < d1; i1 += gridDim.y) for (int i1 = blockIdx.y; i1 < d1; i1 += gridDim.y)
{ {
float myresult = %(reduce_init)s; %(dtype)s myresult = %(reduce_init)s;
for (int i2 = threadIdx.x; i2 < d2; i2 += blockDim.x) for (int i2 = threadIdx.x; i2 < d2; i2 += blockDim.x)
{ {
%(reduce_fct)s; %(reduce_fct)s;
...@@ -2192,7 +2203,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -2192,7 +2203,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
{ {
for (int i1 = blockIdx.y; i1 < d1; i1 += gridDim.y) for (int i1 = blockIdx.y; i1 < d1; i1 += gridDim.y)
{ {
float myresult = %(reduce_init)s; %(dtype)s myresult = %(reduce_init)s;
for (int i2 = threadIdx.y; i2 < d2; i2 += blockDim.y) for (int i2 = threadIdx.y; i2 < d2; i2 += blockDim.y)
{ {
for (int i3 = threadIdx.x; i3 < d3; i3 += blockDim.x) for (int i3 = threadIdx.x; i3 < d3; i3 += blockDim.x)
...@@ -2225,7 +2236,7 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -2225,7 +2236,7 @@ class GpuCAReduceCuda(HideC, CAReduce):
{ {
for (int i2 = blockIdx.y; i2 < d2; i2 += gridDim.y) for (int i2 = blockIdx.y; i2 < d2; i2 += gridDim.y)
{ {
float myresult = %(reduce_init)s; %(dtype)s myresult = %(reduce_init)s;
for (int i1 = threadIdx.y; i1 < d1; i1 += blockDim.y) for (int i1 = threadIdx.y; i1 < d1; i1 += blockDim.y)
{ {
for (int i3 = threadIdx.x; i3 < d3; i3 += blockDim.x) for (int i3 = threadIdx.x; i3 < d3; i3 += blockDim.x)
...@@ -2279,14 +2290,14 @@ class GpuCAReduceCuda(HideC, CAReduce): ...@@ -2279,14 +2290,14 @@ class GpuCAReduceCuda(HideC, CAReduce):
const unsigned int d1, const unsigned int d1,
const unsigned int d2, const unsigned int d2,
const unsigned int d3, const unsigned int d3,
const float *A, const int sA0, const int sA1, const %(dtype)s *A, const int sA0, const int sA1,
const int sA2, const int sA3, const int sA2, const int sA3,
float * Z, const int sZ0) %(dtype)s * Z, const int sZ0)
{ {
const int threadCount = blockDim.x * blockDim.y * blockDim.z; const int threadCount = blockDim.x * blockDim.y * blockDim.z;
const int threadNum = threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x; const int threadNum = threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
extern __shared__ float buf[]; extern __shared__ %(dtype)s buf[];
float myresult = %(reduce_init)s; %(dtype)s myresult = %(reduce_init)s;
if (warpSize != 32) if (warpSize != 32)
{ {
......
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