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提交 66846111 authored 作者: Frederic's avatar Frederic
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theano/sandbox/cuda/basic_ops.py
浏览文件 @ 66846111
......@@ -543,7 +543,9 @@ class GpuCAReduce(GpuOp):
self.scalar_op == other.scalar_op)
def __hash__(self):
return hash(type(self)) ^ hash(self.reduce_mask) ^ hash(type(self.scalar_op))
return (hash(type(self)) ^
hash(self.reduce_mask) ^
hash(type(self.scalar_op)))
def __str__(self):
return "GpuCAReduce{%s}{%s}" % (
......@@ -599,7 +601,7 @@ class GpuCAReduce(GpuOp):
inp = ['fake_input_name_%d' % i for i in xrange(len(inputs))]
out = ['fake_output_name_%d' % i for i in xrange(len(node.outputs))]
sub = { 'fail' : 'fake failure code' }
sub = {'fail': 'fake failure code'}
try:
self.c_code(node, name, inp, out, sub)
......@@ -634,8 +636,9 @@ class GpuCAReduce(GpuOp):
# but tensor.elemwise.CAReduce has this exact same check so I guess
# this is OK to do
if self.scalar_op in [scal.minimum, scal.maximum]:
conds = ["(CudaNdarray_HOST_DIMS(%s)[%d] == 0)" % (x, i) for i in xrange(nd_in) \
if self.reduce_mask[i]]
conds = ["(CudaNdarray_HOST_DIMS(%s)[%d] == 0)" % (x, i)
for i in xrange(nd_in)
if self.reduce_mask[i]]
assert len(conds) > 0
cond = "(" + " || ".join(conds) + ")"
print >> sio, """
......@@ -710,10 +713,12 @@ class GpuCAReduce(GpuOp):
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)
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)
getattr(self, 'c_code_reduce_%s'%(''.join(
str(i) for i in self.reduce_mask)))(sio, node, name, x, z, fail)
# \end bracket the reduction ...
print >> sio, """
......@@ -897,17 +902,16 @@ class GpuCAReduce(GpuOp):
returns C code to reduce left and right, assigning the
result to left."""
x ,= node.inputs
x, = node.inputs
dtype = x.dtype
dummy_left = scal.Scalar(dtype = dtype)()
dummy_right = scal.Scalar(dtype = dtype)()
dummy_left = scal.Scalar(dtype=dtype)()
dummy_right = scal.Scalar(dtype=dtype)()
dummy_node = self.scalar_op.make_node(dummy_left, dummy_right)
dummy_name = name + '_scalar_op'+ str(self._n_scalar_op_calls)
dummy_name = name + '_scalar_op' + str(self._n_scalar_op_calls)
self._n_scalar_op_calls += 1
return self.scalar_op.c_code(dummy_node, dummy_name, (left, right),
......@@ -954,7 +958,8 @@ class GpuCAReduce(GpuOp):
float temp = buf[threadNum + halfPoint];
"""
new_version += self._assign_reduce(node, name, 'buf[threadNum]', 'temp', sub)
new_version += self._assign_reduce(node, name,
'buf[threadNum]', 'temp', sub)
new_version += """
}
......@@ -984,7 +989,8 @@ class GpuCAReduce(GpuOp):
for (int i = threadNum + warpSize; i < threadCount; i += warpSize)
{
"""
current_version += self._assign_reduce(node, name, 'myresult', 'buf[i]', sub) + """
current_version += self._assign_reduce(node, name,
'myresult', 'buf[i]', sub) + """
}
buf[threadNum] = myresult;
/*Comment this optimization as it don't work on Fermi GPU.
......@@ -992,9 +998,11 @@ class GpuCAReduce(GpuOp):
// no sync because only one warp is running
if(threadCount >32)
{"""
for num in [16,8,4,2,1]:
current_version += self._assign_reduce(node, name, 'buf[threadNum]',
'buf[threadNum+%d]' % num, sub)
for num in [16, 8, 4, 2, 1]:
current_version += self._assign_reduce(node, name,
'buf[threadNum]',
'buf[threadNum+%d]' % num,
sub)
current_version += """
if (threadNum == 0)
{
......@@ -1007,9 +1015,11 @@ class GpuCAReduce(GpuOp):
{
//reduce so that threadNum 0 has the reduction of everything
"""
for num in [16,8,4,2,1]:
for num in [16, 8, 4, 2, 1]:
this_if = "if (threadNum + %d < threadCount) " % num + \
self._assign_reduce(node, name, 'buf[threadNum]','buf[threadNum+%d]' % num, sub)
self._assign_reduce(node, name,
'buf[threadNum]','buf[threadNum+%d]' % num,
sub)
current_version += this_if
current_version += """
if (threadNum == 0)
......@@ -1590,7 +1600,7 @@ class GpuCAReduce(GpuOp):
raise NotImplementedError()
#this kernel is ok for up to a few thousand elements, but
# it only runs on ONE multiprocessor
reducebuf = self._k_reduce_buf('Z[0]', node, nodename, sub = {})
reducebuf = self._k_reduce_buf('Z[0]', node, nodename, sub={})
reduce_fct = self._assign_reduce(node, nodename, "myresult",
"A[i0]",
{})
......@@ -1628,7 +1638,7 @@ class GpuCAReduce(GpuOp):
raise NotImplementedError()
#this kernel is ok for up to a few thousand elements, but
# it only runs on ONE multiprocessor
reducebuf = self._k_reduce_buf('Z[0]', node, nodename, sub = {})
reducebuf = self._k_reduce_buf('Z[0]', node, nodename, sub={})
reduce_fct = self._assign_reduce(node, nodename, "myresult",
"A[i0 * sA0]",
{})
......@@ -1666,7 +1676,7 @@ class GpuCAReduce(GpuOp):
raise NotImplementedError()
#this kernel is ok for up to a few thousand elements, but
# it only runs on ONE multiprocessor
reducebuf = self._k_reduce_buf('Z[0]', node, nodename, sub = {})
reducebuf = self._k_reduce_buf('Z[0]', node, nodename, sub={})
reduce_fct = self._assign_reduce(node, nodename, "myresult",
"A[i0 * sA0 + i1 * sA1]",
{})
......@@ -1743,7 +1753,8 @@ class GpuCAReduce(GpuOp):
first_i3 = 'threadIdx.x'
sA3 = 'sA3'
reducebuf = self._k_reduce_buf('Z[i0 * sZ0]', node, nodename, sub = {})
reducebuf = self._k_reduce_buf('Z[i0 * sZ0]', node,
nodename, sub={})
param_dim = ",".join(["const int d%d" % i
for i in xrange(nd_in)])
param_strides = ",".join(["const int sA%d" % i
......@@ -2044,7 +2055,7 @@ class GpuCAReduce(GpuOp):
scal.Minimum)):
raise NotImplementedError()
reducebuf = self._k_reduce_buf('Z[0]', node,
nodename, sub={})
nodename, sub={})
decl = self._k_decl(node, nodename)
init = self._k_init(node, nodename)
reduce_fct = self._assign_reduce(node, nodename, "myresult",
......@@ -2080,7 +2091,7 @@ class GpuCAReduce(GpuOp):
# this kernel uses one block for each row,
# threads per block for each element per row.
reducebuf = self._k_reduce_buf('Z[i0 * sZ0 + i1 * sZ1]',
node, nodename, sub = {})
node, nodename, sub={})
reduce_fct = self._assign_reduce(node, nodename, "myresult",
"A[i0 * sA0 + i1 * sA1 + i2 * sA2]",
{})
......@@ -2128,7 +2139,7 @@ class GpuCAReduce(GpuOp):
# this kernel uses one block for each row,
# threads per block for each element per row.
reducebuf = self._k_reduce_buf('Z[i0 * sZ0 + i1 * sZ1]',
node, nodename, sub = {})
node, nodename, sub={})
decl = self._k_decl(node, nodename)
init = self._k_init(node, nodename)
reduce_fct = self._assign_reduce(node, nodename, "myresult",
......@@ -2168,7 +2179,7 @@ class GpuCAReduce(GpuOp):
# this kernel uses one block for each row,
# threads per block for each element per row.
reducebuf = self._k_reduce_buf('Z[i0 * sZ0 + i2 * sZ1]',
node, nodename, sub = {})
node, nodename, sub={})
decl = self._k_decl(node, nodename)
init = self._k_init(node, nodename)
reduce_fct = self._assign_reduce(node, nodename, "myresult",
......@@ -2206,7 +2217,7 @@ class GpuCAReduce(GpuOp):
scal.Minimum)):
raise NotImplementedError()
reducebuf = self._k_reduce_buf('Z[0]', node, nodename,
sub = {})
sub={})
decl = self._k_decl(node, nodename)
init = self._k_init(node, nodename)
reduce_fct = self._assign_reduce(node, nodename, "myresult",
......@@ -2241,7 +2252,7 @@ class GpuCAReduce(GpuOp):
scal.Minimum)):
raise NotImplementedError()
reducebuf = self._k_reduce_buf('Z[blockIdx.x*sZ0]',
node, nodename, sub = {})
node, nodename, sub={})
reduce_fct = self._assign_reduce(node, nodename, "myresult",
"A[i0 * sA0 + blockIdx.x * sA1 + i2 * sA2 + i3 * sA3]",
{})
......
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