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提交 6e16ef97 authored 作者: Frederic's avatar Frederic
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theano/sandbox/cuda/basic_ops.py
浏览文件 @ 6e16ef97
......@@ -641,7 +641,9 @@ class GpuSum(GpuOp):
printf("running kernel_reduce_sum_%(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, nb_threads=%%d, n_blocks.x=%%d, n_blocks.y=%%d, nb_block=%%d, n_shared=%%d\\n",
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_block=%%d, n_shared=%%d\\n",
n_threads.x,n_threads.y,n_threads.z,
n_threads.x*n_threads.y*n_threads.z,
n_blocks.x,n_blocks.y,
......@@ -673,7 +675,8 @@ class GpuSum(GpuOp):
if (cudaSuccess != sts)
{
PyErr_Format(PyExc_RuntimeError,
"Cuda error: %%s: %%s. (grid: %%i x %%i; block: %%i x %%i x %%i)\\n",
"Cuda error: %%s: %%s."
" (grid: %%i x %%i; block: %%i x %%i x %%i)\\n",
"kernel_reduce_sum_%(pattern)s_%(name)s",
cudaGetErrorString(sts),
n_blocks.x,
......@@ -876,7 +879,8 @@ class GpuSum(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 n_threads.x=%%d, size=%%d, ndim=%%d\\n",
if (verbose) printf("running kernel_reduce_sum_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>>>(
......@@ -887,7 +891,9 @@ class GpuSum(GpuOp):
cudaError_t sts = cudaGetLastError();
if (cudaSuccess != sts)
{
PyErr_Format(PyExc_RuntimeError, "Cuda error: %%s: %%s. (grid: %%i x %%i; block: %%i x %%i x %%i)\\n",
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",
cudaGetErrorString(sts),
n_blocks.x,
......@@ -937,11 +943,13 @@ class GpuSum(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
"""
assert N in [1,2,3]
assert N in [1, 2, 3]
makecall = self._makecall(node, name, x, z, fail)
N_pattern = ''.join(['1']*N)
param_dim = ",".join(["CudaNdarray_HOST_DIMS(%(x)s)[%(i)s]" % locals() for i in xrange(N+1)])
strides_dim = ",".join(["CudaNdarray_HOST_STRIDES(%(x)s)[%(i)s]" % locals() for i in xrange(N+1)])
N_pattern = ''.join(['1'] * N)
param_dim = ",".join(["CudaNdarray_HOST_DIMS(%(x)s)[%(i)s]" % locals()
for i in xrange(N + 1)])
strides_dim = ",".join(["CudaNdarray_HOST_STRIDES(%(x)s)[%(i)s]"
% locals() for i in xrange(N + 1)])
threads_y = """
//get as many y threads as we can fit
while (n_threads.x * (n_threads.y+1) <= NUM_VECTOR_OP_THREADS_PER_BLOCK)
......@@ -962,10 +970,10 @@ class GpuSum(GpuOp):
break;
}
""" % locals()
if len(self.reduce_mask)==2:
if len(self.reduce_mask) == 2:
threads_y = ''
threads_z = ''
if len(self.reduce_mask)==3:
if len(self.reduce_mask) == 3:
threads_z = ''
print >> sio, """
{
......@@ -975,15 +983,18 @@ class GpuSum(GpuOp):
NUM_VECTOR_OP_THREADS_PER_BLOCK));
%(threads_y)s
%(threads_z)s
dim3 n_blocks(std::min(CudaNdarray_HOST_DIMS(%(x)s)[0],NUM_VECTOR_OP_BLOCKS));
dim3 n_blocks(std::min(CudaNdarray_HOST_DIMS(%(x)s)[0],
NUM_VECTOR_OP_BLOCKS));
%(makecall)s
}
""" % locals()
def c_code_reduce_01(self, sio, node, name, x, z, fail):
self.c_code_reduce_01X(sio, node, name, x, z, fail, 1)
def c_code_reduce_011(self, sio, node, name, x, z, fail):
self.c_code_reduce_01X(sio, node, name, x, z, fail, 2)
def c_code_reduce_0111(self, sio, node, name, x, z, fail):
self.c_code_reduce_01X(sio, node, name, x, z, fail, 3)
......@@ -1021,7 +1032,9 @@ class GpuSum(GpuOp):
cudaError_t sts = cudaGetLastError();
if (cudaSuccess != sts)
{
PyErr_Format(PyExc_RuntimeError, "Cuda error: %%s: %%s. (grid: %%i x %%i; block: %%i x %%i x %%i)\\n",
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",
cudaGetErrorString(sts),
n_blocks.x,
......@@ -1033,9 +1046,11 @@ class GpuSum(GpuOp):
}
}
""" % locals()
def c_code_reduce_010(self, sio, node, name, x, z, fail):
makecall = self._makecall(node, name, x, z, fail)
makecall_inner = self._makecall(node, name, x, z, fail, pattern="010_inner")
makecall_inner = self._makecall(node, name, x, z, fail,
pattern="010_inner")
pattern = ''.join(str(i) for i in self.reduce_mask)
print >> sio, """
{
......@@ -1085,7 +1100,9 @@ class GpuSum(GpuOp):
cudaError_t sts = cudaGetLastError();
if (cudaSuccess != sts)
{
PyErr_Format(PyExc_RuntimeError, "Cuda error: %%s: %%s. (grid: %%i x %%i; block: %%i x %%i x %%i)\\n",
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",
cudaGetErrorString(sts),
n_blocks.x,
......@@ -1233,6 +1250,7 @@ class GpuSum(GpuOp):
%(makecall)s
}
""" % locals()
def c_code_reduce_111(self, sio, node, name, x, z, fail):
makecall = self._makecall(node, name, x, z, fail)
print >> sio, """
......@@ -1275,7 +1293,8 @@ class GpuSum(GpuOp):
std::min(CudaNdarray_HOST_DIMS(%(x)s)[0],
NUM_VECTOR_OP_BLOCKS));
while (n_blocks.x * n_blocks.y <= NUM_VECTOR_OP_BLOCKS && n_blocks.y < CudaNdarray_HOST_DIMS(%(x)s)[1])
while (n_blocks.x * n_blocks.y <= NUM_VECTOR_OP_BLOCKS &&
n_blocks.y < CudaNdarray_HOST_DIMS(%(x)s)[1])
{
n_blocks.y += 1;
}
......@@ -1356,7 +1375,7 @@ class GpuSum(GpuOp):
def c_support_code_apply(self, node, nodename):
sio = StringIO.StringIO()
nd_in = len(self.reduce_mask)
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
# it only runs on ONE multiprocessor
reducebuf = self._k_reduce_buf('Z[0]')
......@@ -1411,7 +1430,7 @@ class GpuSum(GpuOp):
%(reducebuf)s
}
""" % locals()
if self.reduce_mask == (1,1):
if self.reduce_mask == (1, 1):
#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]')
......@@ -1444,29 +1463,33 @@ class GpuSum(GpuOp):
}
""" % locals()
#01, 011, 0111
if 0 == self.reduce_mask[0] and all(self.reduce_mask[1:]) and nd_in in[2,3,4]:
if (0 == self.reduce_mask[0] and
all(self.reduce_mask[1:]) and
nd_in in[2, 3, 4]):
# this kernel uses one block for each row.
# threads per block for each element per row.
N_pattern = ''.join(['1']*(nd_in-1))
if nd_in==2:
N_pattern = ''.join(['1'] * (nd_in - 1))
if nd_in == 2:
for_i1 = "for (int i1 = threadIdx.x; i1 < d1; i1 += blockDim.x)"
for_i2="int i2=0, sA2=0;"
for_i3="int i3=0, sA3=0;"
if nd_in==3:
for_i2 = "int i2=0, sA2=0;"
for_i3 = "int i3=0, sA3=0;"
if nd_in == 3:
for_i1 = "for (int i1 = threadIdx.y; i1 < d1; i1 += blockDim.y)"
for_i2 = "for (int i2 = threadIdx.x; i2 < d2; i2 += blockDim.x)"
for_i3="int i3=0, sA3=0;"
if nd_in==4:
for_i3 = "int i3=0, sA3=0;"
if nd_in == 4:
for_i1 = "for (int i1 = threadIdx.z; i1 < d1; i1 += blockDim.z)"
for_i2 = "for (int i2 = threadIdx.y; i2 < d2; i2 += blockDim.y)"
for_i3 = "for (int i3 = threadIdx.x; i3 < d3; i3 += blockDim.x)"
reducebuf = self._k_reduce_buf('Z[i0 * sZ0]')
param_dim = ",".join(["const int d%(i)s" % locals() for i in xrange(nd_in)])
param_strides = ",".join(["const int sA%(i)s" % locals() for i in xrange(nd_in)])
decl = self._k_decl(node,nodename)
init = self._k_init(node,nodename)
param_dim = ",".join(["const int d%(i)s" % locals()
for i in xrange(nd_in)])
param_strides = ",".join(["const int sA%(i)s" % locals()
for i in xrange(nd_in)])
decl = self._k_decl(node, nodename)
init = self._k_init(node, nodename)
print >> sio, """
%(decl)s{
%(init)s
......@@ -1484,7 +1507,7 @@ class GpuSum(GpuOp):
}
}
""" % locals()
if self.reduce_mask == (0,1,0) or self.reduce_mask == (1,0):
if self.reduce_mask == (0, 1, 0) or self.reduce_mask == (1, 0):
# this kernel uses one block for each column,
# threads per block for each element per column.
......@@ -1497,7 +1520,8 @@ class GpuSum(GpuOp):
const int d0,
const int d1,
const int d2,
const float *A, const int sA0, const int sA1, const int sA2,
const float *A, const int sA0,
const int sA1, const int sA2,
float * Z, const int sZ0, const int sZ1)
{
const int threadCount = blockDim.x;
......@@ -1525,7 +1549,7 @@ class GpuSum(GpuOp):
}
""" % locals()
if self.reduce_mask == (0,1,0):
if self.reduce_mask == (0, 1, 0):
print >> sio, """
static __global__ void kernel_reduce_sum_010_AD_%(nodename)s(
const int A,
......@@ -1533,7 +1557,8 @@ class GpuSum(GpuOp):
const int C,
const int D,
//const int E, // THIS is 32
const float *X, const int sX0, const int sX1, const int sX2,
const float *X, const int sX0,
const int sX1, const int sX2,
float * Z, const int sZ0, const int sZ1)
{
const int threadCount = blockDim.x;
......@@ -1564,9 +1589,10 @@ class GpuSum(GpuOp):
}
""" % locals()
if self.reduce_mask == (0,1,0):
if self.reduce_mask == (0, 1, 0):
#
# This kernel is optimized when the inner most dimensions have the smallest stride.
# This kernel is optimized when the inner most dimensions
# have the smallest stride.
# this kernel uses one block for multiple column(up to 32TODO),
# threads per block for each element per column.
......@@ -1575,10 +1601,12 @@ class GpuSum(GpuOp):
#thread.y = dim 1
#block.x = dim 0
#block.y = dim 1 rest
init = self._k_init(node,nodename)
init = self._k_init(node, nodename)
decl = self._k_decl(node, nodename, pattern="010_inner")
reducebuf = self._k_reduce_buf_multiple('Z[i0 * sZ0 + i2*sZ1]','blockDim.x')
reducebuf = self._k_reduce_buf_multiple('Z[i0 * sZ0 + i2*sZ1]','blockDim.x')
reducebuf = self._k_reduce_buf_multiple('Z[i0 * sZ0 + i2*sZ1]',
'blockDim.x')
reducebuf = self._k_reduce_buf_multiple('Z[i0 * sZ0 + i2*sZ1]',
'blockDim.x')
print >> sio, """
%(decl)s
{
......@@ -1602,7 +1630,7 @@ class GpuSum(GpuOp):
}
}
""" % locals()
if self.reduce_mask == (1,1,0):
if self.reduce_mask == (1, 1, 0):
# this kernel uses one block for each column,
# threads per block for each element per column.
......@@ -1615,7 +1643,8 @@ class GpuSum(GpuOp):
const int d0,
const int d1,
const int d2,
const float *A, const int sA0, const int sA1, const int sA2,
const float *A, const int sA0,
const int sA1, const int sA2,
float * Z, const int sZ0)
{
const int threadCount = blockDim.x * blockDim.y;
......@@ -1642,7 +1671,7 @@ class GpuSum(GpuOp):
%(reducebuf)s
}
""" % locals()
if self.reduce_mask == (1,0,0):
if self.reduce_mask == (1, 0, 0):
reducebuf = self._k_reduce_buf('Z[i1 * sZ0 + i2 * sZ1]')
decl = self._k_decl(node, nodename)
init = self._k_init(node, nodename)
......@@ -1664,7 +1693,7 @@ class GpuSum(GpuOp):
}
}
""" % locals()
if self.reduce_mask == (1,1,1):
if self.reduce_mask == (1, 1, 1):
reducebuf = self._k_reduce_buf('Z[0]')
decl = self._k_decl(node, nodename)
init = self._k_init(node, nodename)
......@@ -1686,7 +1715,7 @@ class GpuSum(GpuOp):
%(reducebuf)s
}
""" % locals()
if self.reduce_mask == (0,0,1):
if self.reduce_mask == (0, 0, 1):
# 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]')
......@@ -1695,7 +1724,8 @@ class GpuSum(GpuOp):
const int d0,
const int d1,
const int d2,
const float *A, const int sA0, const int sA1, const int sA2,
const float *A, const int sA0,
const int sA1, const int sA2,
float * Z, const int sZ0, const int sZ1)
{
const int threadCount = blockDim.x;
......@@ -1721,7 +1751,7 @@ class GpuSum(GpuOp):
}
}
""" % locals()
if self.reduce_mask == (0,0,1,1):
if self.reduce_mask == (0, 0, 1, 1):
# 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]')
......@@ -1749,7 +1779,7 @@ class GpuSum(GpuOp):
}
}
""" % locals()
if self.reduce_mask == (0,1,0,1):
if self.reduce_mask == (0, 1, 0, 1):
# 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]')
......@@ -1777,7 +1807,7 @@ class GpuSum(GpuOp):
}
}
""" % locals()
if self.reduce_mask == (1,1,1,1):
if self.reduce_mask == (1, 1, 1, 1):
reducebuf = self._k_reduce_buf('Z[0]')
decl = self._k_decl(node, nodename)
init = self._k_init(node, nodename)
......@@ -1800,7 +1830,7 @@ class GpuSum(GpuOp):
%(reducebuf)s
}
""" % locals()
if self.reduce_mask == (1,0,1,1):
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(
......@@ -1808,7 +1838,8 @@ class GpuSum(GpuOp):
const unsigned int d1,
const unsigned int d2,
const unsigned int d3,
const float *A, const int sA0, const int sA1, const int sA2, const int sA3,
const float *A, const int sA0, const int sA1,
const int sA2, const int sA3,
float * Z, const int sZ0)
{
const int threadCount = blockDim.x * blockDim.y * blockDim.z;
......@@ -1867,7 +1898,7 @@ class GpuSubtensor(tensor.Subtensor, GpuOp):
assert isinstance(x.type, CudaNdarrayType)
rval = tensor.Subtensor.make_node(self, x, *inputs)
otype = CudaNdarrayType(rval.outputs[0].type.broadcastable)
return Apply(self, [x]+rval.inputs[1:], [otype()])
return Apply(self, [x] + rval.inputs[1:], [otype()])
def perform(self, node, inputs, out_):
out, = out_
......@@ -2033,14 +2064,14 @@ class GpuIncSubtensor(tensor.IncSubtensor, GpuOp):
assert isinstance(x.type, CudaNdarrayType)
assert isinstance(y.type, CudaNdarrayType)
rval = tensor.IncSubtensor.make_node(self, x, y, *inputs)
return Apply(self, [x,y]+rval.inputs[2:], [x.type()])
return Apply(self, [x, y] + rval.inputs[2:], [x.type()])
class GpuFlatten(tensor.Flatten, GpuOp):
"""
Implement Flatten on the gpu.
"""
def make_node(self, x ):
def make_node(self, x):
assert isinstance(x.type, CudaNdarrayType)
rval = tensor.Flatten.make_node(self, x)
host_out_broadcastable = rval.outputs[0].type.broadcastable
......@@ -2096,10 +2127,12 @@ class GpuJoin(tensor.Join, GpuOp):
# dimension in "axis" can be different, so make equal for ==
tmp_shape[axis] = template_shape[axis]
if tuple(tmp_shape) != template_shape:
raise ValueError, "Shape of input CudaNdarrays must agree except for the 'axis' dimension"
raise ValueError("Shape of input CudaNdarrays must"
" agree except for the 'axis' dimension")
if len(template_shape) != node.outputs[0].type.ndim:
raise ValueError, "Number of dimension of input tensors disagree with dimensions passed at graph creation time."
raise ValueError("Number of dimension of input tensors disagree"
" with dimensions passed at graph creation time.")
# final shape must be the same as all input tensors
# except for the "axis" dimension, so we can simply
......@@ -2110,7 +2143,8 @@ class GpuJoin(tensor.Join, GpuOp):
# just to be explicit, check that dim=1 for broadcastable
# dimensions
for i, bcastable in enumerate(node.outputs[0].type.broadcastable):
assert not bcastable or final_shape[i] == 1, "Broadcastable dimension but dim != 1, this is invalid"
assert not bcastable or final_shape[i] == 1, (
"Broadcastable dimension but dim != 1, this is invalid")
rval = cuda_ndarray.cuda_ndarray.CudaNdarray.zeros(final_shape)
......@@ -2120,9 +2154,9 @@ class GpuJoin(tensor.Join, GpuOp):
# except for 'axis'
def construct_slices(curlen):
slices = [slice(None,None,None) for i in \
slices = [slice(None, None, None) for i in \
range(len(template_shape))]
slices[axis] = slice(curpos,curpos+curlen,None)
slices[axis] = slice(curpos, curpos + curlen, None)
return tuple(slices)
for i, cnda in enumerate(cndas):
......@@ -2157,7 +2191,9 @@ class GpuAlloc(GpuOp):
v = as_cuda_ndarray_variable(value)
sh = [tensor.as_tensor_variable(s) for s in shape]
if v.ndim != len(shape):
raise TypeError('GpuAlloc requires value of same dimensions as shape', value, len(shape))
raise TypeError(
'GpuAlloc requires value of same dimensions as shape',
value, len(shape))
bcast = []
for s in sh:
......@@ -2170,7 +2206,7 @@ class GpuAlloc(GpuOp):
const_shp = None
bcast.append(numpy.all(1 == const_shp))
otype = CudaNdarrayType(dtype='float32', broadcastable=bcast)
return Apply(self, [v]+sh, [otype()])
return Apply(self, [v] + sh, [otype()])
def perform(self, node, inputs, out_):
out, = out_
......@@ -2178,7 +2214,7 @@ class GpuAlloc(GpuOp):
sh = tuple([int(i) for i in inputs[1:]])
if out[0] is None or out[0].shape != sh:
out[0] = cuda_ndarray.cuda_ndarray.CudaNdarray.zeros(sh)
out[0][...] = v # broadcast v to fill us up
out[0][...] = v # broadcast v to fill us up
def c_code(self, node, name, inputs, out_, sub):
out, = out_
......@@ -2186,12 +2222,12 @@ class GpuAlloc(GpuOp):
value = inputs[0]
shps = inputs[1:]
nd = len(shps)
str = "int dims[%(nd)s];\n" % locals()
for idx,sh in enumerate(shps):
str = "int dims[%(nd)s];\n" % locals()
for idx, sh in enumerate(shps):
str += "dims[%(idx)s] = PyInt_AsLong((PyObject*)%(sh)s);\n" % locals()
str += "if(%(out)s==NULL\n" % locals()
for idx,sh in enumerate(shps):
for idx, sh in enumerate(shps):
str += "||CudaNdarray_HOST_DIMS(%(out)s)[%(idx)s]!=dims[%(idx)s]" % locals()
str += """){
Py_XDECREF(%(out)s);
......@@ -2350,10 +2386,9 @@ def tensordot(a, b, axes=2):
"Axes should be scalar valued or a list/tuple of len 2.",
axes)
# Those are predifined CudaNdarrayType as done in tensor.basic
# Useful mostly for test as the gpu op are inserted automatically...
fscalar = CudaNdarrayType(dtype='float32', broadcastable=())
def scalar(name=None, dtype=None):
"""Return a symbolic scalar variable.
:param dtype: numeric type (None means to use theano.config.floatX)
......@@ -2363,8 +2398,9 @@ def scalar(name=None, dtype=None):
dtype = config.floatX
type = CudaNdarrayType(dtype=dtype, broadcastable=())
return type(name)
fscalar = CudaNdarrayType(dtype='float32', broadcastable=())
fvector = CudaNdarrayType(dtype='float32', broadcastable=(False, ))
def vector(name=None, dtype=None):
"""Return a symbolic vector variable.
:param dtype: numeric type (None means to use theano.config.floatX)
......@@ -2374,8 +2410,9 @@ def vector(name=None, dtype=None):
dtype = config.floatX
type = CudaNdarrayType(dtype=dtype, broadcastable=(False, ))
return type(name)
fvector = CudaNdarrayType(dtype='float32', broadcastable=(False, ))
fmatrix = CudaNdarrayType(dtype='float32', broadcastable=(False, False))
def matrix(name=None, dtype=None):
"""Return a symbolic matrix variable.
:param dtype: numeric type (None means to use theano.config.floatX)
......@@ -2385,8 +2422,9 @@ def matrix(name=None, dtype=None):
dtype = config.floatX
type = CudaNdarrayType(dtype=dtype, broadcastable=(False, False))
return type(name)
fmatrix = CudaNdarrayType(dtype='float32', broadcastable=(False, False))
frow = CudaNdarrayType(dtype='float32', broadcastable=(True, False))
def row(name=None, dtype=None):
"""Return a symbolic row variable (ndim=2, broadcastable=[True,False]).
:param dtype: numeric type (None means to use theano.config.floatX)
......@@ -2396,8 +2434,9 @@ def row(name=None, dtype=None):
dtype = config.floatX
type = CudaNdarrayType(dtype=dtype, broadcastable=(True, False))
return type(name)
frow = CudaNdarrayType(dtype='float32', broadcastable=(True, False))
fcol = CudaNdarrayType(dtype='float32', broadcastable=(False, True))
def col(name=None, dtype=None):
"""Return a symbolic column variable (ndim=2, broadcastable=[False,True]).
:param dtype: numeric type (None means to use theano.config.floatX)
......@@ -2407,8 +2446,9 @@ def col(name=None, dtype=None):
dtype = config.floatX
type = CudaNdarrayType(dtype=dtype, broadcastable=(False, True))
return type(name)
fcol = CudaNdarrayType(dtype='float32', broadcastable=(False, True))
ftensor3 = CudaNdarrayType(dtype='float32', broadcastable=(False,)*3)
def tensor3(name=None, dtype=None):
"""Return a symbolic 3-D variable.
:param dtype: numeric type (None means to use theano.config.floatX)
......@@ -2418,8 +2458,9 @@ def tensor3(name=None, dtype=None):
dtype = config.floatX
type = CudaNdarrayType(dtype=dtype, broadcastable=(False, False, False))
return type(name)
ftensor3 = CudaNdarrayType(dtype='float32', broadcastable=(False,) * 3)
ftensor4 = CudaNdarrayType(dtype='float32', broadcastable=(False,) * 4)
def tensor4(name=None, dtype=None):
"""Return a symbolic 4-D variable.
:param dtype: numeric type (None means to use theano.config.floatX)
......@@ -2430,6 +2471,7 @@ def tensor4(name=None, dtype=None):
type = CudaNdarrayType(dtype=dtype,
broadcastable=(False, False, False, False))
return type(name)
ftensor4 = CudaNdarrayType(dtype='float32', broadcastable=(False,) * 4)
@theano.compile.profilemode.register_profiler_printer
......@@ -2446,22 +2488,24 @@ def profile_printer(fct_name, compile_time, fct_call_time, fct_call,
gpu = 0
trans = 0
for (_, node), t in apply_time.items():
if isinstance(node.op.__class__.__name__, (HostFromGpu, GpuFromHost)):
if isinstance(node.op.__class__.__name__,
(HostFromGpu, GpuFromHost)):
trans += t
elif node.op.__class__.__name__.lower().startswith("gpu"):
gpu += t
else:
cpu += t
print
print " Spent %.3fs(%.3f%%) in cpu Op, %.3fs(%.3f%%) in gpu Op and %.3fs(%.3f%%) transfert Op"%(
cpu, cpu/local_time*100, gpu, gpu/local_time*100, trans, trans/local_time*100)
print " Spent %.3fs(%.3f%%) in cpu Op, %.3fs(%.3f%%) in gpu Op and %.3fs(%.3f%%) transfert Op" % (
cpu, cpu / local_time * 100, gpu, gpu / local_time * 100,
trans, trans / local_time * 100)
print
print " Theano function input that are float64"
print " <fct name> <input name> <input type> <str input>"
for fct in fct_call.keys():
for i in fct.input_storage:
if hasattr(i.type, 'dtype') and i.type.dtype=='float64':
if hasattr(i.type, 'dtype') and i.type.dtype == 'float64':
print ' ', fct.name, i.name, i.type, i
print
......@@ -2470,5 +2514,13 @@ def profile_printer(fct_name, compile_time, fct_call_time, fct_call,
print ' <Apply> <Apply position> <fct name> <inputs type> <outputs type>'
for fct in fct_call.keys():
for idx, node in enumerate(fct.maker.fgraph.toposort()):
if any(hasattr(i,'dtype') and i.dtype=='float64' for i in node.outputs) and not any(hasattr(i,'dtype') and i.dtype=='float64' for i in node.inputs):
print ' ', str(node), idx, fct.name, str([getattr(i,'dtype',None) for i in node.inputs]),str([getattr(i,'dtype',None) for i in node.outputs])
if (any(hasattr(i, 'dtype') and i.dtype == 'float64'
for i in node.outputs) and
not any(hasattr(i, 'dtype') and i.dtype == 'float64'
for i in node.inputs)):
print ' ', str(node), idx, fct.name,
print str([getattr(i, 'dtype', None)
for i in node.inputs]),
print str([getattr(i, 'dtype', None)
for i in node.outputs])
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