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提交 b69ad54d authored 作者: Xavier Bouthillier's avatar Xavier Bouthillier
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Merge pull request #4244 from ChihebTrabelsi/ccw2.0

flake8 sandbox/cuda/*.py
上级 200babca 58267dc2
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theano/sandbox/cuda/GpuConvGrad3D.py
浏览文件 @ b69ad54d
......@@ -39,7 +39,7 @@ class GpuConvGrad3D(GpuOp):
d_ = T.as_tensor_variable(d)
WShape_ = T.as_tensor_variable(WShape)
dCdH_ = as_cuda_ndarray_variable(dCdH)
broad = (False,)*5
broad = (False,) * 5
return theano.Apply(self, inputs=[V_, d_, WShape_, dCdH_],
outputs=[CudaNdarrayType(dtype=V_.dtype,
broadcastable=broad)()])
......@@ -51,15 +51,10 @@ class GpuConvGrad3D(GpuOp):
# partial C / partial W[j,z,k,l,m] = sum_i sum_p sum_q sum_r (partial C /partial H[i,j,p,q,r] ) * V[i,z,dr*p+k,dc*q+l,dt*r+m]
batchSize = dCdH.shape[0]
outputFilters = dCdH.shape[1]
outputHeight = dCdH.shape[2]
outputWidth = dCdH.shape[3]
outputDur = dCdH.shape[4]
assert V.shape[0] == batchSize
inputFilters = V.shape[1]
inputHeight = V.shape[2]
inputWidth = V.shape[3]
inputDur = V.shape[4]
dr, dc, dt = d
dCdW = numpy.zeros(WShape, dtype=V.dtype)
......@@ -76,7 +71,11 @@ class GpuConvGrad3D(GpuOp):
for p in xrange(0, outputHeight):
for q in xrange(0, outputWidth):
for r in xrange(0, outputDur):
dCdW[j, z, k, l, m] += dCdH[i, j, p, q, r] * V[i, z, dr*p+k, dc*q+l, dt*r+m]
dCdW[j, z, k, l, m] += dCdH[
i, j, p, q, r] * \
V[i, z, dr * p + k,
dc * q + l,
dt * r + m]
output_storage[0][0] = dCdW
......@@ -86,7 +85,7 @@ class GpuConvGrad3D(GpuOp):
dCdW = outputs[0]
codeSource = """
codeSource = """
///////////// < code generated by GpuConvGrad3D >
//printf("\t\t\t\tGpuConvGrad3DW c code\\n");
......@@ -285,7 +284,7 @@ if(!work_complete){
# This code is not sensitive to the ignore_border flag.
# It runs for every position in the output z, and then computes the gradient for the
# input pixels that were downsampled to that z-position.
codeSource = """
codeSource = """
__global__ void
//thread block size = WShape[4]
//grid block size = (WShape[0]*WShape[1],WShape[2]*WShape[3])
......
theano/sandbox/cuda/GpuConvTransp3D.py
浏览文件 @ b69ad54d
......@@ -37,9 +37,10 @@ class GpuConvTransp3D(GpuOp):
else:
RShape_ = T.as_tensor_variable([-1, -1, -1])
return theano.Apply(self, inputs=[W_, b_, d_, H_, RShape_],
outputs=[CudaNdarrayType(dtype=H_.dtype,
broadcastable=(False,)*5)()])
return theano.Apply(
self, inputs=[W_, b_, d_, H_, RShape_],
outputs=[CudaNdarrayType(
dtype=H_.dtype, broadcastable=(False,) * 5)()])
def infer_shape(self, node, input_shapes):
W, b, d, H, RShape = node.inputs
......@@ -382,9 +383,9 @@ def computeR(W, b, d, H, Rshape=None):
assert dc > 0
assert dt > 0
videoHeight = (outputHeight-1) * dr + filterHeight
videoWidth = (outputWidth-1) * dc + filterWidth
videoDur = (outputDur-1) * dt + filterDur
videoHeight = (outputHeight - 1) * dr + filterHeight
videoWidth = (outputWidth - 1) * dc + filterWidth
videoDur = (outputDur - 1) * dt + filterDur
if Rshape is not None and Rshape[0] != -1:
if Rshape[0] < videoHeight:
......@@ -399,26 +400,46 @@ def computeR(W, b, d, H, Rshape=None):
# else:
# print "No Rshape passed in"
# print "video size: "+str((videoHeight, videoWidth, videoDur))
# print "video size: " + str((videoHeight, videoWidth, videoDur))
R = numpy.zeros( (batchSize, inputChannels, videoHeight,
videoWidth, videoDur ) , dtype=H.dtype)
R = numpy.zeros((batchSize, inputChannels, videoHeight,
videoWidth, videoDur),
dtype=H.dtype)
# R[i,j,r,c,t] = b_j + sum_{rc,rk | d \circ rc + rk = r} sum_{cc,ck | ...} sum_{tc,tk | ...} sum_k W[k, j, rk, ck, tk] * H[i,k,rc,cc,tc]
# R[i,j,r,c,t] = b_j + sum_{rc,rk | d \circ rc + rk = r} sum_{cc,ck | ...} \
# sum_{tc,tk | ...} sum_k W[k, j, rk, ck, tk] * H[i,k,rc,cc,tc]
for i in xrange(0, batchSize):
# print '\texample '+str(i+1)+'/'+str(batchSize)
for j in xrange(0, inputChannels):
# print '\t\tfeature map '+str(j+1)+'/'+str(inputChannels)
# print '\t\tfeature map ' + str(j+1) + '/' + str(inputChannels)
for r in xrange(0, videoHeight):
# print '\t\t\trow '+str(r+1)+'/'+str(videoHeight)
# print '\t\t\trow ' + str(r+1) + '/'+str(videoHeight)
for c in xrange(0, videoWidth):
for t in xrange(0, videoDur):
R[i, j, r, c, t] = b[j]
ftc = max([0, int(numpy.ceil(float(t-filterDur + 1 )/float(dt))) ])
fcc = max([0, int(numpy.ceil(float(c-filterWidth + 1)/float(dc))) ])
rc = max([0, int(numpy.ceil(float(r-filterHeight+1)/float(dr))) ])
ftc = max(
[0,
int(numpy.ceil(
float(t - filterDur + 1) / float(dt)
))
]
)
fcc = max(
[0,
int(numpy.ceil(
float(c - filterWidth + 1) / float(dc)
))
]
)
rc = max(
[0,
int(numpy.ceil(
float(r - filterHeight + 1) / float(dr)
))
]
)
while rc < outputHeight:
rk = r - rc * dr
if rk < 0:
......@@ -436,7 +457,9 @@ def computeR(W, b, d, H, Rshape=None):
if tk < 0:
break
R[i, j, r, c, t] += numpy.dot(W[:, j, rk, ck, tk], H[i, :, rc, cc, tc] )
R[i, j, r, c, t] += numpy.dot(
W[:, j, rk, ck, tk],
H[i, :, rc, cc, tc])
tc += 1
"" # close loop over tc
......
theano/sandbox/cuda/basic_ops.py
浏览文件 @ b69ad54d
......@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
import copy
import logging
import sys
import warnings
import numpy
from six import iteritems
from six.moves import StringIO, xrange
......@@ -12,6 +12,9 @@ from theano import gof, Type, Apply
from theano import tensor, scalar, config
from theano.gradient import grad_undefined
from theano.scalar import Scalar
from theano.sandbox.cuda import GpuOp
from theano.sandbox.cuda.type import CudaNdarrayType
from theano.sandbox.cuda.elemwise import NaiveAlgo
scal = scalar # somewhere scalar gets reassigned to be a function
......@@ -24,10 +27,6 @@ try:
except ImportError:
pass
from theano.sandbox.cuda import GpuOp
from theano.sandbox.cuda.type import CudaNdarrayType
from theano.sandbox.cuda.elemwise import NaiveAlgo
_logger_name = 'theano.sandbox.cuda.basic_ops'
_logger = logging.getLogger(_logger_name)
......@@ -79,7 +78,7 @@ class HostFromGpu(GpuOp):
"CudaNdarrayType. Got %s with type %s" % (x,
x.type))
return Apply(self, [x], [tensor.TensorType(dtype=x.dtype,
broadcastable=x.broadcastable)()])
broadcastable=x.broadcastable)()])
def perform(self, node, inp, out):
x, = inp
......@@ -535,10 +534,10 @@ class GpuCAReduce(GpuOp):
Parameters
----------
pre_scalar_op
pre_scalar_op
If present, must be a scalar op with only 1 input.
We will execute it on the input value before reduction.
Notes
-----
This Op is a work in progress.
......@@ -596,10 +595,8 @@ class GpuCAReduce(GpuOp):
if self.pre_scalar_op:
pre = "pre=%s,red=" % str(self.pre_scalar_op)
return "GpuCAReduce{%s%s}{%s}" % (
pre,
str(self.scalar_op),
','.join(str(i) for i in self.reduce_mask)
)
pre, str(self.scalar_op),
','.join(str(i) for i in self.reduce_mask))
def __setstate__(self, d):
self.__dict__.update(d)
......@@ -775,15 +772,18 @@ class GpuCAReduce(GpuOp):
# check if the tensor is ccontiguous, if true, use the c_code_reduce_ccontig code.
# TODO: check if we are ccontiguous when we un-dimshuffle
# TODO: if only some dims are ccontiguous, call version with less dims.
print('if(CudaNdarray_is_c_contiguous(%(x)s)){'%locals(), file=sio)
print('if(CudaNdarray_is_c_contiguous( %(x)s)){' % locals(),
file=sio)
self.c_code_reduce_ccontig(sio, node, name, x, z, fail)
print("}else{", file=sio)
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("}", file=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("""
......@@ -976,7 +976,7 @@ class GpuCAReduce(GpuOp):
assert isinstance(self.scalar_op, (scal.Maximum,
scal.Minimum))
if self.pre_scalar_op:
#dtype = node.inputs[0].dtype
# dtype = node.inputs[0].dtype
dtype = 'float32'
dummy_var = scal.Scalar(dtype=dtype)()
......@@ -1275,7 +1275,7 @@ class GpuCAReduce(GpuOp):
def c_code_reduce_01X(self, sio, node, name, x, z, fail, N):
"""
Parameters
----------
N : int
......@@ -1834,12 +1834,15 @@ class GpuCAReduce(GpuOp):
version = [15] # 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,
[Scalar(dtype=input.type.dtype)() for input in node.inputs],
[Scalar(dtype=output.type.dtype)() for output in node.outputs])
Apply(self.scalar_op,
[Scalar(
dtype=input.type.dtype)() for input in node.inputs],
[Scalar(
dtype=output.type.dtype)() for output in node.outputs])
version.extend(self.scalar_op.c_code_cache_version())
for i in node.inputs + node.outputs:
version.extend(Scalar(dtype=i.type.dtype).c_code_cache_version())
version.extend(
Scalar(dtype=i.type.dtype).c_code_cache_version())
if all(version):
return tuple(version)
else:
......@@ -1946,10 +1949,11 @@ class GpuCAReduce(GpuOp):
%(reducebuf)s
}
""" % locals(), file=sio)
#01, 011, 0111
# 01, 011, 0111
if (0 == self.reduce_mask[0] and
all(self.reduce_mask[1:]) and
nd_in in[2, 3, 4]):
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.
......@@ -2117,10 +2121,10 @@ class GpuCAReduce(GpuOp):
# this kernel uses one block for multiple column(up to 32TODO),
# threads per block for each element per column.
# thread.x = dim 2 contiguous
# thread.y = dim 1
# block.x = dim 0
# block.y = dim 1 rest
# thread.x = dim 2 contiguous
# thread.y = dim 1
# block.x = dim 0
# block.y = dim 1 rest
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]',
......@@ -2294,7 +2298,7 @@ class GpuCAReduce(GpuOp):
}
""" % locals(), file=sio)
if self.reduce_mask == (0, 0, 1, 1):
# this kernel uses one block for each row,
# 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={})
......@@ -2470,7 +2474,7 @@ class GpuReshape(tensor.Reshape, GpuOp):
if (x.size % ss) != 0:
raise ValueError("When using -1 in new shape, the computed new shape must be an multiple of the original shape.")
shp_new = numpy.copy(shp)
shp_new[m1_idx] = x.size/ss
shp_new[m1_idx] = x.size / ss
shp = shp_new
else:
......@@ -2711,7 +2715,7 @@ class GpuAdvancedSubtensor1(tensor.AdvancedSubtensor1, GpuOp):
# c code suppose it is int64
if x.ndim in [1, 2, 3] and ilist_.dtype in [
'int8', 'int16', 'int32', 'uint8', 'uint16', 'uint32']:
'int8', 'int16', 'int32', 'uint8', 'uint16', 'uint32']:
ilist_ = tensor.cast(ilist_, 'int64')
bcast = (ilist_.broadcastable[0],) + x_.broadcastable[1:]
......@@ -2721,7 +2725,7 @@ class GpuAdvancedSubtensor1(tensor.AdvancedSubtensor1, GpuOp):
def perform(self, node, inp, out_):
# This don't work as CudaNdarray_Subscript() don't support it.
#super(GpuAdvancedSubtensor1, self).perform(node, inp, out_)
# super(GpuAdvancedSubtensor1, self).perform(node, inp, out_)
x, idx = inp
out, = out_
x_orig = x
......@@ -2733,7 +2737,7 @@ class GpuAdvancedSubtensor1(tensor.AdvancedSubtensor1, GpuOp):
if x.ndim <= 3:
# CudaNdarray.take only supports ndim <= 3
if self.perform_using_take is not None:
assert self.perform_using_take == True, (
assert self.perform_using_take is True, (
"GpuAdvancedSubtensor1 used the fast version")
if idx.dtype != numpy.int64:
if idx.dtype in [numpy.int8, numpy.int16, numpy.int32,
......@@ -2762,7 +2766,7 @@ class GpuAdvancedSubtensor1(tensor.AdvancedSubtensor1, GpuOp):
out[0] = o
else:
if self.perform_using_take is not None:
assert self.perform_using_take == False, (
assert self.perform_using_take is False, (
"GpuAdvancedSubtensor1 didn't use the fast version")
if out_[0][0] is None or out_[0][0].shape != out_shape:
o = cuda_ndarray.cuda_ndarray.CudaNdarray.zeros(out_shape)
......@@ -3006,8 +3010,7 @@ class GpuAdvancedIncSubtensor1_dev20(GpuAdvancedIncSubtensor1):
convert_map = {8: tensor.basic._convert_to_int8,
16: tensor.basic._convert_to_int16,
32: tensor.basic._convert_to_int32,
64: tensor.basic._convert_to_int64
}
64: tensor.basic._convert_to_int64}
intwidth = theano.configdefaults.python_int_bitwidth()
ilist_ = convert_map[intwidth](ilist_)
......@@ -3039,8 +3042,8 @@ class GpuAdvancedIncSubtensor1_dev20(GpuAdvancedIncSubtensor1):
active_device_no = theano.sandbox.cuda.active_device_number()
compute_capability = device_properties(active_device_no)['major']
if ((node.inputs[0].ndim != node.inputs[1].ndim) or
(node.inputs[0].ndim != 2) or
(compute_capability < 2)):
(node.inputs[0].ndim != 2) or
(compute_capability < 2)):
raise NotImplementedError("This case does not have C code yet.")
x = inputs[0]
......@@ -3212,7 +3215,7 @@ class GpuIncSubtensor(tensor.IncSubtensor, GpuOp):
return Apply(self, [x, y] + rval.inputs[2:], [x.type()])
def do_type_checking(self, node):
"""
"""
Should raise NotImplementedError if c_code does not support
the types involved in this node.
......@@ -3248,7 +3251,7 @@ class GpuIncSubtensor(tensor.IncSubtensor, GpuOp):
"""
Parameters
----------
----------
x : str
A string identifying an array to be viewed.
view_ndim : str
......@@ -3354,7 +3357,6 @@ class GpuFlatten(gof.HideC, tensor.Flatten, GpuOp):
return Apply(self, [x], [out_type()])
def gpu_flatten(x, outdim=1):
"""
Implement flatten on the gpu.
......@@ -3378,10 +3380,10 @@ def gpu_flatten(x, outdim=1):
"""
x = as_cuda_ndarray_variable(x)
if outdim > 1:
dims = tuple(x.shape[:outdim-1])+(-1,)
dims = tuple(x.shape[:outdim - 1]) + (-1, )
else:
dims = (-1,)
return GpuReshape(outdim)(x, dims)
dims = (-1, )
return GpuReshape(outdim)(x, dims)
class GpuShape(tensor.Shape, GpuOp):
......@@ -3408,12 +3410,11 @@ class GpuJoin(tensor.Join, GpuOp):
as_tensor_variable_args = [as_cuda_ndarray_variable(x)
for x in tensors]
output_maker = \
lambda bcast: CudaNdarrayType(broadcastable=bcast)()
def output_maker(bcast):
return(CudaNdarrayType(broadcastable=bcast)())
return tensor.Join._make_node_internal(self,
axis, tensors,
as_tensor_variable_args, output_maker)
return tensor.Join._make_node_internal(
self, axis, tensors, as_tensor_variable_args, output_maker)
def perform(self, node, axis_and_tensors, out_):
out, = out_
......@@ -3464,8 +3465,8 @@ class GpuJoin(tensor.Join, GpuOp):
# except for 'axis'
def construct_slices(curlen):
slices = [slice(None, None, None) for i in \
xrange(len(template_shape))]
slices = [slice(None, None, None) for i in
xrange(len(template_shape))]
slices[axis] = slice(curpos, curpos + curlen, None)
return tuple(slices)
......@@ -3829,23 +3830,22 @@ class GpuAlloc(GpuAllocEmpty):
# If the output is a constant, it will have to be deepcopied
# each time the function is called. So we do not fold.
return False
elif ( # The following ops work inplace of their input id 0.
client[1] == 0 and
isinstance(client[0].op, (
# Ops that will work inplace on the Alloc. So if they
# get constant_folded, they would copy the
# constant and this is less efficients.
# Not doing the constant folding could also lower
# the peak memory usage, as we the "constant" won't
# always exists.
# theano.tensor.subtensor.AdvancedIncSubtensor,
GpuIncSubtensor,
GpuAdvancedIncSubtensor1,
theano.sandbox.cuda.blas.GpuGemm,
theano.sandbox.cuda.blas.GpuGemv,
theano.sandbox.cuda.blas.GpuGer,
))):
# Else if the following ops work inplace of their input id 0.
elif(client[1] == 0 and
isinstance(client[0].op, (
# Ops that will work inplace on the Alloc. So if they
# get constant_folded, they would copy the
# constant and this is less efficients.
# Not doing the constant folding could also lower
# the peak memory usage, as we the "constant" won't
# always exists.
# theano.tensor.subtensor.AdvancedIncSubtensor,
GpuIncSubtensor,
GpuAdvancedIncSubtensor1,
theano.sandbox.cuda.blas.GpuGemm,
theano.sandbox.cuda.blas.GpuGemv,
theano.sandbox.cuda.blas.GpuGer,))):
return False
# If the clients is a transfer, we don't want to fold. We
# let the moving opt finish before deciding what to do.
......@@ -3859,7 +3859,7 @@ gpu_alloc = GpuAlloc()
class CopyOnNegativeStrides(GpuOp):
"""
Checks if the input has contains negative strides.
If it does, returns a c contiguous copy.
"""
......@@ -4017,7 +4017,7 @@ def scalar(name=None, dtype=None):
Parameters
----------
dtype
dtype
Numeric type (None means to use theano.config.floatX).
name : str
A name to attach to this variable.
......
theano/sandbox/cuda/blas.py
浏览文件 @ b69ad54d
from __future__ import absolute_import, print_function, division
import copy
import os
import logging
_logger = logging.getLogger(__name__)
from six import integer_types
from six.moves import StringIO, reduce
import theano
from theano import Apply
from theano import tensor
......@@ -15,6 +11,7 @@ from theano.sandbox.cuda import GpuOp
from theano.sandbox.cuda.basic_ops import (as_cuda_ndarray_variable,
gpu_contiguous)
from theano.tensor import as_tensor_variable
_logger = logging.getLogger(__name__)
class GpuBatchedDot(GpuOp):
......@@ -29,11 +26,11 @@ class GpuBatchedDot(GpuOp):
assert inp1.dtype == "float32"
assert inp2.dtype == "float32"
assert inp1.ndim == 3 # (batch, a, b)
assert inp1.ndim == 3 # (batch, a, b)
assert inp2.ndim == 3
return theano.Apply(self, [inp1, inp2],
[self.output_type(inp1, inp2)()])
[self.output_type(inp1, inp2)()])
def output_type(self, inp1, inp2):
return CudaNdarrayType(
......@@ -183,8 +180,7 @@ class GpuBatchedDot(GpuOp):
}
} else {
// copy inputs if not contiguous
""" +
("\n".join("""
""" + ("\n".join("""
if (( CudaNdarray_HOST_DIMS(%(var)s)[0] > 1 && CudaNdarray_HOST_STRIDES(%(var)s)[0] != 1
&& CudaNdarray_HOST_DIMS(%(var)s)[1] > 1 && CudaNdarray_HOST_STRIDES(%(var)s)[1] != 1
&& CudaNdarray_HOST_DIMS(%(var)s)[2] > 1 && CudaNdarray_HOST_STRIDES(%(var)s)[2] != 1)
......@@ -198,8 +194,7 @@ class GpuBatchedDot(GpuOp):
Py_XDECREF(%(var)s);
%(var)s = _copy;
}
""" % dict(var=var, fail=fail) for var in (bx, by)))
+ """
""" % dict(var=var, fail=fail) for var in (bx, by))) + """
// fail if the output is not contiguous; we can't copy it because we
// need to write to the original memory
......@@ -363,7 +358,7 @@ class GpuDot22(GpuOp):
if y.type.ndim != 2:
raise TypeError(y)
otype = CudaNdarrayType(
(x.type.broadcastable[0], y.type.broadcastable[1]))
(x.type.broadcastable[0], y.type.broadcastable[1]))
return Apply(self, [x, y], [otype()])
def c_code_cache_version(self):
......@@ -451,7 +446,7 @@ class GpuDot22Scalar(GpuOp):
if not tensor.blas._as_scalar(a):
raise TypeError(a)
otype = CudaNdarrayType(
(x.type.broadcastable[0], y.type.broadcastable[1]))
(x.type.broadcastable[0], y.type.broadcastable[1]))
return Apply(self, [x, y, a], [otype()])
def c_code_cache_version(self):
......@@ -537,8 +532,8 @@ class GpuGemm(GpuOp):
return 'GpuGemm{no_inplace}'
def __eq__(self, other):
return (type(self) == type(other)\
and self.inplace == other.inplace)
return (type(self) == type(other) and
self.inplace == other.inplace)
def __hash__(self):
return hash(type(self)) ^ hash(self.inplace)
......@@ -562,7 +557,7 @@ class GpuGemm(GpuOp):
return (4,)
def c_code(self, node, name, inputs, outputs, sub):
#z_out = alpha * dot(x,y) + beta * z_in
# z_out = alpha * dot(x,y) + beta * z_in
# inplace version, set set z_out = z_in
# not inplace version, we copy z_in to z_out.
z_in, a, x, y, b = inputs
......@@ -657,8 +652,8 @@ class GpuGemv(GpuOp):
return 'GpuGemv{no_inplace}'
def __eq__(self, other):
return (type(self) == type(other)\
and self.inplace == other.inplace)
return (type(self) == type(other) and
self.inplace == other.inplace)
def __hash__(self):
return hash(type(self)) ^ hash(self.inplace)
......@@ -682,7 +677,7 @@ class GpuGemv(GpuOp):
return (3,)
def c_code(self, node, name, inputs, outputs, sub):
#z_out = alpha * dot(x,y) + beta * z_in
# z_out = alpha * dot(x,y) + beta * z_in
# inplace version, set set z_out = z_in
# not inplace version, we copy z_in to z_out.
z_in, a, x, y, b = inputs
......@@ -757,8 +752,8 @@ class GpuGer(GpuOp):
return 'GpuGer{no_inplace}'
def __eq__(self, other):
return (type(self) == type(other)\
and self.inplace == other.inplace)
return (type(self) == type(other) and
self.inplace == other.inplace)
def __hash__(self):
return hash(type(self)) ^ hash(self.inplace)
......@@ -782,7 +777,7 @@ class GpuGer(GpuOp):
return (2,)
def c_code(self, node, name, inputs, outputs, sub):
#z_out = alpha * dot(x,y) + beta * z_in
# z_out = alpha * dot(x,y) + beta * z_in
# inplace version, set set z_out = z_in
# not inplace version, we copy z_in to z_out.
z_in, a, x, y = inputs
......@@ -934,7 +929,7 @@ class BaseGpuCorrMM(GpuOp):
# these files
files = ['corr_gemm.cu']
codes = [open(os.path.join(os.path.split(__file__)[0], f)).read()
for f in files]
for f in files]
return reduce(str.__add__, codes)
def c_code_helper(self, bottom, weights, top, direction, sub, height=None, width=None):
......@@ -947,7 +942,7 @@ class BaseGpuCorrMM(GpuOp):
Parameters
----------
bottom
bottom
Variable name of the input images in the forward pass,
or the gradient of the input images in backprop wrt. inputs
weights
......@@ -1001,7 +996,7 @@ class BaseGpuCorrMM(GpuOp):
out = bottom
else:
raise ValueError("direction must be one of 'forward', "
"'backprop weights', 'backprop inputs'")
"'backprop weights', 'backprop inputs'")
# When subsampling, we cannot unambiguously infer the height and width
# of bottom and weights from top, so we require them to be given.
# Similarly, when pad="half", we cannot infer the weight size.
......@@ -1158,7 +1153,7 @@ class GpuCorrMM(BaseGpuCorrMM):
Parameters
----------
border_mode
The width of a border of implicit zeros to pad the
The width of a border of implicit zeros to pad the
input with. Must be a tuple with 2 elements giving the numbers of rows
and columns to pad on each side, or a single integer to pad the same
on all sides, or a string shortcut setting the padding at runtime:
......@@ -1174,7 +1169,7 @@ class GpuCorrMM(BaseGpuCorrMM):
but faster.
Set to `(1, 1)` to disable subsampling.
pad
Deprecated alias for `border_mode`.
Deprecated alias for `border_mode`.
Notes
-----
......@@ -1247,8 +1242,8 @@ class GpuCorrMM_gradWeights(BaseGpuCorrMM):
"""
def __init__(self, border_mode="valid",
subsample=(1, 1),
pad=(0, 0)):
subsample=(1, 1),
pad=(0, 0)):
super(GpuCorrMM_gradWeights, self).__init__(border_mode, subsample, pad)
def make_node(self, img, topgrad, shape=None):
......@@ -1283,11 +1278,15 @@ class GpuCorrMM_gradWeights(BaseGpuCorrMM):
bottom, top = inp[:2]
weights, = grads
weights = gpu_contiguous(weights)
d_bottom = GpuCorrMM_gradInputs(self.border_mode, self.subsample)(
weights, top, bottom.shape[-2:])
d_top = GpuCorrMM(self.border_mode, self.subsample)(
bottom, weights)
d_height_width = (theano.gradient.DisconnectedType()(),) * 2 if len(inp) == 4 else ()
d_bottom = GpuCorrMM_gradInputs(
self.border_mode, self.subsample)(weights,
top,
bottom.shape[-2:])
d_top = GpuCorrMM(
self.border_mode, self.subsample)(bottom, weights)
d_height_width = (
theano.gradient.DisconnectedType()(),
) * 2 if len(inp) == 4 else ()
return (d_bottom, d_top) + d_height_width
def connection_pattern(self, node):
......@@ -1309,8 +1308,8 @@ class GpuCorrMM_gradInputs(BaseGpuCorrMM):
"""
def __init__(self, border_mode="valid",
subsample=(1, 1),
pad=(0, 0)):
subsample=(1, 1),
pad=(0, 0)):
super(GpuCorrMM_gradInputs, self).__init__(border_mode, subsample, pad)
def make_node(self, kern, topgrad, shape=None):
......@@ -1342,11 +1341,14 @@ class GpuCorrMM_gradInputs(BaseGpuCorrMM):
weights, top = inp[:2]
bottom, = grads
bottom = gpu_contiguous(bottom)
d_weights = GpuCorrMM_gradWeights(self.border_mode, self.subsample)(
d_weights = GpuCorrMM_gradWeights(
self.border_mode, self.subsample)(
bottom, top, weights.shape[-2:])
d_top = GpuCorrMM(self.border_mode, self.subsample)(
bottom, weights)
d_height_width = (theano.gradient.DisconnectedType()(),) * 2 if len(inp) == 4 else ()
d_top = GpuCorrMM(
self.border_mode, self.subsample)(bottom, weights)
d_height_width = (
theano.gradient.DisconnectedType()(),
) * 2 if len(inp) == 4 else ()
return (d_weights, d_top) + d_height_width
def connection_pattern(self, node):
......@@ -1412,7 +1414,7 @@ class BaseGpuCorr3dMM(GpuOp):
# these files
files = ['corr3d_gemm.cu']
codes = [open(os.path.join(os.path.split(__file__)[0], f)).read()
for f in files]
for f in files]
return reduce(str.__add__, codes)
def c_code_helper(self, bottom, weights,
......@@ -1459,7 +1461,7 @@ class BaseGpuCorr3dMM(GpuOp):
If self.pad == 'half', a variable giving the width of the filters
for direction="backprop weights".
Ignored otherwise.
depth
depth
If self.subsample[2] != 1, a variable giving the depth
of the filters for direction="backprop weights" or the depth of the
input images for direction="backprop inputs".
......@@ -1488,7 +1490,7 @@ class BaseGpuCorr3dMM(GpuOp):
out = bottom
else:
raise ValueError("direction must be one of 'forward', "
"'backprop weights', 'backprop inputs'")
"'backprop weights', 'backprop inputs'")
# When subsampling, we cannot unambiguously infer the height and width
# of bottom and weights from top, so we require them to be given.
# Similarly, when pad="half", we cannot infer the weight size.
......@@ -1755,10 +1757,16 @@ class GpuCorr3dMM(BaseGpuCorr3dMM):
bottom, weights = inp
top, = grads
top = gpu_contiguous(top)
d_bottom = GpuCorr3dMM_gradInputs(self.border_mode, self.subsample, self.pad)(
weights, top, bottom.shape[-3:])
d_weights = GpuCorr3dMM_gradWeights(self.border_mode, self.subsample, self.pad)(
bottom, top, weights.shape[-3:])
d_bottom = GpuCorr3dMM_gradInputs(self.border_mode,
self.subsample,
self.pad)(weights,
top,
bottom.shape[-3:])
d_weights = GpuCorr3dMM_gradWeights(self.border_mode,
self.subsample,
self.pad)(bottom,
top,
weights.shape[-3:])
return d_bottom, d_weights
......@@ -1863,11 +1871,14 @@ class GpuCorr3dMM_gradInputs(BaseGpuCorr3dMM):
weights, top = inp[:2]
bottom, = grads
bottom = gpu_contiguous(bottom)
d_weights = GpuCorr3dMM_gradWeights(self.border_mode, self.subsample, self.pad)(
bottom, top, weights.shape[-3:])
d_top = GpuCorr3dMM(self.border_mode, self.subsample, self.pad)(
d_weights = GpuCorr3dMM_gradWeights(
self.border_mode, self.subsample, self.pad)(
bottom, top, weights.shape[-3:])
d_top = GpuCorr3dMM(
self.border_mode, self.subsample, self.pad)(
bottom, weights)
d_height_width_depth = (theano.gradient.DisconnectedType()(),) * 3 if len(inp) == 5 else ()
d_height_width_depth = (theano.gradient.DisconnectedType()(),)\
* 3 if len(inp) == 5 else ()
return (d_weights, d_top) + d_height_width_depth
def connection_pattern(self, node):
......@@ -1938,19 +1949,19 @@ class GpuConv(GpuOp):
raise ValueError(mode)
def __init__(self, border_mode,
subsample=(1, 1),
logical_img_hw=None,
logical_kern_hw=None,
logical_kern_align_top=True,
version=-1,
direction_hint=None,
verbose=0,
kshp=None,
imshp=None,
max_threads_dim0=None,
nkern=None,
bsize=None,
fft_opt=True):
subsample=(1, 1),
logical_img_hw=None,
logical_kern_hw=None,
logical_kern_align_top=True,
version=-1,
direction_hint=None,
verbose=0,
kshp=None,
imshp=None,
max_threads_dim0=None,
nkern=None,
bsize=None,
fft_opt=True):
self.border_mode = border_mode
if version != -1:
raise Exception(
......@@ -2107,7 +2118,7 @@ class GpuConv(GpuOp):
# these files
files = ['conv_kernel.cu', 'conv_full_kernel.cu', 'conv.cu']
codes = [open(os.path.join(os.path.split(__file__)[0], f)).read()
for f in files]
for f in files]
return reduce(str.__add__, codes)
def c_code(self, node, nodename, inp, out_, sub):
......@@ -2186,7 +2197,7 @@ class GpuDownsampleFactorMax(GpuOp):
return Apply(self, [x], [x.type()])
# def perform(self, node, input_storage, output_storage):
#raise NotImplementedError('only C is implemented')
# raise NotImplementedError('only C is implemented')
def c_code_cache_version(self):
return (6)
......
theano/sandbox/cuda/elemwise.py
浏览文件 @ b69ad54d
......@@ -97,7 +97,7 @@ class NaiveAlgo(object):
self.scalar_op.__class__.__name__, nodename, nd), file=sio)
if (nd):
print("\t,", ", ".join("const int dim%i" % i
for i in xrange(nd)), file=sio)
for i in xrange(nd)), file=sio)
# declare inputs
for ipos, i in enumerate(node.inputs):
s = ", ".join(["const float * i%i_data" % ipos] +
......@@ -108,8 +108,8 @@ class NaiveAlgo(object):
s = ", ".join(["float * o%i_data" % ipos] +
["int o%i_str_%i" % (ipos, d) for d in xrange(nd)])
print("\t,", s, file=sio)
#print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
#print >> sio, "\t,", "float * o%i_data" % ipos
# print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
# print >> sio, "\t,", "float * o%i_data" % ipos
print("\t)\n{", file=sio)
print(" const int idx = blockIdx.x * blockDim.x + threadIdx.x;", file=sio)
print(" const int numThreads = blockDim.x * gridDim.x;", file=sio)
......@@ -129,7 +129,7 @@ class NaiveAlgo(object):
print(" const float * ii_i%i_data = i%i_data;" % (ipos, ipos), file=sio)
for ipos, i in enumerate(node.outputs):
print(" float * ii_o%i_data = o%i_data;" % (ipos, ipos), file=sio)
for d in xrange(nd-1, -1, -1):
for d in xrange(nd - 1, -1, -1):
if d > 0:
print(" int pos%i = ii %% dim%i;" % (d, d), file=sio)
print(" ii = ii / dim%i;" % d, file=sio)
......@@ -161,9 +161,9 @@ class NaiveAlgo(object):
print("ii_o%i_data[0] = o%i_i;" % (ipos, ipos), file=sio)
print(" }", file=sio)
#indent = " "*(4*d+7)
# indent = " "*(4*d+7)
# for ipos, i in enumerate(node.inputs):
#print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
# print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
print("}", file=sio)
# print sio.getvalue()
......@@ -211,10 +211,11 @@ class NaiveAlgo(object):
print("// Input ", ipos, str(i.type), file=sio)
for ipos, i in enumerate(node.outputs):
print("// Output ", ipos, str(i.type), file=sio)
print("static __global__ void kernel_%s_%s_%s(unsigned int numEls" % (
self.scalar_op.__class__.__name__,
nodename,
'tiling%i'%nd), file=sio)
print(
"static __global__ void kernel_%s_%s_%s(unsigned int numEls" %
(self.scalar_op.__class__.__name__,
nodename,
'tiling%i' % nd), file=sio)
if (nd):
print("\t,", ", ".join("const int dim%i" % i for i in xrange(nd)), file=sio)
# declare inputs
......@@ -225,15 +226,15 @@ class NaiveAlgo(object):
for ipos, i in enumerate(node.outputs):
s = ", ".join(["float * o%i_data" % ipos] + list("int o%i_str_%i" % (ipos, d) for d in xrange(nd)))
print("\t,", s, file=sio)
#print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
#print >> sio, "\t,", "float * o%i_data" % ipos
# print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
# print >> sio, "\t,", "float * o%i_data" % ipos
print("\t)\n{", file=sio)
# For each input that is a scalar which has been broadcasted to a tensor,
# load it into a local variable
print(" __shared__ float value0[%i];" % len(node.inputs), file=sio)
print(" __shared__ int shared_dims[%(nd)s];" % locals(), file=sio)
#print >> sio, " __shared__ int shared_i_str[%(n_in)s][%(nd)s]"
# print >> sio, " __shared__ int shared_i_str[%(n_in)s][%(nd)s]"
print(" if ((threadIdx.x == 0) && (threadIdx.y == 0)) {", file=sio)
for ipos, i in enumerate(node.inputs):
if _logical_scalar(i):
......@@ -274,15 +275,18 @@ class NaiveAlgo(object):
# perform the scalar operation on the input and output references
# TODO: What if the scalar_op needs support_code??
task_code = self.scalar_op.c_code(
Apply(self.scalar_op,
[scalar.Scalar(dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(dtype=output.type.dtype).make_variable()
for output in node.outputs])
, nodename + '_scalar_'
, get_str_list_logical_scalar(node, value_str='value0[%i]')
, ['ii_o%i_data[0]'%ipos for ipos, i in enumerate(node.outputs)]
, sub=dict(fail='return;')) # TODO: set a failure code somehow!!!
Apply(
self.scalar_op,
[scalar.Scalar(
dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(
dtype=output.type.dtype).make_variable()
for output in node.outputs]),
nodename + '_scalar_',
get_str_list_logical_scalar(node, value_str='value0[%i]'),
['ii_o%i_data[0]' % ipos for ipos, i in enumerate(node.outputs)],
sub=dict(fail='return;')) # TODO: set a failure code somehow!!!
print(" ", task_code, file=sio)
print(" }" * nd, file=sio)
......@@ -290,9 +294,9 @@ class NaiveAlgo(object):
# TODO: insert runtime stride checks that select the best loop order either here, or in
# the host code that launched the kernel (host code probably better spot)
#indent = " "*(4*d+7)
# indent = " "*(4*d+7)
# for ipos, i in enumerate(node.inputs):
#print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
# print >> sio, indent, "const float * i%i" % ipos, '= i%i_data', ''
print("}", file=sio)
print(sio.getvalue())
......@@ -319,10 +323,11 @@ class NaiveAlgo(object):
print("// Input ", ipos, str(i.type), file=sio)
for ipos, i in enumerate(node.outputs):
print("// Output ", ipos, str(i.type), file=sio)
print("static __global__ void kernel_%s_%s_%s(unsigned int numEls" % (
self.scalar_op.__class__.__name__,
nodename,
'tiling%i_less_registers'%nd), file=sio)
print(
"static __global__ void kernel_%s_%s_%s(unsigned int numEls" %
(self.scalar_op.__class__.__name__,
nodename,
'tiling%i_less_registers' % nd), file=sio)
if (nd):
print("\t,", ", ".join("const int dim%i" % i for i in xrange(nd)), file=sio)
# declare inputs
......@@ -333,8 +338,8 @@ class NaiveAlgo(object):
for ipos, i in enumerate(node.outputs):
s = ", ".join(["float * o%i_data_0" % ipos] + list("int o%i_str_%i" % (ipos, d) for d in xrange(nd)))
print("\t,", s, file=sio)
#print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
#print >> sio, "\t,", "float * o%i_data" % ipos
# print >> sio, "\t,", ", ".join("int o%i_str_%i" % (ipos, d) for d in xrange(nd))
# print >> sio, "\t,", "float * o%i_data" % ipos
print("\t)\n{", file=sio)
# TODO: Setting these to true makes the function fail SOMETIMES. I don't know why yet.
......@@ -350,6 +355,7 @@ class NaiveAlgo(object):
return "s%s_str[%i][%i]" % (io, p, d)
else:
return "%s%i_str_%i" % (io, p, d)
def limits(d):
if use_shared_limits:
return "limits[%i]" % d
......@@ -417,15 +423,19 @@ class NaiveAlgo(object):
def task_code(d):
print(self.scalar_op.c_code(
Apply(self.scalar_op,
Apply(
self.scalar_op,
[scalar.Scalar(dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(dtype=output.type.dtype).make_variable()
for output in node.outputs])
, nodename + '_scalar_'
, ['i%i_data_%i[0]'%(ipos, d) for ipos, i in enumerate(node.inputs)]
, ['o%i_data_%i[0]'%(ipos, d) for ipos, i in enumerate(node.outputs)]
, sub=dict(fail='return;')), file=sio) # TODO: set a failure code somehow!!!
for output in node.outputs]),
nodename + '_scalar_',
['i%i_data_%i[0]' % (ipos, d) for ipos,
i in enumerate(node.inputs)],
['o%i_data_%i[0]' % (ipos, d) for ipos,
i in enumerate(node.outputs)],
sub=dict(fail='return;')), file=sio)
# TODO: set a failure code somehow!!!
if nd == 4:
decl_shared_stride(n_in, n_out, nd)
......@@ -495,16 +505,19 @@ class NaiveAlgo(object):
for ipos, i in enumerate(node.outputs):
print("npy_%s o%d_i;" % (i.dtype, ipos), file=sio)
task_code = self.scalar_op.c_code(
Apply(self.scalar_op,
[scalar.Scalar(dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(dtype=output.type.dtype).make_variable()
for output in node.outputs])
, nodename + '_scalar_'
#, ['i%i_data[i]'%ipos for ipos, i in enumerate(node.inputs)]
, get_str_list_logical_scalar(node, data_str='i%i_data[i]')
, ['o%i_i'%ipos for ipos, i in enumerate(node.outputs)]
, sub=dict(fail='return;')) # TODO: set a failure code somehow!!!
Apply(
self.scalar_op,
[scalar.Scalar(dtype=input.type.dtype).make_variable()
for input in node.inputs],
[scalar.Scalar(dtype=output.type.dtype).make_variable()
for output in node.outputs]),
nodename + '_scalar_',
# , ['i%i_data[i]'%ipos for ipos,
# i in enumerate(node.inputs)]
get_str_list_logical_scalar(node, data_str='i%i_data[i]'),
['o%i_i' % ipos for ipos, i in enumerate(node.outputs)],
sub=dict(fail='return;'))
# TODO: set a failure code somehow!!!
print(" ", task_code, file=sio)
for ipos, _ in enumerate(node.outputs):
print("o%i_data[i] = o%i_i;" % (ipos, ipos), file=sio)
......@@ -539,18 +552,21 @@ class NaiveAlgo(object):
nb_outputs = len(node.outputs)
d = dict()
# input_params and output_params go into the function declaration/definition
input_params = ", ".join("const float * i%i_data, const int * i%i_str"%(ipos, ipos)
for ipos in xrange(len(node.inputs)))
output_params = ", ".join("float * o%i_data, const int * o%i_str"%(ipos, ipos)
for ipos in xrange(len(node.outputs)))
input_params = ", ".join(
"const float * i%i_data, const int * i%i_str" % (ipos, ipos)
for ipos in xrange(len(node.inputs)))
output_params = ", ".join(
"float * o%i_data, const int * o%i_str" % (ipos, ipos)
for ipos in xrange(len(node.outputs)))
# input_args and output_args go into the recursive call.
input_args = ", ".join("i%i_data, i%i_str"%(ipos, ipos)
for ipos in xrange(len(node.inputs)))
output_args = ", ".join("o%i_data, o%i_str"%(ipos, ipos)
for ipos in xrange(len(node.outputs)))
input_args = ", ".join("i%i_data, i%i_str" % (ipos, ipos)
for ipos in xrange(len(node.inputs)))
output_args = ", ".join("o%i_data, o%i_str" % (ipos, ipos)
for ipos in xrange(len(node.outputs)))
prod_dims = '*'.join(["dims[%i]"%di for di in xrange(nd)]+['1'])
prod_dims = '*'.join(
["dims[%i]" % di for di in xrange(nd)] + ['1'])
scalar_op = self.scalar_op.__class__.__name__
......@@ -578,20 +594,30 @@ class NaiveAlgo(object):
print("""
std::cerr << "calling kernel_%(scalar_op)s_%(nodename)s w numEls" << numEls << " dims"<< d << "\\n";
""" % locals(), file=sio)
print('std::cerr << ' + " << ' ' << ".join(['" "']+list("dims[%i]"%di
for di in xrange(nd)) + ["'\\n';"]), file=sio)
print(
'std::cerr << ' + " << ' ' << ".join(
['" "'] +
list("dims[%i]" % di for di in xrange(nd)) +
["'\\n';"]),
file=sio)
if self.verbose > 1:
for ipos in xrange(len(node.inputs)):
istrings = [
"i%s_str[%i]" % (ipos, di) for di in xrange(nd)]
ipositions = " << ' ' << ".join(
["i%s_data" % ipos] + istrings)
print("""
std::cerr << " %(ipos)s data strides" <<
""" % locals() + " << ' ' << ".join(["i%s_data"%ipos]
+ list("i%s_str[%i]"%(ipos, di) for di in xrange(nd))) + ''' << "\\n"; ''', file=sio)
std::cerr << " %(ipos)s data strides" << %(ipositions)s << "\\n";
""" % dict(ipos=ipos, ipositions=ipositions), file=sio)
for ipos in xrange(len(node.outputs)):
print("""
std::cerr << " %(ipos)s data strides" <<
""" % locals() + " << ' ' << ".join(["o%s_data"%ipos]
+ list("o%s_str[%i]"%(ipos, di) for di in xrange(nd))) + ''' << "\\n"; ''', file=sio)
""" % locals() + " << ' ' << ".join(
["o%s_data" % ipos] +
list(
"o%s_str[%i]" % (ipos, di) for di in xrange(nd)
)) +
''' << "\\n"; ''', file=sio)
# collapse dimension that are broadcast in all inputs.
# need to be done before contiguous collapse as it will break it.
# do the dimensions and the strides
......@@ -636,11 +662,19 @@ class NaiveAlgo(object):
print('std::cerr << "\\n";', file=sio)
if nd > 0:
for ipos in xrange(len(node.inputs)):
print('std::cerr << " local_str inputs %(ipos)s: " <<'%locals() + \
' << " " << '.join(["local_str[%s][%s]" % (ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print(
'std::cerr << " local_str inputs %(ipos)s: " <<' % locals() +
' << " " << '.join(["local_str[%s][%s]" % (ipos, x)
for x in xrange(nd)]) +
'<<"\\n";', file=sio)
for ipos in xrange(len(node.outputs)):
print('std::cerr << " local_ostr inputs %(ipos)s: " <<'%locals() + \
' << " " << '.join(["local_ostr[%s][%s]" % (ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print(
'std::cerr << " local_ostr inputs %(ipos)s: " <<' %
locals() +
' << " " << '.join(
["local_ostr[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
print("""
for(int id=0;id<nd_collapse;id++){
......@@ -668,35 +702,51 @@ class NaiveAlgo(object):
nd_collapse--; id--;
}
}
"""%locals(), file=sio)
""" % locals(), file=sio)
if self.verbose > 2:
print('std::cerr <<"after broadcast collapse\\n";', file=sio)
print('std::cerr<< "nd_collapse "<< nd_collapse << "\\n"; ', file=sio)
print('std::cerr << "local_dims";', file=sio)
for d in xrange(nd):
print('std::cerr << " " << local_dims[%(d)s]; '%locals(), file=sio)
print('std::cerr << " " << local_dims[%(d)s]; ' %
locals(), file=sio)
print('std::cerr << "\\n";', file=sio)
if nd > 0:
for ipos in xrange(len(node.inputs)):
print('std::cerr << " local_str %(ipos)s: " <<'%locals()+' << " " << '.join(["local_str[%s][%s]" % (ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print('std::cerr << " local_str %(ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_str[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
for ipos in xrange(len(node.outputs)):
print('std::cerr << " local_ostr %(ipos)s: " <<'%locals()+' << " " << '.join(["local_ostr[%s][%s]" % (ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print(
'std::cerr << " local_ostr %(ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_ostr[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
# collapse contiguous dimensions (ignoring scalars, generic version(collapse any dimensions, right, left, middle))
# this is a good idea because we make less index calculation in the gpu.
if nd > 0:
print("int nd_collapse_[%(nd)s] = {"%locals() + ','.join(['1' for x in xrange(nd)]) + "};", file=sio)
print("int nd_collapse_[%(nd)s] = {" %
locals() + ','.join(
['1' for x in xrange(nd)]) + "};", file=sio)
else:
print("int *nd_collapse_ = NULL;", file=sio)
for ipos in xrange(len(node.inputs)):
if not _logical_scalar(node.inputs[ipos]):
if nd > 0:
print("""
int nd_collapse_%(ipos)s[%(nd)s] = {"""%locals() + ','.join(['1' for x in xrange(nd)]) + "};", file=sio)
int nd_collapse_%(ipos)s[%(nd)s] = {""" %
locals() +
','.join(['1' for x in xrange(nd)]) +
"};", file=sio)
else:
print("""
int *nd_collapse_%(ipos)s = NULL;"""%locals(), file=sio)
int * nd_collapse_%(ipos)s = NULL;""" %
locals(), file=sio)
print("""
can_collapse_%(nodename)s(nd_collapse, local_dims, local_str[%(ipos)s], nd_collapse_%(ipos)s);
for(int i=0;i<nd_collapse;i++){
......@@ -707,8 +757,10 @@ nd_collapse_[i]=0;
if self.verbose > 1:
print("""
std::cerr<< "nd_collapse_%(ipos)s "<<
"""%locals(), file=sio)
print(' << " " << '.join(["nd_collapse_%s[" % ipos + str(i)+"]" for i in xrange(nd)]), file=sio)
""" % locals(), file=sio)
print(' << " " << '.join(["nd_collapse_ %s[" %
ipos + str(i) + "]" for i in xrange(nd)]),
file=sio)
print('<< "\\n";', file=sio)
# update the local stride.
......@@ -721,7 +773,7 @@ nd_collapse_[i]=0;
local_str[%(ipos)s][j-1]=local_str[%(ipos)s][j];
}
}
"""%locals(), file=sio)
""" % locals(), file=sio)
for ipos in xrange(len(node.outputs)):
print("""
......@@ -732,7 +784,7 @@ nd_collapse_[i]=0;
local_ostr[%(ipos)s][j-1]=local_ostr[%(ipos)s][j];
}
}
"""%locals(), file=sio)
""" % locals(), file=sio)
# update the local dims.
print("""
......@@ -743,16 +795,20 @@ nd_collapse_[i]=0;
local_dims[j-1]=local_dims[j];
}
}
"""%locals(), file=sio)
""" % locals(), file=sio)
# update the new number of dim
print("""
for(int i=1, end=nd_collapse;i<end;i++){
if(nd_collapse_[i]==1)nd_collapse--;
}
if(nd_collapse == 1 """%locals(), file=sio)
l = ["local_str[%s][nd_collapse-1]==1 "%ipos for ipos in xrange(len(node.inputs)) if not _logical_scalar(node.inputs[ipos])]
l += ["local_ostr[%s][nd_collapse-1]==1 "%ipos for ipos in xrange(len(node.outputs)) if not _logical_scalar(node.outputs[ipos])]
if(nd_collapse == 1 """ % locals(), file=sio)
l = ["local_str[%s][nd_collapse-1]==1 " %
ipos for ipos in xrange(len(node.inputs)) if not
_logical_scalar(node.inputs[ipos])]
l += ["local_ostr[%s][nd_collapse-1]==1 " %
ipos for ipos in xrange(len(node.outputs)) if not
_logical_scalar(node.outputs[ipos])]
if len(l) > 0:
print(" && ", " && ".join(l), file=sio)
print("""){nd_collapse=0;} """, file=sio)
......@@ -762,20 +818,31 @@ nd_collapse_[i]=0;
print("""std::cerr << "nd_collapse " << nd_collapse << "\\n"; """ % locals(), file=sio)
if self.verbose > 1:
for d in xrange(nd):
print('std::cerr << " " << local_dims[%(d)s]; '%locals(), file=sio)
print('std::cerr << " " << local_dims[%(d)s]; ' %
locals(),
file=sio)
print('std::cerr << "\\n";', file=sio)
if nd > 0:
for ipos in xrange(len(node.inputs)):
print('std::cerr << " local_str %(ipos)s: " <<'%locals()+' << " " << '.join(["local_str[%s][%s]"%(ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print(
'std::cerr << " local_str % (ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_str[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
for ipos in xrange(len(node.outputs)):
print('std::cerr << " local_ostr %(ipos)s: " <<'%locals()+' << " " << '.join(["local_ostr[%s][%s]"%(ipos, x) for x in xrange(nd)])+'<<"\\n";', file=sio)
print('std::cerr << " local_ostr % (ipos)s: " <<' %
locals() + ' << " " << '.join(
["local_ostr[%s][%s]" %
(ipos, x) for x in xrange(nd)]) +
'<<"\\n";', file=sio)
def launch_Ccontiguous(nodename, scalar_op, sync=True):
kernel_call_args = ["numEls"]
for ipos in xrange(len(node.inputs)):
kernel_call_args.append("i%i_data"%ipos)
kernel_call_args.append("i%i_data" % ipos)
for ipos in xrange(len(node.outputs)):
kernel_call_args.append("o%i_data"%ipos)
kernel_call_args.append("o%i_data" % ipos)
kernel_call_args = ", ".join(kernel_call_args)
verb = ""
if self.verbose:
......@@ -817,20 +884,27 @@ nd_collapse_[i]=0;
# kernel_call_args are used to invoke the cuda kernel
local = "local_"
kernel_call_args = ["numEls"]
kernel_call_args.extend(local+"dims[%i]"%di for di in xrange(force_nd))
kernel_call_args.extend(
local + "dims[%i]" %
di for di in xrange(force_nd))
for ipos in xrange(len(node.inputs)):
kernel_call_args += ["i%i_data"%ipos] + list(local+"str[%i][%i]"%(ipos, di) for di in xrange(force_nd))
#strides = ", ".join("i%i_str[%i]"%(ipos, di) for di in xrange(force_nd))
#kernel_call_args.append( "%s, i%i_data" % (strides, ipos))
kernel_call_args += ["i%i_data" % ipos] + list(
local + "str[%i][%i]" %
(ipos, di) for di in xrange(force_nd))
# strides = ", ".join("i%i_str[%i]"%(ipos, di) for di in xrange(force_nd))
# kernel_call_args.append( "%s, i%i_data" % (strides, ipos))
for ipos in xrange(len(node.outputs)):
kernel_call_args += ["o%i_data"%ipos] + list(local+"ostr[%i][%i]"%(ipos, di) for di in xrange(force_nd))
#strides = ", ".join("o%i_str[%i]"%(ipos, di) for di in xrange(force_nd))
#kernel_call_args.append( "%s, o%i_data" % (strides, ipos))
kernel_call_args += ["o%i_data" % ipos] + list(
local + "ostr[%i][%i]" %
(ipos, di) for di in xrange(force_nd))
# strides = ", ".join("o%i_str[%i]"%(ipos, di) for di in xrange(force_nd))
# kernel_call_args.append( "%s, o%i_data" % (strides, ipos))
if self.verbose:
print("""
std::cerr << " Running general version with %(force_nd)s dims\\n";
"""%locals(), file=sio)
print("std::cerr << " + ' << " " << '.join(kernel_call_args)+' << "\\n";', file=sio)
""" % locals(), file=sio)
print("std::cerr << " + ' << " " << '.join(
kernel_call_args) + ' << "\\n";', file=sio)
# std::cerr << numEls << dims[0] << i0_data, i0_str[0] << o0_data, o0_str[0]\n;
kernel_call_args = ", ".join(kernel_call_args)
......@@ -866,12 +940,13 @@ nd_collapse_[i]=0;
else:
print(" return 0; " % locals(), file=sio)
print("if(numEls==0) return 0;", file=sio)
print("switch (nd_collapse==0?0:min(%(nd)s,nd_collapse)) {"%locals(), file=sio)
print("switch (nd_collapse==0?0:min(%(nd)s,nd_collapse)) {" %
locals(), file=sio)
print("case 0: {", file=sio)
launch_Ccontiguous(nodename, scalar_op, self.sync)
print(" } break;", file=sio)
for i in xrange(1, nd+1):
print("case "+str(i)+": {", file=sio)
for i in xrange(1, nd + 1):
print("case " + str(i) + ": {", file=sio)
launch_General(nodename, scalar_op, i, self.sync)
print(" } break;", file=sio)
......@@ -889,9 +964,10 @@ nd_collapse_[i]=0;
#define INTMOD_POW2(a, b) (a & ((1<<b)-1))
"""
kernels = "".join(
[self.c_src_kernel(node, nodename, x) for x in xrange(1, nd + 1)]
+ [self.c_src_kernel_Ccontiguous(node, nodename)]
+ [self.c_src_callkernel(node, nodename)])
[self.c_src_kernel(node, nodename, x)
for x in xrange(1, nd + 1)] +
[self.c_src_kernel_Ccontiguous(node, nodename)] +
[self.c_src_callkernel(node, nodename)])
return defines + kernels
def c_support_code(self):
......
theano/sandbox/cuda/fftconv.py
浏览文件 @ b69ad54d
......@@ -5,9 +5,9 @@ import numpy as np
import theano
import theano.tensor as T
from theano.misc.pycuda_init import pycuda_available
from theano.sandbox.cuda import cuda_available, GpuOp
from theano.ifelse import ifelse
from theano.misc.pycuda_init import pycuda_available
if cuda_available:
from theano.sandbox.cuda import (basic_ops, CudaNdarrayType,
......@@ -448,7 +448,7 @@ def conv2d_fft(input, filters, image_shape=None, filter_shape=None,
o1 = i1 + 1
input_padded = T.zeros((b, ic, o0, o1), dtype='float32')
input_padded = T.set_subtensor(input_padded[:, :, :i0, :i1],
input)
input)
else:
o1 = i1
input_padded = input
......@@ -523,9 +523,11 @@ def conv2d_fft(input, filters, image_shape=None, filter_shape=None,
# special way because we specify explicitly here
# how much values are expected.
if border_mode == 'valid':
output = output_circ[:, :, (f0-1):(f0-1 + i0-f0+1), (f1-1):(f1-1 + i1-f1+1)]
output = output_circ[:, :, (f0 - 1):(f0 - 1 + i0 - f0 + 1),
(f1 - 1):(f1 - 1 + i1 - f1 + 1)]
elif border_mode == 'full':
output = output_circ[:, :, (f0-1):(f0-1 + i0+f0-1), (f1-1):(f1-1 + i1+f1-1)]
output = output_circ[:, :, (f0 - 1):(f0 - 1 + i0 + f0 - 1),
(f1 - 1):(f1 - 1 + i1 + f1 - 1)]
else:
raise ValueError('invalid mode')
......@@ -655,7 +657,7 @@ def conv3d_fft(input, filters, image_shape=None, filter_shape=None,
output_fft_s = mult_and_reduce(input_fft_v, filters_fft_v,
input_shape=input_fft_v_shape,
filter_shape=filters_fft_v_shape)
#output_fft_s = input_fft_v
# output_fft_s = input_fft_v
# reshape for IFFT
output_fft_flat = output_fft_s.reshape((b * oc, o0, o1, o2 // 2 + 1, 2))
......@@ -673,12 +675,16 @@ def conv3d_fft(input, filters, image_shape=None, filter_shape=None,
# special way because we specify explicitly here
# how much values are expected.
if border_mode == 'valid':
output = output_circ[:, :, (f0-1):(f0-1 + i0-f0+1), (f1-1):(f1-1 + i1-f1+1), (f2-1):(f2-1 + i2-f2+1)]
output = output_circ[:, :, (f0 - 1):(f0 - 1 + i0 - f0 + 1),
(f1 - 1):(f1 - 1 + i1 - f1 + 1),
(f2 - 1):(f2 - 1 + i2 - f2 + 1)]
elif border_mode == 'full':
output = output_circ[:, :, (f0-1):(f0-1 + i0+f0-1), (f1-1):(f1-1 + i1+f1-1), (f2-1):(f2-1 + i2+f2-1)]
output = output_circ[:, :, (f0 - 1):(f0 - 1 + i0 + f0 - 1),
(f1 - 1):(f1 - 1 + i1 + f1 - 1),
(f2 - 1):(f2 - 1 + i2 + f2 - 1)]
else:
raise ValueError('invalid mode')
#output = output_circ[:, :, :, :, :]
# output = output_circ[:, :, :, :, :]
# Rescale manually. This is just a factor that comes in during the
# trip through FFT and inverse FFT.
......
theano/sandbox/cuda/kernel_codegen.py
浏览文件 @ b69ad54d
......@@ -76,7 +76,7 @@ def inline_reduce(N, buf, pos, count, manner_fn):
rest of the buffer is trashed by this function.
Notes
-----
-----
buf should be in gpu shared memory, we access it many times.
"""
......@@ -167,29 +167,26 @@ def inline_softmax(N, buf, buf2, threadPos, threadCount):
We use __i as an int variable in a loop.
"""
return [
# get max of buf (trashing all but buf[0])
inline_reduce_max(N, buf, threadPos, threadCount),
'__syncthreads()',
'float row_max = ' + buf + '[0]',
'__syncthreads()',
'for(int __i=' + threadPos + '; __i<' + N +
'; __i+=' + threadCount + '){',
buf + '[__i] = exp(' + buf2 + '[__i] - row_max)',
buf2 + '[__i] = ' + buf + '[__i]',
'}',
'__syncthreads()',
inline_reduce_sum(N, buf, threadPos, threadCount),
'__syncthreads()',
'float row_sum = ' + buf + '[0]',
'__syncthreads()',
# divide each exp() result by the sum to complete the job.
'for(int __i=' + threadPos + '; __i<' + N +
'; __i+=' + threadCount + '){',
buf + '[__i] = ' + buf2 + '[__i] / row_sum',
'}',
'__syncthreads()',
]
return [ # get max of buf (trashing all but buf[0])
inline_reduce_max(N, buf, threadPos, threadCount),
'__syncthreads()',
'float row_max = ' + buf + '[0]',
'__syncthreads()',
'for(int __i=' + threadPos + '; __i<' + N + '; __i+=' +
threadCount + '){',
buf + '[__i] = exp(' + buf2 + '[__i] - row_max)',
buf2 + '[__i] = ' + buf + '[__i]', '}',
'__syncthreads()',
inline_reduce_sum(N, buf, threadPos, threadCount),
'__syncthreads()',
'float row_sum = ' + buf + '[0]',
'__syncthreads()',
# divide each exp() result by the sum to complete the job.
'for(int __i=' + threadPos + '; __i<' + N +
'; __i+=' + threadCount + '){',
buf + '[__i] = ' + buf2 + '[__i] / row_sum', '}',
'__syncthreads()',
]
@code_version((1,))
......@@ -241,8 +238,7 @@ def inline_reduce_fixed_shared(N, buf, x, stride_x, pos, count,
init = manner_init("%(x)s[%(pos)s * %(stride_x)s]" % locals())
loop_line = manner_fn("red", manner_init("%(x)s[i * %(stride_x)s]" %
locals()))
loop_line2 = manner_fn("%s[%s]" % (buf, pos),
"%s[i]" % buf)
loop_line2 = manner_fn("%s[%s]" % (buf, pos), "%s[i]" % buf)
r_16 = manner_fn("%s[%s]" % (buf, pos), "%s[%s+16]" % (buf, pos))
r_8 = manner_fn("%s[%s]" % (buf, pos), "%s[%s+8]" % (buf, pos))
r_4 = manner_fn("%s[%s]" % (buf, pos), "%s[%s+4]" % (buf, pos))
......
theano/sandbox/cuda/neighbours.py
浏览文件 @ b69ad54d
from __future__ import absolute_import, print_function, division
# This is work in progress
from theano import Op, Apply, tensor
from theano import Apply, tensor
from theano.gof import local_optimizer
from theano.sandbox.cuda import cuda_available, GpuOp
......
theano/sandbox/cuda/nnet.py
浏览文件 @ b69ad54d
......@@ -578,45 +578,46 @@ class GpuSoftmax(GpuOp):
""" % locals()
def c_support_code_apply(self, node, nodename):
ret1 = nvcc_kernel("kSoftmax_%s" % nodename,
params=['int M', 'int N',
'const float * x', 'const int sx0', 'const int sx1',
'float * sm', 'const int sm_s0', 'const int sm_s1'],
body=[
"extern __shared__ float buf[]",
"float * buf2 = buf + N",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
"buf[tx] = x[blockIDX * sx0 + tx * sx1]",
"buf2[tx] = buf[tx]",
"}",
"__syncthreads()",
inline_softmax('N', 'buf', 'buf2',
'threadIdx.x', 'blockDim.x'),
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
# This set all value correctly
"sm[blockIDX * sm_s0 + tx * sm_s1] = buf[tx]",
"}",
"__syncthreads()",
"}",
])
ret2 = nvcc_kernel("kSoftmax_fixed_shared%s" % nodename,
params=['int M', 'int N',
'const float * x', 'const int sx0', 'const int sx1',
'float * sm', 'const int sm_s0', 'const int sm_s1'],
body=[
"extern __shared__ float buf[]",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"const float *x_ptr = &x[blockIDX * sx0]",
"float *sm_ptr = &sm[blockIDX * sm_s0]",
inline_softmax_fixed_shared('N', 'buf', 'x_ptr', 'sx1',
'sm_ptr', 'sm_s1',
'threadIdx.x', 'blockDim.x'),
"__syncthreads()",
"}",
])
ret1 = nvcc_kernel(
"kSoftmax_%s" % nodename,
params=['int M', 'int N',
'const float * x',
'const int sx0',
'const int sx1',
'float * sm',
'const int sm_s0',
'const int sm_s1'],
body=["extern __shared__ float buf[]",
"float * buf2 = buf + N",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
"buf[tx] = x[blockIDX * sx0 + tx * sx1]",
"buf2[tx] = buf[tx]", "}", "__syncthreads()",
inline_softmax('N',
'buf',
'buf2',
'threadIdx.x',
'blockDim.x'),
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
# This set all value correctly
"sm[blockIDX * sm_s0 + tx * sm_s1] = buf[tx]", "}",
"__syncthreads()", "}", ])
ret2 = nvcc_kernel(
"kSoftmax_fixed_shared%s" % nodename,
params=['int M', 'int N',
'const float * x', 'const int sx0', 'const int sx1',
'float * sm', 'const int sm_s0', 'const int sm_s1'],
body=["extern __shared__ float buf[]",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"const float *x_ptr = &x[blockIDX * sx0]",
"float *sm_ptr = &sm[blockIDX * sm_s0]",
inline_softmax_fixed_shared('N', 'buf', 'x_ptr', 'sx1',
'sm_ptr', 'sm_s1',
'threadIdx.x',
'blockDim.x'),
"__syncthreads()", "}", ])
return ret1 + "\n" + ret2
gpu_softmax = GpuSoftmax()
......@@ -768,25 +769,20 @@ class GpuSoftmaxWithBias(GpuOp):
'const float * x', 'const int sx0', 'const int sx1',
'const float * b', 'const int sb0',
'float * sm', 'const int sm_s0', 'const int sm_s1'],
body=[
"extern __shared__ float buf[]",
"float * buf2 = buf + N",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
"buf[tx] = x[blockIDX * sx0 + tx * sx1]",
"buf[tx] += b[tx * sb0]",
"buf2[tx] = buf[tx]",
"}",
"__syncthreads()",
inline_softmax('N', 'buf', 'buf2',
'threadIdx.x', 'blockDim.x'),
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
"sm[blockIDX * sm_s0 + tx * sm_s1] = buf[tx]",
"}",
"__syncthreads()",
"}",
])
body=["extern __shared__ float buf[]",
"float * buf2 = buf + N",
"for (int blockIDX = blockIdx.x; blockIDX < M;"
" blockIDX += gridDim.x){",
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
"buf[tx] = x[blockIDX * sx0 + tx * sx1]",
"buf[tx] += b[tx * sb0]",
"buf2[tx] = buf[tx]", "}",
"__syncthreads()", inline_softmax('N', 'buf', 'buf2',
'threadIdx.x',
'blockDim.x'),
"for (int tx = threadIdx.x; tx< N; tx += blockDim.x){",
"sm[blockIDX * sm_s0 + tx * sm_s1] = buf[tx]", "}",
"__syncthreads()", "}", ])
ret2 = nvcc_kernel("kSoftmaxWithBias_fixed_shared%s" % nodename,
params=['int M', 'int N',
'const float * x',
......@@ -802,7 +798,8 @@ class GpuSoftmaxWithBias(GpuOp):
"float *sm_ptr = &sm[blockIDX * sm_s0]",
inline_softmax_fixed_shared('N', 'buf',
'x_ptr', 'sx1',
'sm_ptr', 'sm_s1',
'sm_ptr',
'sm_s1',
'threadIdx.x',
'blockDim.x',
'b', 'sb0'),
......
theano/sandbox/cuda/nvcc_compiler.py
浏览文件 @ b69ad54d
......@@ -4,7 +4,6 @@ import logging
import os
import subprocess
import sys
import warnings
from locale import getpreferredencoding
import numpy
......@@ -249,8 +248,9 @@ class NVCC_compiler(Compiler):
_logger.debug('Writing module C++ code to %s', cppfilename)
cppfile.write(src_code)
lib_filename = os.path.join(location, '%s.%s' %
(module_name, get_lib_extension()))
lib_filename = os.path.join(
location, '%s.%s' %
(module_name, get_lib_extension()))
_logger.debug('Generating shared lib %s', lib_filename)
# TODO: Why do these args cause failure on gtx285 that has 1.3
......@@ -268,7 +268,7 @@ class NVCC_compiler(Compiler):
continue
for pattern in ['-O', '-arch=', '-ccbin=', '-G', '-g', '-I',
'-L', '--fmad', '--ftz', '--maxrregcount',
'--prec-div', '--prec-sqrt', '--use_fast_math',
'--prec-div', '--prec-sqrt', '--use_fast_math',
'-fmad', '-ftz', '-maxrregcount',
'-prec-div', '-prec-sqrt', '-use_fast_math',
'--use-local-env', '--cl-version=']:
......@@ -311,7 +311,7 @@ class NVCC_compiler(Compiler):
# https://wiki.debian.org/RpathIssue for details.
if (not type(config.cuda).root.is_default and
os.path.exists(os.path.join(config.cuda.root, 'lib'))):
os.path.exists(os.path.join(config.cuda.root, 'lib'))):
rpaths.append(os.path.join(config.cuda.root, 'lib'))
if sys.platform != 'darwin':
......@@ -341,7 +341,7 @@ class NVCC_compiler(Compiler):
indexof = cmd.index('-u')
cmd.pop(indexof) # Remove -u
cmd.pop(indexof) # Remove argument to -u
except ValueError as e:
except ValueError:
done = True
# CUDA Toolkit v4.1 Known Issues:
......@@ -359,11 +359,13 @@ class NVCC_compiler(Compiler):
try:
os.chdir(location)
p = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
nvcc_stdout_raw, nvcc_stderr_raw = p.communicate()[:2]
console_encoding = getpreferredencoding()
nvcc_stdout = decode_with(nvcc_stdout_raw, console_encoding)
nvcc_stderr = decode_with(nvcc_stderr_raw, console_encoding)
p = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
finally:
os.chdir(orig_dir)
......
theano/sandbox/cuda/opt.py
浏览文件 @ b69ad54d
......@@ -10,22 +10,32 @@ import warnings
import numpy
from six.moves import reduce, xrange
from . import dnn
import theano
from theano import scalar as scal
from theano import config, tensor, gof
import theano.ifelse
import theano.tensor.signal.pool
import theano.tensor.nnet
import theano.tensor.nnet.neighbours
# Convolution
from theano.tensor.nnet import conv
from theano.tensor.nnet.ConvGrad3D import ConvGrad3D
from theano.tensor.nnet.ConvTransp3D import ConvTransp3D
# Pooling
import theano.tensor.signal.pool as pool
from theano.compile import optdb
from theano.gof import (local_optimizer, EquilibriumDB, ProxyDB,
Optimizer, TopoOptimizer, toolbox)
from theano.gof.opt import LocalMetaOptimizer
from theano.sandbox.cuda.basic_ops import gpu_join, GpuJoin
from theano.sandbox.cuda import as_cuda_ndarray_variable
from theano.sandbox.cuda.basic_ops import (
gpu_eye, gpu_contiguous,
gpu_from_host, host_from_gpu, GpuFromHost, HostFromGpu,
GpuContiguous,
GpuElemwise, GpuDimShuffle, GpuReshape, GpuCAReduce,
GpuFlatten, gpu_flatten,
gpu_flatten,
GpuSubtensor, GpuAdvancedSubtensor1,
GpuAdvancedIncSubtensor1, GpuAdvancedIncSubtensor1_dev20,
GpuIncSubtensor, gpu_alloc, GpuAlloc, gpu_shape, GpuSplit, GpuAllocEmpty)
......@@ -137,8 +147,6 @@ register_opt(name='local_gpu_reshape_chain')(
# This is a partial list of CPU ops that can be in some circonstance
# moved to the GPU. This list is used by an optimization.
# Hopefully, we can keep this list up to date.
import theano.tensor.signal.pool
import theano.tensor.nnet.neighbours
cpu_ops_moved_to_gpu = [
tensor.blas.Dot22, tensor.blas.Dot22Scalar, tensor.blas.Gemm,
tensor.blas.Gemv, tensor.blas.Ger, tensor.nnet.conv.ConvOp,
......@@ -630,7 +638,7 @@ def local_gpu_batched_dot(node):
if y.ndim == 2:
y_ = y_.dimshuffle(0, 1, "x")
z = GpuBatchedDot()(as_cuda_ndarray_variable(x_),
as_cuda_ndarray_variable(y_))
as_cuda_ndarray_variable(y_))
# unpad z shape
if x.ndim == 2:
z = z.dimshuffle(0, *range(2, z.ndim))
......@@ -850,8 +858,8 @@ def local_gpu_careduce(node):
if x.type == node.outputs[0].type:
return [x]
elif (all([c != "output" and isinstance(c.op, GpuFromHost)
for c, i in node.outputs[0].clients])
and x.owner and x.owner.op.__class__ in
for c, i in node.outputs[0].clients]) and
x.owner and x.owner.op.__class__ in
cpu_ops_moved_to_gpu):
# It is not always good to transfer the reduction to
# the GPU when the clients are on the GPU but not the
......@@ -970,7 +978,7 @@ def local_gpu_elemwise_careduce(node):
# automatically add more case, as some like trigonometic
# operation with some reduction pattern will probably result
# to slow down.
isinstance(node.inputs[0].owner.op.scalar_op, scal.basic.Sqr)):
isinstance(node.inputs[0].owner.op.scalar_op, scal.basic.Sqr)):
op = node.op
inp = node.inputs[0].owner.inputs[0]
......@@ -1023,7 +1031,8 @@ def local_gpu_flatten(node):
return [gpu_flatten(host_input.owner.inputs[0], outdim)(
as_cuda_ndarray_variable(host_input.owner.inputs[0]))]
if isinstance(node.op, tensor.Flatten):
x, = node.inputs
x, shp = node.inputs
outdim = node.op.outdim
if x.owner and isinstance(x.owner.op, HostFromGpu):
outdim = node.op.outdim
gpu_x, = x.owner.inputs
......@@ -1050,15 +1059,13 @@ def local_gpu_subtensor(node):
*coords)]
if isinstance(node.op, tensor.Subtensor):
x = node.inputs[0]
if (x.owner and
isinstance(x.owner.op, HostFromGpu) and
x.dtype == "float32"):
if (x.owner and x.dtype == "float32" and
isinstance(x.owner.op, HostFromGpu)):
gpu_x = x.owner.inputs[0]
if (gpu_x.owner and
isinstance(gpu_x.owner.op, GpuFromHost) and
# And it is a shared var or an input of the graph.
not gpu_x.owner.inputs[0].owner):
if (gpu_x.owner and # And it is a shared var or an input of the graph.
not(gpu_x.owner.inputs[0].owner) and
isinstance(gpu_x.owner.op, GpuFromHost)):
if len(x.clients) == 1:
if any([n == 'output' or isinstance(n.op, GpuOp)
......@@ -1119,9 +1126,7 @@ def local_gpu_advanced_incsubtensor1(node):
'least \'0.6\'.', stacklevel=1)
active_device_no = theano.sandbox.cuda.active_device_number()
compute_capability = device_properties(active_device_no)['major']
if (compute_capability < 2 or
x.ndim != 2 or
y.ndim != 2):
if (compute_capability < 2 or y.ndim != 2 or x.ndim != 2):
gpu_op = GpuAdvancedIncSubtensor1(
set_instead_of_inc=set_instead_of_inc)
......@@ -1162,9 +1167,7 @@ def local_gpu_advanced_incsubtensor1(node):
active_device_no = theano.sandbox.cuda.active_device_number()
compute_capability = device_properties(active_device_no)['major']
if (compute_capability < 2 or
x.ndim != 2 or
y.ndim != 2):
if (compute_capability < 2 or y.ndim != 2 or x.ndim != 2):
gpu_op = GpuAdvancedIncSubtensor1(
set_instead_of_inc=set_instead_of_inc)
else:
......@@ -1203,8 +1206,8 @@ def local_gpu_incsubtensor(node):
# Incrementing a float32 x results in a float32
# output even if y is float64, so we can downcast
# y to put it on GPU
elif type(node.op) == tensor.IncSubtensor and \
node.inputs[0].dtype == "float32":
elif (type(node.op) == tensor.IncSubtensor and
node.inputs[0].dtype == "float32"):
x, y = node.inputs[0:2]
assert isinstance(x.type, tensor.TensorType)
assert isinstance(y.type, tensor.TensorType)
......@@ -1346,8 +1349,6 @@ def cast(x, dtype):
cast_op = theano.tensor.Elemwise(scal.Identity(scal.specific_out(stype)))
return cast_op(x)
import theano.tensor.nnet
@register_opt()
@local_optimizer([tensor.nnet.CrossentropySoftmaxArgmax1HotWithBias])
......@@ -1419,18 +1420,13 @@ def local_gpu_softmax_with_bias(node):
return False
# Convolution
from theano.tensor.nnet import conv
def _gpu_conv_to_fftconv(node):
# shared helper function for local_conv_fft_valid and local_conv_fft_full.
# we import conv2d_fft locally to avoid pycuda warnings
from theano.sandbox.cuda.fftconv import conv2d_fft
kwargs = {'border_mode': node.op.border_mode}
if (node.op.imshp is not None and
node.op.imshp[-1] is not None and
node.op.imshp[-1] % 2 == 1):
if (node.op.imshp is not None and node.op.imshp[-1] % 2 == 1 and
node.op.imshp[-1] is not None):
kwargs['pad_last_dim'] = True
# If the user supplied the full nonsymbolic image_shape and
......@@ -1459,9 +1455,8 @@ def _gpu_conv_to_fftconv(node):
@local_optimizer([GpuConv])
def local_conv_fft_valid(node):
if isinstance(node.op, GpuConv):
if (node.op.border_mode == 'valid' and
node.op.subsample == (1, 1) and
node.op.fft_opt):
if (node.op.border_mode == 'valid' and node.op.fft_opt and
node.op.subsample == (1, 1)):
return [_gpu_conv_to_fftconv(node)]
return False
......@@ -1470,9 +1465,8 @@ def local_conv_fft_valid(node):
@local_optimizer([GpuConv])
def local_conv_fft_full(node):
if isinstance(node.op, GpuConv):
if (node.op.border_mode == 'full' and
node.op.subsample == (1, 1) and
node.op.fft_opt):
if (node.op.border_mode == 'full' and node.op.fft_opt and
node.op.subsample == (1, 1)):
return [_gpu_conv_to_fftconv(node)]
return
......@@ -1586,7 +1580,7 @@ def local_gpu_conv(node):
@local_optimizer([GpuConv])
def local_conv_gemm(node):
if (isinstance(node.op, GpuConv) and
node.op.border_mode in ['full', 'valid']):
node.op.border_mode in ['full', 'valid']):
img, kern = node.inputs
border_mode = node.op.border_mode
......@@ -1659,7 +1653,6 @@ conv_groupopt.register('conv_fft_full', local_conv_fft_full, 10,
'conv_fft')
# cuDNN is the second, but only registered if cuDNN is available.
# It can be disabled by excluding 'conv_dnn' or 'cudnn'.
from . import dnn
# We can't check at import if dnn is available, so we must always
# register it. This do not cause problem as if it is not avail, the
# opt will do nothing.
......@@ -1708,9 +1701,8 @@ class ConvMetaOptimizer(LocalCudaMetaOptimizer):
shapes = ((node.op.bsize,) + node.op.imshp,
(node.op.nkern, nchannels) + node.op.kshp)
for (var, shape) in zip(vars, shapes):
if ((var in inputs) and
(shape is not None) and
not any(s is None for s in shape)):
if ((var in inputs) and (shape is not None) and
not any(s is None for s in shape)):
result[var] = theano.shared(
# TODO: Use var.type.filter when cuda_ndarray.filter
......@@ -1763,8 +1755,6 @@ def local_conv3d_fft(node):
gpu_optimizer.register("conv3d_fft", local_conv3d_fft)
from theano.tensor.nnet.ConvGrad3D import ConvGrad3D
@local_optimizer([ConvGrad3D])
def local_convgrad3d_fft(node):
......@@ -1775,7 +1765,7 @@ def local_convgrad3d_fft(node):
except tensor.NotScalarConstantError:
return False
if (isinstance(node.op, ConvGrad3D) and
(stride_x, stride_y, stride_z) == (1, 1, 1)):
(stride_x, stride_y, stride_z) == (1, 1, 1)):
# we import conv3d_fft locally to avoid pycuda warnings
from theano.sandbox.cuda.fftconv import conv3d_fft
......@@ -1794,8 +1784,6 @@ def local_convgrad3d_fft(node):
gpu_optimizer.register("convgrad3d_fft", local_convgrad3d_fft)
from theano.tensor.nnet.ConvTransp3D import ConvTransp3D
@local_optimizer([ConvTransp3D])
def local_convtransp3d_fft(node):
......@@ -1806,7 +1794,7 @@ def local_convtransp3d_fft(node):
except tensor.NotScalarConstantError:
return False
if (isinstance(node.op, ConvTransp3D) and
(stride_x, stride_y, stride_z) == (1, 1, 1)):
(stride_x, stride_y, stride_z) == (1, 1, 1)):
# we import conv3d_fft locally to avoid pycuda warnings
from theano.sandbox.cuda.fftconv import conv3d_fft
# Shuffle filters from (oc, 0, 1, t, ic) to (ic, oc, 0, 1, t)
......@@ -1894,15 +1882,11 @@ def local_convtransp3d_gemm(node):
gpu_optimizer.register("convtransp3d_gemm", local_convtransp3d_gemm)
# Pooling
import theano.tensor.signal.pool as pool
@register_opt()
@local_optimizer([pool.Pool])
def local_gpu_downsample_factor_max(node):
if (isinstance(node.op, pool.Pool)
and node.op.ds == node.op.st):
if (isinstance(node.op, pool.Pool) and
node.op.ds == node.op.st):
assert node.op.__props__ == ('ds', 'ignore_border', 'st', 'padding',
'mode')
......@@ -1917,14 +1901,12 @@ def local_gpu_downsample_factor_max(node):
@register_opt()
@local_optimizer([pool.MaxPoolGrad])
def local_gpu_downsample_factor_max_grad(node):
if (isinstance(node.op, pool.MaxPoolGrad) and
node.op.ds == node.op.st):
if (isinstance(node.op, pool.MaxPoolGrad) and node.op.ds == node.op.st):
assert node.op.__props__ == ('ds', 'ignore_border', 'st', 'padding',
'mode')
if (node.op.padding != (0, 0) or
node.op.mode != 'max' or
node.op.st != node.op.ds):
node.op.mode != 'max' or
node.op.st != node.op.ds):
return
x, z, gz = node.inputs
......@@ -1955,9 +1937,6 @@ def local_gpu_downsample_factor_max_grad_grad(node):
as_cuda_ndarray_variable(gx)))]
from theano.sandbox.cuda.basic_ops import gpu_join, GpuJoin
@register_opt()
@local_optimizer([tensor.Join])
def local_gpu_join(node):
......@@ -2252,8 +2231,8 @@ def local_gpualloc_memset_0(node):
if isinstance(node.op, GpuAlloc) and not node.op.memset_0:
inp = node.inputs[0]
if (isinstance(inp, CudaNdarrayConstant) and
inp.data.size == 1 and
(numpy.asarray(inp.data) == 0).all()):
inp.data.size == 1 and
(numpy.asarray(inp.data) == 0).all()):
new_out = GpuAlloc(memset_0=True)(*node.inputs)
old_bcast = node.outputs[0].type.broadcastable
......@@ -2308,8 +2287,9 @@ def local_gpu_eye(node):
if isinstance(node.op, GpuFromHost):
host_input = node.inputs[0]
if (host_input.owner and
isinstance(host_input.owner.op, tensor.Eye) and
host_input.owner.op.dtype == "float32"):
isinstance(host_input.owner.op, tensor.Eye) and
host_input.owner.op.dtype == "float32"):
if tensor.extract_constant(host_input.owner.inputs[2]) != 0:
return
return [gpu_eye(*host_input.owner.inputs)]
......@@ -2324,7 +2304,7 @@ def local_gpu_eye(node):
def safe_to_gpu(x):
if (isinstance(x.type, tensor.TensorType) and
x.type.dtype == 'float32'):
x.type.dtype == 'float32'):
return as_cuda_ndarray_variable(x)
else:
......@@ -2379,7 +2359,7 @@ def gpu_reconstruct_graph(inputs, outputs, tag=None):
def tensor_to_cuda(x):
if (isinstance(x.type, tensor.TensorType) and
x.type.dtype == 'float32'):
x.type.dtype == 'float32'):
y = CudaNdarrayType(broadcastable=x.type.broadcastable)()
if x.name:
......@@ -2437,9 +2417,9 @@ def gpuScanOptimization(node):
if isinstance(node.op, GpuFromHost):
host_input = node.inputs[0]
if (host_input.owner and
isinstance(host_input.owner.op, scan_op.Scan) and
not host_input.owner.op.info['gpu'] and
len(host_input.owner.outputs) == 1):
isinstance(host_input.owner.op, scan_op.Scan) and
not host_input.owner.op.info['gpu'] and
len(host_input.owner.outputs) == 1):
# Note that we are not doing the right thing here !!
# This is because the local optimizer expects only one
......@@ -2492,8 +2472,8 @@ def gpuScanOptimization(node):
return _outputs
# scan(host_from_gpu) -> host_from_gpu(GPUscan)
if (type(node.op) == scan_op.Scan
and not node.op.info['gpu']):
if (type(node.op) == scan_op.Scan and
not node.op.info['gpu']):
if any([(i.owner and isinstance(i.owner.op, HostFromGpu))
for i in node.inputs]):
......@@ -2792,7 +2772,7 @@ def local_abstractconv_gemm(node):
kern = kern.dimshuffle(1, 0, 2, 3)
# call GpuCorrMM_gradInputs
rval = GpuCorrMM_gradInputs('valid', subsample)(
gpu_contiguous(kern), gpu_contiguous(img))
gpu_contiguous(kern), gpu_contiguous(img))
else:
# need to flip the kernel if necessary
if node.op.filter_flip:
......@@ -2807,11 +2787,11 @@ def local_abstractconv_gemm(node):
# GpuConv does not always store information on the batchsize and
# channels, though, so we only use what information we have.)
if ((subsample == (1, 1)) and
(node.op.imshp is not None) and
(None not in node.op.imshp[-2:]) and
(node.op.kshp is not None) and
(None not in node.op.kshp) and
border_mode != "half"):
(node.op.imshp is not None) and
(None not in node.op.imshp[-2:]) and
(node.op.kshp is not None) and
(None not in node.op.kshp) and
border_mode != "half"):
# we know the kernel and output size
prod1 = node.op.kshp[0] * node.op.kshp[1]
prod2 = ((node.op.imshp[-2] - node.op.kshp[0] + 1) *
......
theano/sandbox/cuda/rng_curand.py
浏览文件 @ b69ad54d
"""
Define CURAND_RandomStreams - backed by CURAND.
"""
from __future__ import absolute_import, print_function, division
__authors__ = "James Bergstra"
__copyright__ = "(c) 2011, University of Montreal"
__license__ = "3-clause BSD License"
__contact__ = "theano-dev@googlegroups.com"
import numpy
import theano.gof
from theano.compat import PY3
......@@ -17,12 +7,21 @@ from theano.tensor import (get_vector_length, cast, opt)
from theano.compile import optdb
from theano.gof import local_optimizer, Variable
__authors__ = "James Bergstra"
__copyright__ = "(c) 2011, University of Montreal"
__license__ = "3-clause BSD License"
__contact__ = "theano-dev@googlegroups.com"
"""
Define CURAND_RandomStreams - backed by CURAND.
"""
config = theano.config
class CURAND_Base(GpuOp):
"""
"""
Base class for a random number generator implemented in CURAND.
The random number generator itself is an opaque reference managed by
......@@ -70,8 +69,7 @@ class CURAND_Base(GpuOp):
Return a tuple of attributes that define the Op.
"""
return (
self.destructive,
return (self.destructive,
self.output_type,
self.seed,
)
......@@ -88,7 +86,7 @@ class CURAND_Base(GpuOp):
def make_node(self, generator, size):
return theano.gof.Apply(self, [generator, size],
[generator.type(), self.output_type()])
[generator.type(), self.output_type()])
@classmethod
def new_auto_update(cls, generator, ndim, dtype, size, seed):
......@@ -101,10 +99,9 @@ class CURAND_Base(GpuOp):
v_size = theano.tensor.as_tensor_variable(size)
if ndim is None:
ndim = get_vector_length(v_size)
self = cls(
output_type=CudaNdarrayType((False,) * ndim),
seed=seed,
destructive=False)
self = cls(output_type=CudaNdarrayType((False,) * ndim),
seed=seed,
destructive=False)
o_gen, sample = self(generator, cast(v_size, 'int32'))
......@@ -282,7 +279,7 @@ class CURAND_RandomStreams(object):
RandomStreams instance that creates CURAND-based random variables.
One caveat is that generators are not serializable.
Parameters
----------
seed : int
......@@ -319,7 +316,7 @@ class CURAND_RandomStreams(object):
return rval
def uniform(self, size, low=0.0, high=1.0, ndim=None,
dtype=config.floatX):
dtype=config.floatX):
"""
Return symbolic tensor of uniform numbers.
......@@ -327,14 +324,14 @@ class CURAND_RandomStreams(object):
if isinstance(size, tuple):
msg = "size must be a tuple of int or a Theano variable"
assert all([isinstance(i, int) or isinstance(i, Variable)
for i in size]), msg
for i in size]), msg
else:
msg = "size must be a tuple of int or a Theano variable"
assert isinstance(size, Variable) and size.ndim == 1, msg
generator = theano.shared(False) # makes a generic
s_size = theano.tensor.as_tensor_variable(size)
u = CURAND_Uniform.new_auto_update(generator, ndim, dtype, s_size,
self.next_seed())
self.next_seed())
self.state_updates.append(u.update)
rval = u * (high - low) + low
if u.type.broadcastable != rval.type.broadcastable:
......@@ -342,10 +339,10 @@ class CURAND_RandomStreams(object):
'Increase the size to match the broadcasting pattern of '
'low and `high` arguments'
)
return rval
return rval
def normal(self, size=None, avg=0.0, std=1.0, ndim=None,
dtype=config.floatX):
dtype=config.floatX):
"""
Return symbolic tensor of normally-distributed numbers.
......@@ -359,14 +356,14 @@ class CURAND_RandomStreams(object):
if isinstance(size, tuple):
msg = "size must be a tuple of int or a Theano variable"
assert all([isinstance(i, int) or isinstance(i, Variable)
for i in size]), msg
for i in size]), msg
else:
msg = "size must be a tuple of int or a Theano variable"
assert isinstance(size, Variable) and size.ndim == 1, msg
generator = theano.shared(False) # makes a generic
s_size = theano.tensor.as_tensor_variable(size)
u = CURAND_Normal.new_auto_update(generator, ndim, dtype, s_size,
self.next_seed())
self.next_seed())
self.state_updates.append(u.update)
rval = u * std + avg
if u.type.broadcastable != rval.type.broadcastable:
......@@ -374,7 +371,7 @@ class CURAND_RandomStreams(object):
'Increase the size to match the broadcasting pattern of `low`'
'and `high` arguments'
)
return rval
return rval
@local_optimizer([CURAND_Base])
......@@ -386,5 +383,5 @@ def local_destructive(node):
return new_op.make_node(*node.inputs).outputs
return False
optdb.register('CURAND_destructive',
opt.in2out(local_destructive, ignore_newtrees=True), 99, 'fast_run',
'inplace')
opt.in2out(local_destructive, ignore_newtrees=True),
99, 'fast_run', 'inplace')
theano/sandbox/cuda/tests/test_basic_ops.py
浏览文件 @ b69ad54d
......@@ -9,19 +9,20 @@ import numpy
from six.moves import xrange
import theano
import theano.tensor as T
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
raise SkipTest('Optional package cuda disabled')
import theano.sandbox.cuda as tcn
import theano.sandbox.cuda as cuda
import theano.sandbox.cuda.basic_ops as B
from theano.tensor.basic import _allclose
from theano.tests import unittest_tools as utt
import theano.tensor.tests.test_basic
import theano.tensor.tests.test_subtensor
import theano.tensor.tests.test_sharedvar
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
......@@ -75,8 +76,8 @@ def test_careduce():
# The following 2 cases could work if the scalar_op.c_code work with float* dtype.
# Currently we have this error:
# error: invalid operands of types 'npy_float32' and 'npy_float32' to binary 'operator&'
#(theano.scalar.and_, tensor.elemwise.CAReduce),
#(theano.scalar.or_, tensor.elemwise.CAReduce),
# (theano.scalar.and_, tensor.elemwise.CAReduce),
# (theano.scalar.or_, tensor.elemwise.CAReduce),
]:
for shape, pattern in [((1, 1), (1,)),
((1, 0), (1,)),
......@@ -113,7 +114,7 @@ def test_careduce():
((4100, 4, 3), [2]), ((5, 4100, 3), [2]), ((5, 4, 4100), [2]), # 001
((4100, 4, 3), [0, 1]), ((5, 4100, 3), [0, 1]), ((5, 4, 4100), [0, 1]), # 110
((4100, 4, 3), [1, 2]), ((5, 4100, 3), [1, 2]), ((5, 4, 4100), [1, 2]), # 011
((4100,4,3),[0,2]),((5,4100,3),[0,2]),((5,4,4100),[0,2]),
((4100, 4, 3), [0, 2]), ((5, 4100, 3), [0, 2]), ((5, 4, 4100), [0, 2]),
((4100, 4, 3), [0, 1, 2]), ((5, 4100, 3), [0, 1, 2]), ((5, 4, 4100), [0, 1, 2]), # 111
((65, 4, 3), [0, 1, 2]), ((5, 65, 3), [0, 1, 2]), ((5, 4, 65), [0, 1, 2]), # 111
......@@ -127,15 +128,15 @@ def test_careduce():
((4100, 4, 3, 2), [2, 3]), ((4, 4100, 3, 2), [2, 3]), ((4, 3, 4100, 2), [2, 3]), ((4, 3, 2, 4100), [2, 3]), # 0011
((4100, 4, 3, 2), [1, 3]), ((4, 4100, 3, 2), [1, 3]), ((4, 3, 4100, 2), [1, 3]), ((4, 3, 2, 4100), [1, 3]), # 0101
((4100, 4, 3, 2), [1, 2]), ((4, 4100, 3, 2), [1, 2]), ((4, 3, 4100, 2), [1, 2]), ((4, 3, 2, 4100), [1, 2]), # 0110
((4100,4,3,2),[0,3]),((4,4100,3,2),[0,3]),((4,3,4100,2),[0,3]),((4,3,2,4100),[0,3]),#1001
# ((4100,4,3,2),[0,2]),((4,4100,3,2),[0,2]),((4,3,4100,2),[0,2]),((4,3,2,4100),[0,2]),#1010 not implemented
((4100, 4, 3, 2), [0, 3]), ((4, 4100, 3, 2), [0, 3]), ((4, 3, 4100, 2), [0, 3]), ((4, 3, 2, 4100), [0, 3]), # 1001
# ((4100,4,3,2),[0,2]),((4,4100,3,2),[0,2]),((4,3,4100,2),[0,2]),((4,3,2,4100),[0,2]),#1010 not implemented
((4100, 4, 3, 2), [0, 1]), ((4, 4100, 3, 2), [0, 1]), ((4, 3, 4100, 2), [0, 1]), ((4, 3, 2, 4100), [0, 1]), # 1100
# reduce over 3d
# 3d not tested: 1101, 1110, 1111
((4100,4,3,2),[0,1,3]),((4,4100,3,2),[0,1,3]),((4,3,4100,2),[0,1,3]),((4,3,2,4100),[0,1,3]),#1101
((4100, 4, 3, 2), [0, 1, 3]), ((4, 4100, 3, 2), [0, 1, 3]), ((4, 3, 4100, 2), [0, 1, 3]), ((4, 3, 2, 4100), [0, 1, 3]), # 1101
((4100, 4, 3, 2), [0, 1, 2]), ((4, 4100, 3, 2), [0, 1, 2]), ((4, 3, 4100, 2), [0, 1, 2]), ((4, 3, 2, 4100), [0, 1, 2]), # 1110
((4100, 4, 3, 2), [0, 2, 3]), ((4, 4100, 3, 2), [0, 2, 3]), ((4, 3, 4100, 2), [0, 2, 3]), # ((4,3,2,4100),[0,2,3]),#1011
((4100, 4, 3, 2), [0, 2, 3]), ((4, 4100, 3, 2), [0, 2, 3]), ((4, 3, 4100, 2), [0, 2, 3]), # ((4, 3, 2, 4100), [0, 2, 3]), # 1011
((4100, 4, 3, 2), [1, 2, 3]), ((4, 4100, 3, 2), [1, 2, 3]), ((4, 3, 4100, 2), [1, 2, 3]), ((4, 3, 2, 4100), [1, 2, 3]), # 0111
((65, 4, 3, 2), [1, 2, 3]), ((4, 65, 3, 2), [1, 2, 3]), ((4, 3, 65, 2), [1, 2, 3]), ((4, 3, 2, 65), [1, 2, 3]), # 0111
......@@ -148,26 +149,26 @@ def test_careduce():
]:
op = careduce_op(scalar_op, axis=pattern)
pat = tensor_pattern_to_gpu_pattern(shape, pattern)
tensor_pattern_to_gpu_pattern(shape, pattern)
a = tensor.TensorType('float32', (False,) * len(shape))()
b = op(a*a)
b = op(a * a)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape)
val = theano._asarray(val, dtype='float32')
f = theano.function([a], b, mode=mode_with_gpu)
f2 = theano.function([a], b, mode=mode_without_gpu)
assert tcn.GpuCAReduce in [x.op.__class__
for x in f.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
if tcn.GpuElemwise in [x.op.__class__
for x in f.maker.fgraph.toposort()]:
assert tcn.GpuReshape in [x.op.__class__
for x in f.maker.fgraph.toposort()]
assert op.__class__ in [x.op.__class__
for x in f2.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
assert tcn.GpuCAReduce in [
x.op.__class__ for x in f.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
if(tcn.GpuElemwise in [
x.op.__class__ for x in f.maker.fgraph.toposort()]):
assert tcn.GpuReshape in [
x.op.__class__ for x in f.maker.fgraph.toposort()]
assert op.__class__ in [
x.op.__class__ for x in f2.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
f_caused_value_error = False
try:
f_out = f(val)
......@@ -176,8 +177,9 @@ def test_careduce():
f_caused_value_error = True
except NotImplementedError:
if (numpy.prod(shape) == 0 and
getattr(scalar_op, 'identity', None) != 0):
continue
getattr(
scalar_op, 'identity', None) != 0):
continue
raise
f2_caused_value_error = False
......@@ -208,45 +210,49 @@ def test_careduce():
# example in debug mode with unittests.rseed=9275
orig_rtol = theano.tensor.basic.float32_rtol
theano.tensor.basic.float32_rtol = 2e-5
assert _allclose(f_out, f2_out), ('shape', shape,
'pattern', pattern,
scalar_op,
sum([shape[i] for i in pattern]),
f2(val), f(val), val)
assert _allclose(f_out, f2_out), (
'shape',
shape,
'pattern',
pattern, scalar_op,
sum([shape[i] for i in pattern]),
f2(val), f(val), val)
finally:
theano.tensor.basic.float32_rtol = orig_rtol
# test with dimshuffle
# we shuffle the 2 outer dims.
for shape, pattern in [ # ((5,),[0]),
((5, 4), [0, 1]), ((5, 4), [0]),
((5, 4, 3), [0]), ((5, 4, 3), [0, 1]), ((5, 4, 3), [2]), ((5, 4, 3), [0, 1, 2]),
((5, 4, 3, 2), [0, 1, 2, 3]), ((5, 4, 3, 2), [0, 2, 3]),
((128, 1, 3, 3), [0, 1, 2, 3]),
]:
# for shape, pattern in [((5,), [0]),
for shape, pattern in [((5, 4), [0, 1]), ((5, 4), [0]),
((5, 4, 3), [0]), ((5, 4, 3), [0, 1]),
((5, 4, 3), [2]), ((5, 4, 3), [0, 1, 2]),
((5, 4, 3, 2), [0, 1, 2, 3]),
((5, 4, 3, 2), [0, 2, 3]),
((128, 1, 3, 3), [0, 1, 2, 3]), ]:
op = careduce_op(scalar_op, axis=pattern)
pat = tensor_pattern_to_gpu_pattern(shape, pattern)
tensor_pattern_to_gpu_pattern(shape, pattern)
a = tensor.TensorType('float32', (False,) * len(shape))()
dim_pattern = list(range(len(shape)))
dim_pattern[0] = 1
dim_pattern[1] = 0
a = a.dimshuffle(dim_pattern)
b = op(a*a)
b = op(a * a)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape)
val = theano._asarray(val, dtype='float32')
f = theano.function([a], b, mode=mode_with_gpu)
f2 = theano.function([a], b, mode=mode_without_gpu)
assert tcn.GpuCAReduce in [x.op.__class__
for x in f.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
assert tcn.GpuElemwise not in [x.op.__class__
for x in f.maker.fgraph.toposort()]
assert op.__class__ in [x.op.__class__
for x in f2.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
assert tcn.GpuCAReduce in [
x.op.__class__ for x in f.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
assert tcn.GpuElemwise not in [
x.op.__class__ for x in f.maker.fgraph.toposort()]
assert op.__class__ in [
x.op.__class__ for x in f2.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
assert _allclose(f2(val), f(val)), ('shape', shape,
'pattern', pattern,
scalar_op,
......@@ -258,16 +264,15 @@ def test_careduce():
((5, 4, 3), [0]), ((5, 4, 3), [0, 1]),
((5, 4, 3), [2]), ((5, 4, 3), [0, 1, 2]),
((5, 4, 3, 2), [0, 1, 2, 3]), ((5, 4, 3, 2), [0, 2, 3]),
((128, 1, 3, 3), [0, 1, 2, 3]),
]:
((128, 1, 3, 3), [0, 1, 2, 3]), ]:
op = careduce_op(scalar_op, axis=pattern)
pat = tensor_pattern_to_gpu_pattern(shape, pattern)
tensor_pattern_to_gpu_pattern(shape, pattern)
shape = numpy.asarray(shape) * 2
a = tensor.TensorType('float32', (False,) * len(shape))()
a2 = tcn.CudaNdarrayType((False,) * len(shape))()
b = op(a*a)
b2 = op(a2*a2)
b = op(a * a)
b2 = op(a2 * a2)
val = numpy.random.rand(numpy.prod(shape)).reshape(shape)
# val = numpy.ones(shape)
# val = numpy.arange(numpy.prod(shape)).reshape(shape)
......@@ -287,14 +292,14 @@ def test_careduce():
val2 = val2[::2, ::2, ::2, ::2]
f = theano.function([a], b, mode=mode_without_gpu)
f2 = theano.function([a2], b2, mode=mode_with_gpu)
assert tcn.GpuCAReduce in [x.op.__class__
for x in f2.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
assert tcn.GpuCAReduce in [
x.op.__class__ for x in f2.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
assert tcn.GpuElemwise not in [x.op.__class__
for x in f.maker.fgraph.toposort()]
assert op.__class__ in [x.op.__class__
for x in f.maker.fgraph.toposort()], (
scalar_op, shape, pattern)
scalar_op, shape, pattern)
assert _allclose(f2(val2), f(val)), ('shape', shape,
'pattern', pattern,
sum([shape[i] for i in pattern]))
......@@ -374,8 +379,10 @@ def test_reshape():
# Test zero dimensions are allowed
x = T.vector('x')
f_reshp = theano.function([x], x.reshape((0,100)), mode=mode_with_gpu)
assert f_reshp(numpy.ndarray((0,), dtype='float32')).shape == (0,100)
f_reshp = theano.function(
[x], x.reshape((0, 100)), mode=mode_with_gpu)
assert f_reshp(
numpy.ndarray((0, ), dtype='float32')).shape == (0, 100)
def test_alloc_empty():
......@@ -406,7 +413,7 @@ def test_elemwise_empty():
b = tensor.fmatrix()
f = pfunc([b], [], updates=[(a, a + b)], mode=mode_with_gpu)
f2 = pfunc([b], [], updates=[(a, a + b)], mode=mode_without_gpu)
pfunc([b], [], updates=[(a, a + b)], mode=mode_without_gpu)
a0 = a.get_value() * 1.0
f(numpy.ones((0, 0), dtype='float32'))
......@@ -424,8 +431,9 @@ def test_elemwise0():
f = pfunc([b], [], updates=[(a, a + b)], mode=mode_with_gpu)
# check that we work inplace.
assert (list(f.maker.fgraph.toposort()[1].op.destroy_map.items())
== [(0, [0])])
assert (list(
f.maker.fgraph.toposort()[1].op.destroy_map.items()) == [
(0, [0])])
a0 = a.get_value() * 1.0
f(numpy.ones((4, 4), dtype='float32'))
......@@ -495,7 +503,8 @@ def test_elemwise2():
dtype='float32'), 'a')
b = tensor.Tensor(dtype='float32', broadcastable=[0] * len(shape))()
f = pfunc([b], [], updates=[(a, (a + b).dimshuffle([2, 0, 3, 1]) *
tensor.exp(b ** a).dimshuffle([2, 0, 3, 1]))], mode=mode_with_gpu)
tensor.exp(b ** a).dimshuffle([2, 0, 3, 1]))],
mode=mode_with_gpu)
has_elemwise = False
for i, node in enumerate(f.maker.fgraph.toposort()):
has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
......@@ -585,10 +594,11 @@ def test_elemwise_composite_float64():
return l
for mode in [mode_with_gpu, mode_with_gpu.excluding('gpu_after_fusion'),
mode_with_gpu.excluding('elemwise_fusion')]:
f = pfunc([a, b],
tensor.cast(tensor.lt(tensor.cast(a, 'float64') ** 2,
b),
'float32'), mode=mode)
f = pfunc(
[a, b],
tensor.cast(
tensor.lt(tensor.cast(a, 'float64') ** 2, b), 'float32'),
mode=mode)
out = f(av, bv)
assert numpy.all(out == ((av ** 2) < bv))
......@@ -648,11 +658,11 @@ def speed_elemwise_collapse():
v = theano._asarray(numpy.random.rand(*shape), dtype='float32')
v = v[:, ::2, :, :]
v = cuda_ndarray.CudaNdarray(v)
t1 = time.time()
time.time()
for i in range(100):
# let debugmode catch errors
f(v)
t2 = time.time()
time.time()
def speed_elemwise_collapse2():
......@@ -672,11 +682,11 @@ def speed_elemwise_collapse2():
v = theano._asarray(numpy.random.rand(*shape), dtype='float32')
v = v[:, :, :, ::2]
v = cuda_ndarray.CudaNdarray(v)
t1 = time.time()
time.time()
for i in range(100):
# let debugmode catch errors
f(v)
t2 = time.time()
time.time()
def test_elemwise_collapse():
......@@ -848,8 +858,8 @@ def test_hostfromgpu_shape_i():
ca = theano.sandbox.cuda.var.CudaNdarrayType((False, False))()
av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')
cv = cuda.CudaNdarray(numpy.asarray(numpy.random.rand(5, 4),
dtype='float32'))
cv = cuda.CudaNdarray(numpy.asarray(
numpy.random.rand(5, 4), dtype='float32'))
f = theano.function([a], cuda.basic_ops.gpu_from_host(a), mode=m)
assert cuda.basic_ops.gpu_from_host in [x.op
......@@ -880,7 +890,7 @@ def test_gpujoin_assert_cndas():
a = theano.shared(_a)
try:
c = cuda.basic_ops.gpu_join(1, a)
cuda.basic_ops.gpu_join(1, a)
# can't "assert False" here, as we want the assertion
# error from gpu_join
except TypeError:
......@@ -921,13 +931,18 @@ def test_gpujoin_gpualloc():
b = T.fmatrix('b')
b_val = numpy.asarray(numpy.random.rand(3, 5), dtype='float32')
f = theano.function([a, b], T.join(0, T.zeros_like(a), T.ones_like(b)) + 4,
mode=mode_without_gpu)
f_gpu = theano.function([a, b], T.join(0, T.zeros_like(a), T.ones_like(b)),
mode=mode_with_gpu)
f_gpu2 = theano.function([a, b], T.join(0, T.zeros_like(a),
T.ones_like(b)) + 4,
mode=mode_with_gpu)
f = theano.function(
[a, b],
T.join(0, T.zeros_like(a), T.ones_like(b)) + 4,
mode=mode_without_gpu)
f_gpu = theano.function(
[a, b],
T.join(0, T.zeros_like(a), T.ones_like(b)),
mode=mode_with_gpu)
f_gpu2 = theano.function(
[a, b],
T.join(0, T.zeros_like(a), T.ones_like(b)) + 4,
mode=mode_with_gpu)
assert sum([node.op == T.alloc for node in f.maker.fgraph.toposort()]) == 2
assert sum([node.op == T.join for node in f.maker.fgraph.toposort()]) == 1
......@@ -963,9 +978,6 @@ def test_gpualloc_output_to_gpu():
assert numpy.allclose(f(5), f_gpu(5))
import theano.tensor.tests.test_basic
class TestAlloc(theano.tensor.tests.test_basic.TestAlloc):
dtype = "float32"
mode = mode_with_gpu
......@@ -987,7 +999,6 @@ class T_Join_and_Split(theano.tensor.tests.test_basic.T_Join_and_Split):
self.shared = cuda.shared_constructor
import theano.tensor.tests.test_subtensor
# This is to don't duplicate test.
......@@ -1026,7 +1037,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor):
# version when we should. Users should not use it.
for shape, idx, fast in [((70000,), range(70000), True),
((70000, 5), range(70000), True),
((70000, 5), numpy.zeros((0,), 'int64'),
((70000, 5), numpy.zeros((0,), 'int64'),
True),
((70000, 2, 3), range(70000), True),
((1025, 1025), [5, 10], True),
......@@ -1035,7 +1046,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor):
((3, 10, 68000), [1, 2], True),
((3, 69000, 11), [1, 2], True),
# much memory, will be disabled if needed
((2*10e7,), [-1, 199999999], True),
((2 * 10e7,), [-1, 199999999], True),
((4, 5), [2, 3], True),
((4, 2, 3), [0, 3], True),
((4, 2, 3), [3, 3, 1, 1, 2,
......@@ -1047,8 +1058,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor):
# optimized for that case.
((4, 4, 2, 3), [3, 3, 1, 1, 2, 2, 0, 0,
-1, -2, -3, -4], False),
((1, 10), [0, 0], True),
]:
((1, 10), [0, 0], True), ]:
# If there is not enough memory on the GPU, skip the test
size_needed = numpy.prod(shape) * (4 + 1)
if isinstance(theano.compile.get_default_mode(),
......@@ -1106,13 +1116,14 @@ def test_advinc_subtensor1():
rep[[0, 2]] += yval
utt.assert_allclose(rval, rep)
def test_advset_subtensor1():
""" Test GPU version of set_subtensor on vectors (uses GpuAdvancedIncSubtensor1) """
shp = (10,)
shared = cuda.shared_constructor
xval = numpy.arange(shp[0], dtype='float32').reshape(shp) + 1
idxs = numpy.array([0,2,5,7,3], dtype='int32')
yval = numpy.ones(len(idxs), dtype='float32')*10
idxs = numpy.array([0, 2, 5, 7, 3], dtype='int32')
yval = numpy.ones(len(idxs), dtype='float32') * 10
x = shared(xval, name='x')
y = T.tensor(dtype='float32', broadcastable=(False,) * len(shp), name='y')
expr = T.advanced_set_subtensor1(x, y, idxs)
......@@ -1124,13 +1135,14 @@ def test_advset_subtensor1():
rep[idxs] = yval
utt.assert_allclose(rval, rep)
def test_advset_subtensor1_2d():
""" Test GPU version of set_subtensor on matrices (uses GpuAdvancedIncSubtensor1_dev20 if compute capability >= 2.0) """
shp = (10,5)
shp = (10, 5)
shared = cuda.shared_constructor
xval = numpy.arange(numpy.prod(shp), dtype='float32').reshape(shp) + 1
idxs = numpy.array([0,2,5,7,3], dtype='int32')
yval = numpy.ones((len(idxs), shp[1]), dtype='float32')*10
idxs = numpy.array([0, 2, 5, 7, 3], dtype='int32')
yval = numpy.ones((len(idxs), shp[1]), dtype='float32') * 10
x = shared(xval, name='x')
y = T.tensor(dtype='float32', broadcastable=(False,) * len(shp), name='y')
expr = T.advanced_set_subtensor1(x, y, idxs)
......@@ -1142,37 +1154,38 @@ def test_advset_subtensor1_2d():
rep[idxs] = yval
utt.assert_allclose(rval, rep)
def test_inc_subtensor():
shared = cuda.shared_constructor
#shared = tensor.shared
cuda.shared_constructor
# shared = tensor.shared
x, y = T.fmatrices('x', 'y')
xval = numpy.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
dtype='float32')
yval = numpy.asarray([[10, 10, 10], [10, 10, 10], [10, 10, 10]],
dtype='float32')
xval = numpy.asarray(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype='float32')
yval = numpy.asarray(
[[10, 10, 10], [10, 10, 10], [10, 10, 10]], dtype='float32')
expr = T.inc_subtensor(x[:, 1:3], y[:, 1:3])
f = theano.function([x, y], expr, mode=mode_with_gpu)
assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and
node.op.set_instead_of_inc == False
node.op.set_instead_of_inc is False
for node in f.maker.fgraph.toposort()]) == 1
utt.assert_allclose(f(xval, yval), [[1., 12., 13.],
[4., 15., 16.], [7., 18., 19.]])
def test_set_subtensor():
shared = cuda.shared_constructor
#shared = tensor.shared
cuda.shared_constructor
# shared = tensor.shared
x, y = T.fmatrices('x', 'y')
xval = numpy.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
dtype='float32')
yval = numpy.asarray([[10, 10, 10], [10, 10, 10], [10, 10, 10]],
dtype='float32')
xval = numpy.asarray(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype='float32')
yval = numpy.asarray(
[[10, 10, 10], [10, 10, 10], [10, 10, 10]], dtype='float32')
expr = T.set_subtensor(x[:, 1:3], y[:, 1:3])
f = theano.function([x, y], expr, mode=mode_with_gpu)
assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and
node.op.set_instead_of_inc == True
node.op.set_instead_of_inc is True
for node in f.maker.fgraph.toposort()]) == 1
f(xval, yval)
......@@ -1191,7 +1204,7 @@ def test_many_arg_elemwise():
for arg in xrange(0, num_args)]
symb_args = [theano.tensor.TensorType('float32',
(False,)*nb_dim)()
(False,) * nb_dim)()
for arg in xrange(0, num_args)]
outputs = []
......@@ -1313,7 +1326,6 @@ class test_size(unittest.TestCase):
assert y.size == theano.function([], x.size)()
import theano.tensor.tests.test_sharedvar
# This test the case when the shared constructor view an CudaNdarray as input
test_shared_options = theano.tensor.tests.test_sharedvar.makeSharedTester(
shared_constructor_=tcn.shared_constructor,
......@@ -1374,7 +1386,7 @@ def speed_reduce10():
if __name__ == '__main__':
#test_many_arg_elemwise()
#test_gpujoin_assert_cndas()
# test_many_arg_elemwise()
# test_gpujoin_assert_cndas()
test_advset_subtensor1()
test_advset_subtensor1_2d()
theano/sandbox/cuda/tests/test_bench_loopfusion.py
浏览文件 @ b69ad54d
......@@ -10,7 +10,7 @@ from __future__ import absolute_import, print_function, division
# so state is ignored
# since this job is not restartable, channel is also ignored
import logging, time, sys
import logging
import numpy
from six.moves import xrange
......@@ -18,17 +18,22 @@ from six.moves import xrange
import theano
from theano.compile import shared, pfunc
from theano import tensor
from theano.tensor.nnet import softplus
from theano.tensor.nnet.nnet import softsign
try:
from PIL import Image
except ImportError:
Image = None
# from PIL import Image
_logger = logging.getLogger('theano.sandbox.cuda.tests.test_bench_loopfusion')
def _shared_uniform(rng, low, high, size, dtype, name=None):
return shared(
theano._asarray(
rng.uniform(low=low, high=high, size=size),
dtype=dtype), name)
theano._asarray(
rng.uniform(low=low, high=high, size=size),
dtype=dtype),
name)
class Kouh2008(object):
......@@ -49,8 +54,10 @@ class Kouh2008(object):
"""
if len(w_list) != len(x_list):
raise ValueError('w_list must have same len as x_list')
output = (sum(w * tensor.pow(x, p) for (w, x) in zip(w_list, x_list)))\
/ (theano._asarray(eps, dtype=k.type.dtype) + k + tensor.pow(sum(tensor.pow(x, q) for x in x_list), r))
output = ((sum(w * tensor.pow(x, p)
for (w, x) in zip(w_list, x_list))) /
(theano._asarray(eps, dtype=k.type.dtype) + k +
tensor.pow(sum(tensor.pow(x, q) for x in x_list), r)))
assert output.type.ndim == 2
self.__dict__.update(locals())
......@@ -80,10 +87,15 @@ class Kouh2008(object):
w_sm = theano.tensor.nnet.softmax(w)
w_list = [w_sm[:, i] for i in xrange(n_terms)]
w_l1 = abs(w).sum()
w_l2_sqr = (w**2).sum()
w_l2_sqr = (w ** 2).sum()
else:
w_list = [shared_uniform(low=-2.0/n_terms, high=2.0/n_terms, size=(n_out,), name='w_%i'%i)
for i in xrange(n_terms)]
w_list = [
shared_uniform(
low=-2.0 / n_terms,
high=2.0 / n_terms,
size=(n_out,),
name='w_%i' % i)
for i in xrange(n_terms)]
w_l1 = sum(abs(wi).sum() for wi in w_list)
w_l2_sqr = sum((wi**2).sum() for wi in w_list)
......@@ -102,19 +114,27 @@ class Kouh2008(object):
p = tensor.nnet.sigmoid(p_unbounded) * e_range_mag + e_range_low
q = tensor.nnet.sigmoid(q_unbounded) * e_range_mag + e_range_low
r = tensor.nnet.sigmoid(r_unbounded) * \
theano._asarray(1.0/e_range_low - 1.0/e_range_high, dtype=dtype) \
+ theano._asarray(1.0/e_range_high, dtype=dtype)
theano._asarray(1.0 / e_range_low - 1.0 / e_range_high,
dtype=dtype) + \
theano._asarray(1.0 / e_range_high, dtype=dtype)
k = softsign(k_unbounded)
if use_softmax_w:
rval = cls(w_list, x_list, p, q, r, k,
params=[p_unbounded, q_unbounded, r_unbounded, k_unbounded, w] + params,
updates=updates)
params=[p_unbounded,
q_unbounded,
r_unbounded,
k_unbounded,
w] + params,
updates=updates)
else:
rval = cls(w_list, x_list, p, q, r, k,
params=[p_unbounded, q_unbounded, r_unbounded, k_unbounded] + w_list + params,
updates=updates)
params=[p_unbounded,
q_unbounded,
r_unbounded,
k_unbounded] + w_list + params,
updates=updates)
rval.p_unbounded = p_unbounded
rval.q_unbounded = q_unbounded
rval.r_unbounded = r_unbounded
......@@ -126,8 +146,10 @@ class Kouh2008(object):
return rval
@classmethod
def new_filters_expbounds(cls, rng, input, n_in, n_out, n_terms, dtype=None, eps=1e-1,
exponent_range=(1.0, 3.0), filter_range=1.0):
def new_filters_expbounds(cls, rng, input, n_in, n_out, n_terms,
dtype=None, eps=1e-1,
exponent_range=(1.0, 3.0),
filter_range=1.0):
"""Return a KouhLayer instance with random parameters
The parameters are drawn on a range [typically] suitable for fine-tuning by gradient
......@@ -161,19 +183,30 @@ class Kouh2008(object):
def shared_uniform(low, high, size, name):
return _shared_uniform(rng, low, high, size, dtype, name)
f_list = [shared_uniform(low=-2.0/numpy.sqrt(n_in), high=2.0/numpy.sqrt(n_in), size=(n_in, n_out), name='f_%i'%i)
for i in xrange(n_terms)]
b_list = [shared_uniform(low=0, high=.01, size=(n_out,), name='b_%i'%i)
for i in xrange(n_terms)]
#x_list = [theano._asarray(eps, dtype=dtype)+softplus(tensor.dot(input, f_list[i])) for i in xrange(n_terms)]
f_list = [shared_uniform(low=-2.0 / numpy.sqrt(n_in),
high=2.0 / numpy.sqrt(n_in),
size=(n_in, n_out),
name='f_%i' % i)
for i in xrange(n_terms)]
b_list = [shared_uniform(low=0,
high=.01,
size=(n_out,),
name='b_%i' % i)
for i in xrange(n_terms)]
# x_list = [theano._asarray(eps, dtype=dtype) + softplus(tensor.dot(input, f_list[i])) for i in xrange(n_terms)]
filter_range = theano._asarray(filter_range, dtype=dtype)
half_filter_range = theano._asarray(filter_range/2, dtype=dtype)
x_list = [theano._asarray(filter_range + eps, dtype=dtype)+half_filter_range * softsign(tensor.dot(input, f_list[i]) +
b_list[i]) for i in xrange(n_terms)]
rval = cls.new_expbounds(rng, x_list, n_out, dtype=dtype, params=f_list + b_list,
exponent_range=exponent_range)
half_filter_range = theano._asarray(filter_range / 2,
dtype=dtype)
x_list = [
theano._asarray(filter_range + eps, dtype=dtype) +
half_filter_range * softsign(
tensor.dot(input, f_list[i]) + b_list[i])
for i in xrange(n_terms)]
rval = cls.new_expbounds(
rng, x_list, n_out, dtype=dtype, params=f_list + b_list,
exponent_range=exponent_range)
rval.f_list = f_list
rval.input = input # add the input to the returned object
rval.filter_l1 = sum(abs(fi).sum() for fi in f_list)
......@@ -183,6 +216,8 @@ class Kouh2008(object):
def img_from_weights(self, rows=None, cols=None, row_gap=1, col_gap=1, eps=1e-4):
""" Return an image that visualizes all the weights in the layer.
"""
if Image is None:
raise ImportError("No module named PIL")
n_in, n_out = self.f_list[0].value.shape
......@@ -190,10 +225,12 @@ class Kouh2008(object):
rows = int(numpy.sqrt(n_out))
if cols is None:
cols = n_out // rows
if n_out % rows: cols += 1
if n_out % rows:
cols += 1
if rows is None:
rows = n_out // cols
if n_out % cols: rows += 1
if n_out % cols:
rows += 1
filter_shape = self.filter_shape
height = rows * (row_gap + filter_shape[0]) - row_gap
......@@ -203,34 +240,40 @@ class Kouh2008(object):
w = self.w.value
w_col = 0
def pixel_range(x):
return 255 * (x - x.min()) / (x.max() - x.min() + eps)
for r in xrange(rows):
out_r_low = r*(row_gap + filter_shape[0])
out_r_low = r * (row_gap + filter_shape[0])
out_r_high = out_r_low + filter_shape[0]
for c in xrange(cols):
out_c_low = c*(col_gap + filter_shape[1])
out_c_low = c * (col_gap + filter_shape[1])
out_c_high = out_c_low + filter_shape[1]
out_tile = out_array[out_r_low:out_r_high, out_c_low:out_c_high, :]
out_tile = out_array[out_r_low:out_r_high,
out_c_low:out_c_high,
:]
if c % 3 == 0: # linear filter
if w_col < w.shape[1]:
out_tile[...] = pixel_range(w[:, w_col]).reshape(filter_shape+(1,))
out_tile[...] = pixel_range(
w[:, w_col]).reshape(filter_shape + (1,))
w_col += 1
if c % 3 == 1: # E filters
if w_col < w.shape[1]:
# filters after the 3rd do not get rendered, but are skipped over.
# there are only 3 colour channels.
for i in xrange(min(self.n_E_quadratic, 3)):
out_tile[:, :, i] = pixel_range(w[:, w_col+i]).reshape(filter_shape)
out_tile[:, :, i] = pixel_range(
w[:, w_col + i]).reshape(filter_shape)
w_col += self.n_E_quadratic
if c % 3 == 2: # S filters
if w_col < w.shape[1]:
# filters after the 3rd do not get rendered, but are skipped over.
# there are only 3 colour channels.
for i in xrange(min(self.n_S_quadratic, 3)):
out_tile[:, :, 2-i] = pixel_range(w[:, w_col+i]).reshape(filter_shape)
out_tile[:, :, 2 - i] = pixel_range(
w[:, w_col + i]).reshape(filter_shape)
w_col += self.n_S_quadratic
return Image.fromarray(out_array, 'RGB')
......@@ -264,8 +307,9 @@ class Config(object):
ft_batchsize = 30
ft_epoch_len = 50000
ft_status_interval = 50 # property( lambda s:s.ft_epoch_len/s.ft_batchsize)
ft_validation_interval = property( lambda s: s.ft_epoch_len/s.ft_batchsize)
ft_status_interval = 50 # property(lambda s:s.ft_epoch_len/s.ft_batchsize)
ft_validation_interval = property(
lambda s: s.ft_epoch_len / s.ft_batchsize)
ft_ntrain_limit = 0
ft_test_lag1 = True
......@@ -290,14 +334,15 @@ if 0:
debug = False
if isinstance(theano.compile.mode.get_default_mode(),
theano.compile.debugmode.DebugMode):
theano.compile.debugmode.DebugMode):
debug = True
# get symbolic train set
s_lr = theano.tensor.fscalar()
if not debug:
sshape = (None, 784)
else: sshape = (None, 3)
else:
sshape = (None, 3)
x = theano.tensor.TensorType(dtype=conf.dtype, broadcastable=(0, 0), shape=sshape)()
y = theano.tensor.lvector()
......@@ -315,7 +360,8 @@ if 0:
print(layer.params)
gparams = theano.tensor.grad(cost, layer.params)
updates = [(p, p - s_lr*gp) for p, gp in zip(layer.params, gparams)]
updates = [
(p, p - s_lr * gp) for p, gp in zip(layer.params, gparams)]
train_nll = pfunc([x, y, s_lr], [], updates=updates)
......
theano/sandbox/cuda/tests/test_blas.py
浏览文件 @ b69ad54d
......@@ -8,31 +8,31 @@ from theano import tensor
from theano.tests import unittest_tools
import numpy
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
raise SkipTest('Optional package cuda disabled')
import theano.sandbox.cuda as tcn
from theano.tensor.signal.pool import (Pool,
PoolGrad, DownsampleFactorMaxGradGrad)
import theano.compile.mode
from theano.tensor.tests.test_blas import BaseGemv, TestBlasStrides, TestGer
from theano.sandbox.cuda.blas import gpu_gemv_no_inplace, gpu_gemv_inplace
from theano.sandbox.cuda.blas import gpu_ger_inplace, gpu_ger_no_inplace
from theano.sandbox.cuda.blas import batched_dot, GpuBatchedDot
from theano.tensor.signal.pool import (Pool, PoolGrad, DownsampleFactorMaxGradGrad)
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
mode_with_gpu = theano.compile.mode.get_mode(
'FAST_RUN').including('gpu')
mode_without_gpu = theano.compile.mode.get_mode(
'FAST_RUN').excluding('gpu')
'FAST_RUN').excluding('gpu')
else:
mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')
mode_with_gpu = theano.compile.mode.get_default_mode(
).including('gpu')
mode_without_gpu = theano.compile.mode.get_default_mode(
).excluding('gpu')
# The CPU tests already compare C/Py, so we only check C/GPU
mode_with_gpu = copy.copy(mode_with_gpu)
......@@ -55,73 +55,81 @@ class TestBatchedDot(unittest_tools.InferShapeTester):
def cmp(a_shp, b_shp):
a=numpy.random.randn(*a_shp).astype(numpy.float32)
b=numpy.random.randn(*b_shp).astype(numpy.float32)
a = numpy.random.randn(* a_shp).astype(numpy.float32)
b = numpy.random.randn(* b_shp).astype(numpy.float32)
x=tensor.ftensor3()
y=tensor.ftensor3()
x = tensor.ftensor3()
y = tensor.ftensor3()
f=theano.function([x,y], batched_dot(x,y), mode=mode_with_gpu)
f = theano.function([x, y],
batched_dot(x, y),
mode=mode_with_gpu)
z0=numpy.asarray(f(a,b))
z0 = numpy.asarray(f(a, b))
ga = cuda_ndarray.CudaNdarray(a)
gb = cuda_ndarray.CudaNdarray(b)
z1=numpy.asarray(f(ga,gb))
z1 = numpy.asarray(f(ga, gb))
z_test = numpy.sum(a[:,:,:,None]*b[:,None,:,:],axis=-2)
z_test = numpy.sum(
a[:, :, :, None] * b[:, None, :, :], axis=-2)
z1 = numpy.asarray(f(ga, gb))
z_test = numpy.sum(
a[:, :, :, None] * b[:, None, :, :], axis=-2)
unittest_tools.assert_allclose(z0, z_test)
unittest_tools.assert_allclose(z1, z_test)
cmp((5,4,3), (5,3,2))
cmp((5,3,3), (5,3,3))
cmp((5,2,6), (5,6,3))
cmp((5, 4, 3), (5, 3, 2))
cmp((5, 3, 3), (5, 3, 3))
cmp((5, 2, 6), (5, 6, 3))
# Test dimensions of 0
cmp((0,2,6), (0,6,3))
cmp((5,0,3), (5,3,2))
cmp((5,4,0), (5,0,2))
cmp((5,4,3), (5,3,0))
cmp((0,0,0), (0,0,0))
cmp((0, 2, 6), (0, 6, 3))
cmp((5, 0, 3), (5, 3, 2))
cmp((5, 4, 0), (5, 0, 2))
cmp((5, 4, 3), (5, 3, 0))
cmp((0, 0, 0), (0, 0, 0))
# Test dimensions of 1
cmp((1,2,6), (1,6,3))
cmp((5,1,3), (5,3,2))
cmp((5,4,1), (5,1,2))
cmp((5,4,3), (5,3,1))
cmp((1, 2, 6), (1, 6, 3))
cmp((5, 1, 3), (5, 3, 2))
cmp((5, 4, 1), (5, 1, 2))
cmp((5, 4, 3), (5, 3, 1))
def test_batched_dot_errors(self):
def fail(a_shp, b_shp):
a=numpy.random.randn(*a_shp).astype(numpy.float32)
b=numpy.random.randn(*b_shp).astype(numpy.float32)
a = numpy.random.randn(* a_shp).astype(numpy.float32)
b = numpy.random.randn(* b_shp).astype(numpy.float32)
x=tensor.ftensor3()
y=tensor.ftensor3()
x = tensor.ftensor3()
y = tensor.ftensor3()
f=theano.function([x,y], batched_dot(x,y), mode=mode_with_gpu)
f = theano.function([x, y],
batched_dot(x, y),
mode=mode_with_gpu)
z = f(a,b)
f(a, b)
# Different batch size
self.assertRaises(RuntimeError, fail, (5,4,3), (6,3,2))
self.assertRaises(RuntimeError, fail, (5, 4, 3), (6, 3, 2))
# Shape mismatch
self.assertRaises(RuntimeError, fail, (5,4,3), (5,2,2))
self.assertRaises(RuntimeError, fail, (5, 4, 3), (5, 2, 2))
def test_batched_dot_gradient(self):
for threshold in [0, 100]:
unittest_tools.verify_grad(
GpuBatchedDot(stream_threshold=threshold),
[numpy.random.randn(5,7,2).astype(numpy.float32),
numpy.random.randn(5,2,6).astype(numpy.float32)],
mode=mode_with_gpu)
unittest_tools.verify_grad(
batched_dot, [
numpy.random.randn(5, 7, 2).astype(numpy.float32),
numpy.random.randn(5, 2, 6).astype(numpy.float32)],
mode=mode_with_gpu)
def test_infer_shape(self):
# only matrix/matrix is supported
# only matrix / matrix is supported
admat = tensor.ftensor3()
bdmat = tensor.ftensor3()
admat_val = my_rand(7, 4, 5)
......@@ -134,24 +142,23 @@ class TestBatchedDot(unittest_tools.InferShapeTester):
def test_dot22():
def cmp(a_shp, b_shp):
a0 = my_rand(*a_shp)
a0 = my_rand(* a_shp)
a = tcn.shared_constructor(a0, 'a')
b = tensor.fmatrix()
f = pfunc([b], [], updates=[(a, tensor.dot(a, b))], mode=mode_with_gpu)
bval = my_rand(*b_shp)
bval = my_rand(* b_shp)
f(bval)
assert numpy.allclose(numpy.dot(a0, bval), a.get_value())
# Try with a matrix equal to a0, but with strides in both dims
a.set_value(a0)
a.set_value(
a.get_value(borrow=True,
a.set_value(a.get_value(borrow=True,
return_internal_type=True)[::-1, ::-1],
borrow=True)
borrow=True)
f(bval)
cmp((3, 4), (4, 5))
......@@ -171,12 +178,12 @@ def test_dot22scalar():
bv = my_rand(*b_shp)
f = theano.function(
[a, b],
tensor.dot(a, b) * numpy.asarray(4, 'float32'),
mode=mode_with_gpu)
[a, b],
tensor.dot(a, b) * numpy.asarray(4, 'float32'),
mode=mode_with_gpu)
f2 = theano.function(
[a, b],
tensor.dot(a, b) * numpy.asarray(4, 'float32'))
[a, b],
tensor.dot(a, b) * numpy.asarray(4, 'float32'))
t = f.maker.fgraph.toposort()
assert any([isinstance(n.op, tcn.blas.GpuDot22Scalar) for n in t])
# assert any([isinstance(n.op, tcn.basic_ops.GpuAllocEmpty)
......@@ -220,23 +227,22 @@ def test_gemm():
c = tensor.fmatrix('c')
f = pfunc([b, c], [], updates=[(a, tensor.dot(a, b) + tensor.exp(c))],
mode=mode_with_gpu)
mode=mode_with_gpu)
assert any([node.op == tcn.blas.gpu_gemm_inplace
for node in f.maker.fgraph.toposort()])
for node in f.maker.fgraph.toposort()])
bval = my_rand(*b_shp)
bval = my_rand(* b_shp)
cval = my_rand(a_shp[0], b_shp[1])
f(bval, cval)
assert numpy.allclose(numpy.dot(a0, bval) + numpy.exp(cval),
a.get_value())
a.get_value())
# Try with a matrix equal to a0, but with strides in both dims
a.set_value(a0)
a.set_value(
a.get_value(borrow=True,
return_internal_type=True)[::-1, ::-1],
borrow=True)
a.set_value(a.get_value(borrow=True,
return_internal_type=True)[::-1, ::-1],
borrow=True)
f(bval, cval)
cmp((3, 4), (4, 5))
......@@ -250,7 +256,7 @@ def test_gemm():
def test_gemm_no_inplace():
def cmp(a_shp, b_shp):
a0 = my_rand(*a_shp)
a0 = my_rand(* a_shp)
a = tcn.shared_constructor(a0, 'a')
cval = my_rand(a_shp[0], b_shp[1])
c = tcn.shared_constructor(cval.copy(), 'c')
......@@ -258,14 +264,13 @@ def test_gemm_no_inplace():
b = tcn.fmatrix('b')
b2 = tcn.fmatrix('b2')
f = pfunc(
[b, b2],
[tensor.dot(a, b2) + c],
updates=[(a, tensor.dot(a, b) + c)],
mode=mode_with_gpu)
f = pfunc([b, b2],
[tensor.dot(a, b2) + c],
updates=[(a, tensor.dot(a, b) + c)],
mode=mode_with_gpu)
assert any([node.op == tcn.blas.gpu_gemm_no_inplace
for node in f.maker.fgraph.toposort()])
for node in f.maker.fgraph.toposort()])
bval = my_rand(*b_shp)
bval2 = my_rand(*b_shp)
rval = f(bval, bval2)
......@@ -276,9 +281,10 @@ def test_gemm_no_inplace():
# Try with a matrix equal to a0, but with strides in both dims
a.set_value(a0)
a.set_value(
a.get_value(borrow=True,
return_internal_type=True)[::-1, ::-1],
borrow=True)
a.get_value(
borrow=True,
return_internal_type=True)[::-1, ::-1],
borrow=True)
f(bval, bval2)
cmp((3, 4), (4, 5))
......@@ -303,8 +309,8 @@ if 0:
def test_maxpool():
"""TODO: test the gpu version!!! """
for d0, d1, r_true, r_false in [(4, 4, [[[[5, 7], [13, 15]]]], [[[[5, 7], [13, 15]]]]),
(5, 5, [[[[6, 8], [ 16, 18], [ 21, 23]]]],
[[[[6, 8, 9], [ 16, 18, 19], [ 21, 23, 24]]]])]:
(5, 5, [[[[6, 8], [16, 18], [21, 23]]]],
[[[[6, 8, 9], [16, 18, 19], [21, 23, 24]]]])]:
for border, ret in [(True, r_true), (False, r_false)]:
ret = numpy.array(ret)
a = tcn.blas.Pool((2, 2), border)
......@@ -312,7 +318,7 @@ if 0:
b = dmatrix4()
f = pfunc([b], [a(b)], mode=mode_with_gpu)
bval = numpy.arange(0, d0*d1).reshape(1, 1, d0, d1)
bval = numpy.arange(0, d0 * d1).reshape(1, 1, d0, d1)
r = f(bval)[0]
# print bval, bval.shape, border
# print r, r.shape
......@@ -347,8 +353,7 @@ def test_downsample():
(1, 1, 1025, 10),
(1, 1, 1023, 10),
(65536, 1, 10, 10),
(1, 65536, 10, 10),
]
(1, 65536, 10, 10), ]
numpy.random.RandomState(unittest_tools.fetch_seed()).shuffle(shps)
......@@ -368,14 +373,14 @@ def test_downsample():
a = tcn.shared_constructor(my_rand(*shp), 'a')
f = pfunc([], ds_op(tensor.as_tensor_variable(a)),
mode=mode_with_gpu.excluding('cudnn'))
mode=mode_with_gpu.excluding('cudnn'))
f2 = pfunc([], ds_op(tensor.as_tensor_variable(a)),
mode=mode_without_gpu)
mode=mode_without_gpu)
assert any([isinstance(node.op,
tcn.blas.GpuDownsampleFactorMax)
for node in f.maker.fgraph.toposort()])
for node in f.maker.fgraph.toposort()])
assert any([isinstance(node.op, Pool)
for node in f2.maker.fgraph.toposort()])
for node in f2.maker.fgraph.toposort()])
assert numpy.allclose(f(), f2())
# The grad is too slow on GT220 GPU
......@@ -387,15 +392,15 @@ def test_downsample():
continue
g = pfunc(
[],
tensor.grad(ds_op(tensor.as_tensor_variable(a)).sum(),
a),
mode=mode_with_gpu.excluding('cudnn'))
[],
tensor.grad(ds_op(tensor.as_tensor_variable(a)).sum(),
a),
mode=mode_with_gpu.excluding('cudnn'))
g2 = pfunc(
[],
tensor.grad(ds_op(tensor.as_tensor_variable(a)).sum(),
a),
mode=mode_without_gpu)
[],
tensor.grad(ds_op(tensor.as_tensor_variable(a)).sum(),
a),
mode=mode_without_gpu)
assert any([isinstance(node.op,
tcn.blas.GpuDownsampleFactorMaxGrad)
for node in g.maker.fgraph.toposort()])
......@@ -413,11 +418,12 @@ def test_downsample():
gg = pfunc([], ggf, mode=gpu_mode)
gg2 = pfunc([], ggf, mode=ref_mode)
assert any([isinstance(node.op,
tcn.blas.GpuDownsampleFactorMaxGradGrad)
for node in gg.maker.fgraph.toposort()])
assert any([isinstance(node.op, DownsampleFactorMaxGradGrad)
for node in gg2.maker.fgraph.toposort()])
assert any([isinstance(
node.op, tcn.blas.GpuDownsampleFactorMaxGradGrad)
for node in gg.maker.fgraph.toposort()])
assert any([isinstance(
node.op, DownsampleFactorMaxGradGrad)
for node in gg2.maker.fgraph.toposort()])
assert numpy.allclose(gg(), gg2()), shp
# We already check that the gpu version return
......@@ -434,6 +440,7 @@ class TestGpuGemv(TestCase, BaseGemv,
gemv = gpu_gemv_no_inplace
gemv_inplace = gpu_gemv_inplace
# Mimic shared constructors registry
@staticmethod
def shared(val):
# If we don't put shared on the GPU, we won't be able to test
......@@ -445,7 +452,7 @@ class TestGpuGemv(TestCase, BaseGemv,
class TestGpuGemvNoTransfer(TestCase, BaseGemv,
unittest_tools.TestOptimizationMixin):
unittest_tools.TestOptimizationMixin):
mode = mode_with_gpu
dtype = 'float32'
......@@ -471,13 +478,13 @@ class TestVectorMatrixDot(TestCase):
''' Test vector dot matrix '''
v = theano.shared(numpy.array(numpy.random.rand(2), dtype='float32'))
m = theano.shared(numpy.array(numpy.random.rand(2, 5),
dtype='float32'))
dtype='float32'))
no_gpu_f = theano.function([], theano.dot(v, m), mode=mode_without_gpu)
gpu_f = theano.function([], theano.dot(v, m), mode=mode_with_gpu)
# gpu_f2 is needed to test the case when the input is not on the gpu
# but the output is moved to the gpu.
gpu_f2 = theano.function([], tcn.gpu_from_host(theano.dot(v, m)),
mode=mode_with_gpu)
mode=mode_with_gpu)
# Assert they produce the same output
assert numpy.allclose(no_gpu_f(), gpu_f(), atol=self.atol)
......@@ -490,9 +497,9 @@ class TestVectorMatrixDot(TestCase):
# Check double-strided m
m.set_value(
m.get_value(borrow=True,
return_internal_type=True)[::-1, ::-1],
borrow=True)
m.get_value(borrow=True,
return_internal_type=True)[::-1, ::-1],
borrow=True)
assert numpy.allclose(no_gpu_f(), gpu_f(), atol=self.atol)
assert numpy.allclose(no_gpu_f(), gpu_f2(), atol=self.atol)
......@@ -500,13 +507,13 @@ class TestVectorMatrixDot(TestCase):
''' Test matrix dot vector '''
v = theano.shared(numpy.array(numpy.random.rand(2), dtype='float32'))
m = theano.shared(numpy.array(numpy.random.rand(5, 2),
dtype='float32'))
dtype='float32'))
no_gpu_f = theano.function([], theano.dot(m, v), mode=mode_without_gpu)
gpu_f = theano.function([], theano.dot(m, v), mode=mode_with_gpu)
# gpu_f2 is needed to test the case when the input is not on the gpu
# but the output is moved to the gpu.
gpu_f2 = theano.function([], tcn.gpu_from_host(theano.dot(m, v)),
mode=mode_with_gpu)
mode=mode_with_gpu)
# Assert they produce the same output
assert numpy.allclose(no_gpu_f(), gpu_f(), atol=self.atol)
......@@ -520,19 +527,21 @@ class TestVectorMatrixDot(TestCase):
def test_gemv1(self):
''' test vector1+dot(matrix,vector2) '''
v1 = theano.tensor._shared(numpy.array(numpy.random.rand(2),
dtype='float32'))
dtype='float32'))
v2 = theano.tensor._shared(numpy.array(numpy.random.rand(5),
dtype='float32'))
dtype='float32'))
m = theano.tensor._shared(numpy.array(numpy.random.rand(5, 2),
dtype='float32'))
dtype='float32'))
no_gpu_f = theano.function([], v2 + theano.dot(m, v1),
mode=mode_without_gpu)
mode=mode_without_gpu)
gpu_f = theano.function([], v2 + theano.dot(m, v1), mode=mode_with_gpu)
# gpu_f2 is needed to test the case when the input is not on the gpu
# but the output is moved to the gpu.
gpu_f2 = theano.function([], tcn.gpu_from_host(v2 + theano.dot(m, v1)),
mode=mode_with_gpu)
gpu_f2 = theano.function(
[],
tcn.gpu_from_host(v2 + theano.dot(m, v1)),
mode=mode_with_gpu)
# Assert they produce the same output
assert numpy.allclose(no_gpu_f(), gpu_f(), atol=self.atol)
......@@ -548,16 +557,17 @@ class TestVectorMatrixDot(TestCase):
v1 = theano.shared(numpy.array(numpy.random.rand(5), dtype='float32'))
v2 = tensor._shared(numpy.array(numpy.random.rand(2), dtype='float32'))
m = theano.shared(numpy.array(numpy.random.rand(5, 2),
dtype='float32'))
dtype='float32'))
no_gpu_f = theano.function([], v2 + theano.dot(v1, m),
mode=mode_without_gpu)
mode=mode_without_gpu)
gpu_f = theano.function([], v2 + theano.dot(v1, m),
mode=mode_with_gpu)
mode=mode_with_gpu)
# gpu_f2 is needed to test the case when the input is not on the gpu
# but the output is moved to the gpu.
gpu_f2 = theano.function([], tcn.gpu_from_host(v2 + theano.dot(v1, m)),
mode=mode_with_gpu)
gpu_f2 = theano.function(
[], tcn.gpu_from_host(v2 + theano.dot(v1, m)),
mode=mode_with_gpu)
# Assert they produce the same output
assert numpy.allclose(no_gpu_f(), gpu_f(), atol=self.atol)
......
theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py
浏览文件 @ b69ad54d
......@@ -2,14 +2,16 @@
Tests for GPU convolution
"""
from __future__ import absolute_import, print_function, division
import sys
import time
import unittest
import traceback
import theano
from theano import tensor
from theano.tests.unittest_tools import seed_rng, assert_allclose
from theano.sandbox import cuda
import numpy
from six.moves import xrange
from theano.sandbox.cuda.dnn import GpuDnnConv, DnnBase, dnn_conv
from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
imported_scipy_convolve2d = False
......@@ -19,16 +21,10 @@ try:
except ImportError:
pass
import theano
from theano import tensor
from theano.tests.unittest_tools import seed_rng, assert_allclose
# Skip test if cuda is not available.
from theano.sandbox import cuda
if cuda.cuda_available == False:
if cuda.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
from theano.sandbox.cuda.dnn import GpuDnnConv, DnnBase, dnn_conv
# needed as the gpu conv don't have a perform implementation.
if theano.config.mode == 'FAST_COMPILE':
......@@ -56,8 +52,8 @@ device_prop = cuda_ndarray.device_properties(device_id)
def py_conv_valid_numpy(img, kern):
assert img.shape[1] == kern.shape[1]
outshp = (img.shape[0], kern.shape[0],
img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1)
img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1)
out = numpy.zeros(outshp, dtype='float32')
for b in xrange(out.shape[0]):
for k in xrange(out.shape[1]):
......@@ -106,11 +102,11 @@ def py_conv(img, kern, mode, subsample):
if imported_scipy_convolve2d:
return py_conv_scipy(img, kern, mode, subsample)
elif mode == 'valid':
return py_conv_valid_numpy(img, kern)[:, :, ::subsample[0],
::subsample[1]]
return py_conv_valid_numpy(img, kern)[
:, :, ::subsample[0], ::subsample[1]]
elif mode == 'full':
return py_conv_full_numpy(img, kern)[:, :, ::subsample[0],
::subsample[1]]
return py_conv_full_numpy(img, kern)[
:, :, ::subsample[0], ::subsample[1]]
else:
raise Exception("Can't execute this kernel.")
......@@ -119,20 +115,20 @@ def py_conv_scipy(img, kern, mode, subsample):
assert img.shape[1] == kern.shape[1]
if mode == 'valid':
outshp = (img.shape[0], kern.shape[0],
img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1)
img.shape[2] - kern.shape[2] + 1,
img.shape[3] - kern.shape[3] + 1)
else:
outshp = (img.shape[0], kern.shape[0],
img.shape[2] + kern.shape[2] - 1,
img.shape[3] + kern.shape[3] - 1)
img.shape[2] + kern.shape[2] - 1,
img.shape[3] + kern.shape[3] - 1)
out = numpy.zeros(outshp, dtype='float32')
for b in xrange(out.shape[0]):
for k in xrange(out.shape[1]):
for s in xrange(img.shape[1]):
#convolve2d or correlate
# convolve2d or correlate
out[b, k, :, :] += convolve2d(img[b, s, :, :],
kern[k, s, :, :],
mode)
kern[k, s, :, :],
mode)
return out[:, :, ::subsample[0], ::subsample[1]]
......@@ -168,10 +164,12 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
npy_kern = theano._asarray(numpy.random.rand(*kshape) - 2,
dtype='float32')
else:
npy_img = theano._asarray(numpy.arange(
numpy.prod(ishape)).reshape(ishape), dtype='float32') + 1
npy_kern = -(theano._asarray(numpy.arange(
numpy.prod(kshape)).reshape(kshape), dtype='float32') + 1)
npy_img = theano._asarray(
numpy.arange(numpy.prod(ishape)).reshape(ishape),
dtype='float32') + 1
npy_kern = -(theano._asarray(
numpy.arange(numpy.prod(kshape)).reshape(kshape),
dtype='float32') + 1)
img = cuda_ndarray.CudaNdarray(npy_img)
kern = cuda_ndarray.CudaNdarray(npy_kern)
......@@ -239,7 +237,7 @@ def _params_allgood(ishape, kshape, mode, subsample=(1, 1), img_stride=(1, 1),
div = float('inf')
print('%15s' % str(ishape), '%15s' % str(kshape), end=' ')
print('%12.5f %7.2f %7.2f %7.1f' % (
approx_fp, cpu_mflops, gpu_mflops, div))
approx_fp, cpu_mflops, gpu_mflops, div))
def exec_conv(version, shapes, verbose, random, mode,
......@@ -261,7 +259,7 @@ def get_basic_shapes():
return [((1, 1, 1, 1), (1, 1, 1, 1), (1, 1), (1, 1), (1, 1)),
((1, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 3, 3), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
# basic test for unsquare kernel and image
# basic test for unsquare kernel and image
((1, 1, 2, 4), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 3, 4), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 4, 3), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
......@@ -281,17 +279,17 @@ def get_shapes(imshp=(1, 1), kshp=(1, 1), subsample=(1, 1),
((3, 1) + imshp, (1, 1) + kshp, subsample, img_stride, kern_stride),
# nkern only
((1, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride),
#batch and nkern
# batch and nkern
((3, 1) + imshp, (2, 1) + kshp, subsample, img_stride, kern_stride),
#batch and stack
# batch and stack
((3, 2) + imshp, (1, 2) + kshp, subsample, img_stride, kern_stride),
#stack and nkern
# stack and nkern
((1, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride),
#batch, nkern and stack
# batch, nkern and stack
((2, 2) + imshp, (2, 2) + kshp, subsample, img_stride, kern_stride),
#batch, nkern and stack
# batch, nkern and stack
((3, 2) + imshp, (4, 2) + kshp, subsample, img_stride, kern_stride)
]
]
def get_shapes2(scales_img=(1, 1), scales_kern=(1, 1), subsample=(1, 1),
......@@ -344,39 +342,39 @@ def get_valid_shapes():
# test subsample done in a separate fct
shapes += [
# other test
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
, ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
, ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
, ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
, ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize
, ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image
, ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image, non-square kern
, ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
, ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)) # a big one
, ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # MNIST LeNET layer 1
, ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)) # layer 1 backprop to weights
, ((60, 20, 28, 28), (10, 20, 5, 5), (1, 1), (2, 2), (1, 1)) # added a test case that fail from test_nnet.py.test_conv_nnet2
, ((10, 5, 28, 28), (10, 5, 5, 5), (1, 1), (2, 2), (1, 1)) # test precedent but reduced that triger the error
# Test more than maxThreadsDim0
, ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
, ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
]
# other test
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize,
((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image,
((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image, non-square kern,
((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim,
((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)), # a big one
((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # MNIST LeNET layer 1
((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)), # layer 1 backprop to weights
((60, 20, 28, 28), (10, 20, 5, 5), (1, 1), (2, 2), (1, 1)), # added a test case that fail from test_nnet.py.test_conv_nnet2
((10, 5, 28, 28), (10, 5, 5, 5), (1, 1), (2, 2), (1, 1)), # test precedent but reduced that triger the error
# Test more than maxThreadsDim0
((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
]
shapes += [ ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 1 layers
, ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 2 layers
, ((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 bprop 1 full
, ((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)) # test_lenet_28 bprop 2 valid
# , ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid
, ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 1 layers
, ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 2 layers
, ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 full
# , ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 1
# , ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 2
]
shapes += [((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 1 layers
((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 2 layers
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 1 full
((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 2 valid
# ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 2 valid
((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 1 layers
((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 2 layers
((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 full
# ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1)), # test_lenet_64 bprop 1
# ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1)) # test_lenet_64 bprop 2
]
return shapes
......@@ -466,48 +464,47 @@ def _test_full(cls, mode=None, version=[-1], extra_shapes=[],
shapes += [
# other test
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
, ((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1))
, ((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1))
, ((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
, ((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1))
, ((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize
, ((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image
, ((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize, non-square image, non-square kern
, ((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)) # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
, ((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)) # a big one
, ((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # MNIST LeNET layer 1
, ((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)) # layer 1 backprop to weights
]
((2, 1, 2, 2), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((3, 2, 4, 4), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 2), (1, 1), (1, 1), (1, 1)),
((1, 1, 4, 4), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 3), (1, 1), (1, 1), (1, 1)),
((4, 1, 10, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
((4, 1, 20, 10), (1, 1, 2, 10), (1, 1), (1, 1), (1, 1)),
((3, 2, 8, 8), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize
((3, 2, 8, 6), (4, 2, 4, 4), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image
((3, 2, 8, 6), (4, 2, 4, 3), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize, non-square image, non-square kern
((3, 2, 8, 6), (4, 2, 4, 6), (1, 1), (1, 1), (1, 1)), # stack, nkern, bsize ,non-square image, non-square kern, kernsize==imgsize on one dim
((16, 5, 64, 64), (8, 5, 8, 8), (1, 1), (1, 1), (1, 1)), # a big one
((16, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # MNIST LeNET layer 1
((20, 16, 32, 32), (1, 16, 28, 28), (1, 1), (1, 1), (1, 1)) # layer 1 backprop to weights
]
if test_bigger_kernels:
# Shapes where the kernel is larger than the image in some dimension
shapes += [
((3, 1, 1, 1), (2, 1, 5, 3), (1, 1), (1, 1), (1, 1))
, ((3, 2, 1, 1), (4, 2, 1, 1), (1, 1), (1, 1), (1, 1))
, ((3, 2, 4, 4), (4, 2, 2, 6), (1, 1), (1, 1), (1, 1))
, ((3, 2, 4, 4), (4, 2, 8, 6), (1, 1), (1, 1), (1, 1))
, ((4, 2, 10, 10), (3, 2, 2, 12), (1, 1), (1, 1), (1, 1))
((3, 1, 1, 1), (2, 1, 5, 3), (1, 1), (1, 1), (1, 1)),
((3, 2, 1, 1), (4, 2, 1, 1), (1, 1), (1, 1), (1, 1)),
((3, 2, 4, 4), (4, 2, 2, 6), (1, 1), (1, 1), (1, 1)),
((3, 2, 4, 4), (4, 2, 8, 6), (1, 1), (1, 1), (1, 1)),
((4, 2, 10, 10), (3, 2, 2, 12), (1, 1), (1, 1), (1, 1))
]
shapes += [
# ((60,1,28,28),(20,1,5,5), (1, 1), (1, 1), (1, 1))#test_lenet_28 1 layers
# , ((60,20,12,12),(30,20,5,5), (1, 1), (1, 1), (1, 1))#test_lenet_28 2 layers
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # test_lenet_28 bprop 1 full
# , ((20,60,12,12),(30,60,8,8), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid
# , ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1))#test_lenet_28 bprop 2 valid
# , ((10,1,64,64),(20,1,7,7), (1, 1), (1, 1), (1, 1))#test_lenet_64 1 layers
# , ((10,20,29,29),(30,20,7,7), (1, 1), (1, 1), (1, 1))#test_lenet_64 2 layers
, ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # test_lenet_64 full
# , ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 1
# , ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1))#test_lenet_64 bprop 2
# Test more than maxThreadsDim0
, ((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
, ((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1))
, ((1, 1, 44800, 1), (6, 1, 1, 1), (1, 1), (1, 1), (1, 1)) # This caused crash
]
shapes += [((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 1 layers
# ((60, 20, 12, 12),(30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 2 layers
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 1 full
# ((20,60,12,12),(30,60,8,8), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 2 valid
# ((1,60,28,28),(20,60,24,24), (1, 1), (1, 1), (1, 1)), # test_lenet_28 bprop 2 valid
# ((10,1,64,64),(20,1,7,7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 1 layers
# ((10,20,29,29),(30,20,7,7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 2 layers
((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # test_lenet_64 full
# ((20,10,29,29),(30,10,23,23), (1, 1), (1, 1), (1, 1)), # test_lenet_64 bprop 1
# ((1,10,64,64),(20,10,58,58), (1, 1), (1, 1), (1, 1)), # test_lenet_64 bprop 2
# Test more than maxThreadsDim0
((2, 4, 13, 1050), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
((2, 4, 1050, 13), (3, 4, 10, 11), (1, 1), (1, 1), (1, 1)),
((1, 1, 44800, 1), (6, 1, 1, 1), (1, 1), (1, 1), (1, 1)) # This caused crash
]
verbose = 0
random = True
......@@ -561,7 +558,7 @@ def _test_subsample(cls, mode, version_valid=[-1], version_full=[-1]):
((4, 2, 10, 10), (3, 2, 2, 2), (1, 3), (1, 1), (1, 1)),
((4, 2, 10, 10), (3, 2, 2, 2), (3, 3), (1, 1), (1, 1)),
((4, 2, 10, 10), (3, 2, 2, 2), (3, 1), (1, 1), (1, 1))
]
]
shapes += get_shapes2(scales_img=(2, 2), subsample=(1, 1))
shapes += get_shapes2(scales_img=(2, 2), subsample=(1, 2))
shapes += get_shapes2(scales_img=(2, 2), subsample=(2, 1))
......@@ -636,7 +633,6 @@ class TestConv2DGPU(unittest.TestCase):
imshp_logical=featshp_logical[1:],
kshp_logical=kshp[2:])
def test_invalid_input_shape(self):
"""
Tests that when the shape gived at build time is not the same as
......@@ -659,7 +655,7 @@ class TestConv2DGPU(unittest.TestCase):
for mode in ['valid', 'full']:
for shapes in [((3, 2, 8, 8), (4, 2, 5, 5), (8, 8)),
((3, 2, 8, 8), (4, 2, 5, 5), (5, 8)),
#((3, 2, 8, 8), (4, 2, 5, 5), (8, 5)),
# ((3, 2, 8, 8), (4, 2, 5, 5), (8, 5)),
# We use only the number of columns.
]:
......@@ -700,11 +696,11 @@ class TestConvWithPadding(object):
kern = theano._asarray(numpy.empty((1, 1, 1, 1)), dtype='float32')
for i in self.conv_ops:
assert_raises(ValueError, i, img, kern,
border_mode=(-1, 0))
border_mode=(-1, 0))
assert_raises(ValueError, i, img, kern,
border_mode=(0, -1))
border_mode=(0, -1))
assert_raises(ValueError, i, img, kern,
border_mode='not border')
border_mode='not border')
def _run_onecase(self, img_shape, kern_shape, padding, op):
npy_img = numpy.random.rand(*img_shape).astype('float32')
......@@ -776,9 +772,9 @@ def gemm_directly(bs, ch, nf, rImg1, rImg2, rFlt1, rFlt2, subsx, subsy,
border_mode='valid', subsample=subsample)(i, k)
f = theano.function([i, k], op, mode=theano_mode)
gpuval = numpy.array(f(
npy_img.transpose(1, 0, 2, 3),
npy_kern.transpose(1, 0, 2, 3)[:, :, ::-1, ::-1])).transpose(
1, 0, 2, 3)
npy_img.transpose(1, 0, 2, 3),
npy_kern.transpose(1, 0, 2, 3)[:, :, ::-1, ::-1])
).transpose(1, 0, 2, 3)
assert_allclose(cpuval, gpuval, rtol=1e-4)
......@@ -892,44 +888,44 @@ def benchmark():
shapes_valid = [
# test_lenet_28 shape
((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)) # valid
, ((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)) # valid
, ((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 60, 28, 28), (20, 60, 24, 24), (1, 1), (1, 1), (1, 1)) # valid
((20, 60, 12, 12), (30, 60, 8, 8), (1, 1), (1, 1), (1, 1)), # valid
((60, 20, 12, 12), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
((60, 1, 28, 28), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
((1, 60, 28, 28), (20, 60, 24, 24), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_32 shape
, ((20, 60, 14, 14), (30, 60, 10, 10), (1, 1), (1, 1), (1, 1)) # valid
, ((60, 20, 14, 14), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)) # valid
, ((60, 1, 32, 32), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 60, 32, 32), (20, 60, 28, 28), (1, 1), (1, 1), (1, 1)) # valid
((20, 60, 14, 14), (30, 60, 10, 10), (1, 1), (1, 1), (1, 1)), # valid
((60, 20, 14, 14), (30, 20, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
((60, 1, 32, 32), (20, 1, 5, 5), (1, 1), (1, 1), (1, 1)), # valid
((1, 60, 32, 32), (20, 60, 28, 28), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_64 shape
, ((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)) # valid
, ((20, 10, 29, 29), (30, 10, 23, 23), (1, 1), (1, 1), (1, 1)) # valid
, ((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 10, 64, 64), (20, 10, 58, 58), (1, 1), (1, 1), (1, 1)) # valid
((10, 20, 29, 29), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
((20, 10, 29, 29), (30, 10, 23, 23), (1, 1), (1, 1), (1, 1)), # valid
((10, 1, 64, 64), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
((1, 10, 64, 64), (20, 10, 58, 58), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_108 shape
, ((10, 20, 51, 51), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)) # valid
, ((20, 10, 51, 51), (30, 10, 45, 45), (1, 1), (1, 1), (1, 1)) # valid
, ((10, 1, 108, 108), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 10, 108, 108), (20, 10, 102, 102), (1, 1), (1, 1), (1, 1)) # valid
((10, 20, 51, 51), (30, 20, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
((20, 10, 51, 51), (30, 10, 45, 45), (1, 1), (1, 1), (1, 1)), # valid
((10, 1, 108, 108), (20, 1, 7, 7), (1, 1), (1, 1), (1, 1)), # valid
((1, 10, 108, 108), (20, 10, 102, 102), (1, 1), (1, 1), (1, 1)), # valid
# test_lenet_256 shape
, ((2, 20, 124, 124), (30, 20, 9, 9), (1, 1), (1, 1), (1, 1)) # valid
, ((20, 2, 124, 124), (30, 2, 116, 116), (1, 1), (1, 1), (1, 1)) # valid
, ((2, 1, 256, 256), (20, 1, 9, 9), (1, 1), (1, 1), (1, 1)) # valid
, ((1, 2, 256, 256), (20, 2, 248, 248), (1, 1), (1, 1), (1, 1)) # valid
]
((2, 20, 124, 124), (30, 20, 9, 9), (1, 1), (1, 1), (1, 1)), # valid
((20, 2, 124, 124), (30, 2, 116, 116), (1, 1), (1, 1), (1, 1)), # valid
((2, 1, 256, 256), (20, 1, 9, 9), (1, 1), (1, 1), (1, 1)), # valid
((1, 2, 256, 256), (20, 2, 248, 248), (1, 1), (1, 1), (1, 1)) # valid
]
shapes_full = [
# test_lenet_28 shape
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # full
((60, 30, 8, 8), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # full
# test_lenet_32 shape
, ((60, 30, 10, 10), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)) # full conv_full_patch_stack_padded' N=1
((60, 30, 10, 10), (20, 30, 5, 5), (1, 1), (1, 1), (1, 1)), # full conv_full_patch_stack_padded' N=1
# test_lenet_64 shape
, ((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # full conv_full_patch_stack_padded' N=3
((10, 30, 23, 23), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # full conv_full_patch_stack_padded' N=3
# test_lenet_108 shape
, ((10, 30, 45, 45), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)) # full 'conv_full_patch_stack_padded' N=9
((10, 30, 45, 45), (20, 30, 7, 7), (1, 1), (1, 1), (1, 1)), # full 'conv_full_patch_stack_padded' N=9
# test_lenet_256 shape
, ((2, 30, 116, 116), (20, 30, 9, 9), (1, 1), (1, 1), (1, 1)) # full conv_reference_full
]
((2, 30, 116, 116), (20, 30, 9, 9), (1, 1), (1, 1), (1, 1)) # full conv_reference_full
]
version = [-1]
verbose = 1
......@@ -952,6 +948,6 @@ def test_stack_rows_segfault_070312():
kern = theano.shared(numpy.random.rand(1, 80, 9, 9).astype('float32'))
out = theano.shared(numpy.random.rand(1, 2, 2, 3).astype('float32'))
op = theano.tensor.nnet.conv.ConvOp(imshp=(80, 96, 96), kshp=(9, 9),
nkern=1, bsize=1)
nkern=1, bsize=1)
f = theano.function([], [], updates=[(out, op(img, kern))], mode=theano_mode)
f()
theano/sandbox/cuda/tests/test_cuda_ndarray.py
浏览文件 @ b69ad54d
from __future__ import absolute_import, print_function, division
import time, copy, sys, unittest
import copy
import unittest
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
......@@ -32,7 +33,7 @@ def advantage(cpu_dt, gpu_dt):
def test_host_to_device():
#print >>sys.stdout, 'starting test_host_to_dev'
# print >>sys.stdout, 'starting test_host_to_dev'
for shape in ((), (3,), (2, 3), (3, 4, 5, 6)):
a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
......@@ -52,30 +53,29 @@ def test_host_to_device():
def test_add_iadd_idiv():
for shapes in (
[(5, 5), (5, 1)],
[(5, 5), (1, 5)],
(), (0,), (3,), (2, 3),
(1, 10000000), (10000, 1000), (1000000, 10),
(4100, 33, 34), (33, 4100, 34), (33, 34, 4100),
(4100, 33, 3, 6), (33, 4100, 3, 6), (33, 3, 4100, 6), (33, 3, 6, 4100),
(4100, 3, 34, 6), (3, 4100, 34, 6), (3, 34, 4100, 6), (3, 34, 6, 4100),
(4100, 3, 4, 36), (3, 4100, 4, 36), (3, 4, 4100, 36), (3, 4, 36, 4100),
(0, 0, 0, 0, 0),
(3, 34, 35, 36, 37),
(33, 34, 3, 36, 37),
(33, 34, 35, 36, 3),
(0, 0, 0, 0, 0, 0),
(3, 34, 35, 36, 37, 2),
(33, 34, 3, 36, 37, 2),
(33, 34, 35, 36, 3, 2),
(3, 4, 5, 6, 7, 1025),
(3, 4, 5, 6, 1025, 7),
(3, 4, 5, 1025, 6, 7),
(3, 4, 1025, 5, 6, 7),
(3, 1025, 4, 5, 6, 7),
(1025, 3, 4, 5, 6, 7),
):
for shapes in ([(5, 5), (5, 1)],
[(5, 5), (1, 5)],
(), (0,), (3,), (2, 3),
(1, 10000000), (10000, 1000), (1000000, 10),
(4100, 33, 34), (33, 4100, 34), (33, 34, 4100),
(4100, 33, 3, 6), (33, 4100, 3, 6), (33, 3, 4100, 6), (33, 3, 6, 4100),
(4100, 3, 34, 6), (3, 4100, 34, 6), (3, 34, 4100, 6), (3, 34, 6, 4100),
(4100, 3, 4, 36), (3, 4100, 4, 36), (3, 4, 4100, 36), (3, 4, 36, 4100),
(0, 0, 0, 0, 0),
(3, 34, 35, 36, 37),
(33, 34, 3, 36, 37),
(33, 34, 35, 36, 3),
(0, 0, 0, 0, 0, 0),
(3, 34, 35, 36, 37, 2),
(33, 34, 3, 36, 37, 2),
(33, 34, 35, 36, 3, 2),
(3, 4, 5, 6, 7, 1025),
(3, 4, 5, 6, 1025, 7),
(3, 4, 5, 1025, 6, 7),
(3, 4, 1025, 5, 6, 7),
(3, 1025, 4, 5, 6, 7),
(1025, 3, 4, 5, 6, 7),
):
if isinstance(shapes, tuple):
shape = shapes
shape2 = shapes
......@@ -98,18 +98,12 @@ def test_add_iadd_idiv():
# add don't support stride
if shape == shape2:
t0 = time.time()
bsum = b0 + b1
bsum = b0 + b1
t1 = time.time()
gpu_dt = t1 - t0
t0 = time.time()
asum = a0 + a1
asum = a0 + a1
t1 = time.time()
cpu_dt = t1 - t0
# print shape, 'adding ', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
assert numpy.allclose(asum, numpy.asarray(bsum))
assert numpy.allclose(asum, numpy.asarray(bsum))
# test not contiguous version.
# should raise not implemented.
......@@ -133,23 +127,9 @@ def test_add_iadd_idiv():
raise Exception("You need to modify this case!")
# TODO: b0[...,::-1] don't work
if shape == shape2:
t = False
try:
_c = _b+b1
except TypeError:
t = True
assert t
# test inplace version
t0 = time.time()
b0 += b1
t1 = time.time()
gpu_dt = t1 - t0
t0 = time.time()
a0 += a1
t1 = time.time()
cpu_dt = t1 - t0
# print shape, 'adding inplace', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
assert numpy.allclose(a0, numpy.asarray(b0))
assert numpy.allclose(a0, a0_orig + a1)
......@@ -157,14 +137,14 @@ def test_add_iadd_idiv():
b0 /= b1
a0 /= a1
assert numpy.allclose(a0, numpy.asarray(b0))
assert numpy.allclose(a0, (a0_orig + a1)/a1)
assert numpy.allclose(a0, (a0_orig + a1) / a1)
# test inplace version
# for not contiguous input
b0 += _b
a0 += a1[..., ::-1]
assert numpy.allclose(a0, numpy.asarray(b0))
assert numpy.allclose(a0, (a0_orig+a1)/a1+a1[..., ::-1])
assert numpy.allclose(a0, (a0_orig + a1) / a1 + a1[..., ::-1])
b0 /= _b
a0 /= a1[..., ::-1]
......@@ -174,48 +154,42 @@ def test_add_iadd_idiv():
def test_exp():
#print >>sys.stdout, 'starting test_exp'
# print >>sys.stdout, 'starting test_exp'
for shape in ((), (3,), (2, 3),
(1, 10000000), (10, 1000000),
(100, 100000), (1000, 10000), (10000, 1000)):
a0 = theano._asarray(numpy.random.rand(*shape), dtype='float32')
a1 = a0.copy()
b0 = cuda_ndarray.CudaNdarray(a0)
b1 = cuda_ndarray.CudaNdarray(a1)
t0 = time.time()
cuda_ndarray.CudaNdarray(a1)
bsum = b0.exp()
t1 = time.time()
gpu_dt = t1 - t0
t0 = time.time()
asum = numpy.exp(a1)
t1 = time.time()
cpu_dt = t1 - t0
# print shape, 'adding ', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
#c = numpy.asarray(b0+b1)
# c = numpy.asarray(b0+b1)
if asum.shape:
assert numpy.allclose(asum, numpy.asarray(bsum))
def test_copy():
#print >>sys.stdout, 'starting test_copy'
# print >>sys.stdout, 'starting test_copy'
shape = (500, 499)
a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
#print >>sys.stdout, '.. creating device object'
# print >>sys.stdout, '.. creating device object'
b = cuda_ndarray.CudaNdarray(a)
#print >>sys.stdout, '.. copy'
# print >>sys.stdout, '.. copy'
c = copy.copy(b)
#print >>sys.stdout, '.. deepcopy'
# print >>sys.stdout, '.. deepcopy'
d = copy.deepcopy(b)
#print >>sys.stdout, '.. comparisons'
# print >>sys.stdout, '.. comparisons'
assert numpy.allclose(a, numpy.asarray(b))
assert numpy.allclose(a, numpy.asarray(c))
assert numpy.allclose(a, numpy.asarray(d))
b += b
assert numpy.allclose(a+a, numpy.asarray(b))
assert numpy.allclose(a+a, numpy.asarray(c))
assert numpy.allclose(a + a, numpy.asarray(b))
assert numpy.allclose(a + a, numpy.asarray(c))
assert numpy.allclose(a, numpy.asarray(d))
......@@ -237,8 +211,8 @@ def test_nvcc_bug():
assert numpy.allclose(a, numpy.asarray(c))
assert numpy.allclose(a, numpy.asarray(d))
b += b
assert numpy.allclose(a+a, numpy.asarray(b))
assert numpy.allclose(a+a, numpy.asarray(c))
assert numpy.allclose(a + a, numpy.asarray(b))
assert numpy.allclose(a + a, numpy.asarray(c))
assert numpy.allclose(a, numpy.asarray(d))
......@@ -318,7 +292,7 @@ class test_DimShuffle(unittest.TestCase):
def test_dot():
#print >>sys.stdout, 'starting test_dot'
# print >>sys.stdout, 'starting test_dot'
utt.seed_rng()
rng = numpy.random.RandomState(utt.fetch_seed())
......@@ -336,7 +310,7 @@ def test_dot():
numpy_version = numpy.dot(a0, a1.T)
transposed = cuda_ndarray.dimshuffle(b1, (1, 0))
cuda_version = cuda_ndarray.dot(b0, transposed)
cuda_version = cuda_ndarray.dot(b0, transposed)
assert _allclose(numpy_version, cuda_version)
......@@ -347,14 +321,16 @@ def test_dot():
b0 = cuda_ndarray.CudaNdarray(a0)
assert _allclose(numpy.dot(a0.T, a1),
cuda_ndarray.dot(cuda_ndarray.dimshuffle(b0, (1, 0)), b1))
cuda_ndarray.dot(
cuda_ndarray.dimshuffle(b0, (1, 0)), b1))
a1 = theano._asarray(rng.randn(6, 7), dtype='float32')
b1 = cuda_ndarray.CudaNdarray(a1)
assert _allclose(numpy.dot(a0.T, a1.T),
cuda_ndarray.dot(cuda_ndarray.dimshuffle(b0, (1, 0)),
cuda_ndarray.dimshuffle(b1, (1, 0))))
assert _allclose(
numpy.dot(a0.T, a1.T),
cuda_ndarray.dot(cuda_ndarray.dimshuffle(b0, (1, 0)),
cuda_ndarray.dimshuffle(b1, (1, 0))))
def test_sum():
......@@ -367,8 +343,8 @@ def test_sum():
assert numpy.allclose(a0.sum(),
numpy.asarray(b0.reduce_sum([1, 1])))
a0sum = a0.sum(axis=0)
b0sum = b0.reduce_sum([1, 0])
a0.sum(axis=0)
b0.reduce_sum([1, 0])
# print 'asum\n',a0sum
# print 'bsum\n',numpy.asarray(b0sum)
......@@ -399,31 +375,30 @@ def test_sum():
def test_reshape():
shapelist = [
((1, 2, 3), (1, 2, 3)),
((1,), (1,)),
((1, 2, 3), (3, 2, 1)),
((1, 2, 3), (6,)),
((1, 2, 3, 2), (6, 2)),
((2, 3, 2), (6, 2)),
((2, 3, 2), (12,))
]
shapelist = [((1, 2, 3), (1, 2, 3)),
((1,), (1,)),
((1, 2, 3), (3, 2, 1)),
((1, 2, 3), (6,)),
((1, 2, 3, 2), (6, 2)),
((2, 3, 2), (6, 2)),
((2, 3, 2), (12,))
]
bad_shapelist = [
((1, 2, 3), (1, 2, 4)),
((1,), (2,)),
((1, 2, 3), (2, 2, 1)),
((1, 2, 3), (5,)),
((1, 2, 3, 2), (6, 3)),
((2, 3, 2), (5, 2)),
((2, 3, 2), (11,))
]
((1, 2, 3), (1, 2, 4)),
((1,), (2,)),
((1, 2, 3), (2, 2, 1)),
((1, 2, 3), (5,)),
((1, 2, 3, 2), (6, 3)),
((2, 3, 2), (5, 2)),
((2, 3, 2), (11,))
]
utt.seed_rng()
rng = numpy.random.RandomState(utt.fetch_seed())
def subtest(shape_1, shape_2, rng):
#print >> sys.stdout, "INFO: shapes", shape_1, shape_2
# print >> sys.stdout, "INFO: shapes", shape_1, shape_2
a = theano._asarray(rng.randn(*shape_1), dtype='float32')
b = cuda_ndarray.CudaNdarray(a)
......@@ -459,8 +434,8 @@ def test_reshape():
b = cuda_ndarray.CudaNdarray(a)
try:
bb = b.reshape(shape_2)
except Exception as ValueError:
b.reshape(shape_2)
except Exception:
return
assert False
......@@ -477,13 +452,13 @@ def test_reshape():
def test_getshape():
shapelist = [
((1, 2, 3), (1, 2, 3)),
((1,), (1,)),
((1, 2, 3), (3, 2, 1)),
((1, 2, 3), (6,)),
((1, 2, 3, 2), (6, 2)),
((2, 3, 2), (6, 2))
]
((1, 2, 3), (1, 2, 3)),
((1,), (1,)),
((1, 2, 3), (3, 2, 1)),
((1, 2, 3), (6,)),
((1, 2, 3, 2), (6, 2)),
((2, 3, 2), (6, 2))
]
def subtest(shape):
a = theano._asarray(numpy.random.rand(*shape_1), dtype='float32')
......@@ -509,7 +484,7 @@ def test_stride_manipulation():
b_strides = b._strides
for i in xrange(len(b.shape)):
offset += (b.shape[i]-1) * b_strides[i]
offset += (b.shape[i] - 1) * b_strides[i]
v._set_stride(i, -b_strides[i])
v._dev_data += offset * sizeof_float
......@@ -699,8 +674,8 @@ def test_setitem_matrixvector1():
assert numpy.allclose(a, numpy.asarray(_a))
# test direct transfert from numpy
_a[:, 1] = b*100
a[:, 1] = b*100
_a[:, 1] = b * 100
a[:, 1] = b * 100
assert numpy.allclose(a, numpy.asarray(_a))
row = theano._asarray([777, 888, 999], dtype='float32')
......@@ -725,8 +700,8 @@ def test_setitem_matrix_tensor3():
assert numpy.allclose(a, numpy.asarray(_a))
# test direct transfert from numpy
_a[:, 1, 1] = b*100
a[:, 1, 1] = b*100
_a[:, 1, 1] = b * 100
a[:, 1, 1] = b * 100
assert numpy.allclose(a, numpy.asarray(_a))
row = theano._asarray([777, 888, 999], dtype='float32')
......@@ -752,7 +727,7 @@ def test_setitem_matrix_bad_shape():
# attempt to assign the ndarray b with setitem
_a[:, 1, 1] = _b
assert False
except ValueError as e:
except ValueError:
# print e
assert True
......@@ -761,7 +736,7 @@ def test_setitem_matrix_bad_shape():
# attempt to assign the ndarray b with setitem
_a[1, 1, :] = b
assert False
except ValueError as e:
except ValueError:
# print e
assert True
......@@ -779,7 +754,7 @@ def test_setitem_matrix_bad_ndim():
# attempt to assign the ndarray b with setitem
_a[:, :, 1] = _b
assert False
except ValueError as e:
except ValueError:
# print e
assert True
......@@ -788,7 +763,7 @@ def test_setitem_matrix_bad_ndim():
# attempt to assign the ndarray b with setitem
_a[1, :, :] = b
assert False
except ValueError as e:
except ValueError:
# print e
assert True
......@@ -806,7 +781,7 @@ def test_setitem_matrix_bad_type():
# attempt to assign the ndarray b with setitem
_a[1, :, :] = b
assert False
except TypeError as e:
except TypeError:
# print e
assert True
......@@ -832,8 +807,8 @@ def test_setitem_assign_to_slice():
# test direct transfert from numpy
_d = _a[1, :, :]
_d[1, :] = b*10
a[1, :, :][1, :] = b*10
_d[1, :] = b * 10
a[1, :, :][1, :] = b * 10
assert numpy.allclose(a, numpy.asarray(_a))
......@@ -923,7 +898,7 @@ def test_setitem_rightvalue_ndarray_fails():
b = theano._asarray([7, 8, 9, 10], dtype='float32')
_b = cuda_ndarray.CudaNdarray(b)
b5 = theano._asarray([7, 8, 9, 10, 11], dtype='float32')
_b5 = cuda_ndarray.CudaNdarray(b)
cuda_ndarray.CudaNdarray(b)
# attempt to assign the ndarray b with setitem
_a[:, :, 1] = _b
......@@ -941,9 +916,9 @@ def test_setitem_rightvalue_ndarray_fails():
# without same number of dim
try:
_a[0, :, :] = mat
#a[0, :, :] = mat
#assert numpy.allclose(numpy.asarray(_a), a)
except ValueError as e:
# a[0, :, :] = mat
# assert numpy.allclose(numpy.asarray(_a), a)
except ValueError:
pass
# test direct transfert from numpy with broadcast
......@@ -964,7 +939,7 @@ def test_zeros_basic():
_n = numpy.zeros(shp, dtype="float32")
assert numpy.allclose(numpy.asarray(_a), _n)
assert _a.shape == _n.shape
assert all(_a._strides == numpy.asarray(_n.strides)/4)
assert all(_a._strides == numpy.asarray(_n.strides) / 4)
# TODO:The following don't have the same stride!
# This should be fixed with the new GpuNdArray.
......@@ -1039,10 +1014,7 @@ def test_is_c_contiguous():
assert not a[::2].is_c_contiguous()
if __name__ == '__main__':
test_zeros_basic_3d_tensor()
test_zeros_basic_vector()
test_setitem_matrixvector1()
test_setitem_matrix_tensor3()
test_setitem_broadcast_must_fail()
test_setitem_assign_to_slice()
test_setitem_rightvalue_ndarray_fails()
theano/sandbox/cuda/tests/test_driver.py
浏览文件 @ b69ad54d
......@@ -6,7 +6,7 @@ import theano
try:
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
if cuda_ndarray.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
except ImportError:
# To have the GPU back-end work without nose, we need this file to
......@@ -33,8 +33,9 @@ def test_nvidia_driver1():
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
if sum(isinstance(node.op, B.GpuCAReduce) for node in topo) != 1:
msg = '\n\t'.join(['Expected exactly one occurrence of GpuCAReduce ' +
'but got:']+[str(app) for app in topo])
msg = '\n\t'.join(
['Expected exactly one occurrence of GpuCAReduce ' +
'but got:'] + [str(app) for app in topo])
raise AssertionError(msg)
if not numpy.allclose(f(), a.sum()):
raise Exception("The nvidia driver version installed with this OS "
......
theano/sandbox/cuda/tests/test_extra_ops.py
浏览文件 @ b69ad54d
......@@ -5,24 +5,22 @@ import itertools
from nose.plugins.skip import SkipTest
import numpy as np
from six.moves import xrange
from theano import tensor as T
import theano
from theano.tensor.extra_ops import cumsum, CumsumOp
from theano.tests import unittest_tools as utt
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available is False:
if cuda_ndarray.cuda_available:
import theano.tensor.tests.test_extra_ops
from theano.sandbox.cuda.extra_ops import GpuCumsum
else:
raise SkipTest('Optional package cuda disabled')
import theano.tensor.tests.test_extra_ops
from theano.sandbox.cuda.extra_ops import GpuCumsum
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
else:
mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
from theano import tensor as T
import theano
from theano.tensor.extra_ops import cumsum, CumsumOp
from theano.tests import unittest_tools as utt
class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
mode = mode_with_gpu
......@@ -129,11 +127,11 @@ class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
utt.assert_allclose(np.cumsum(a[:i]), f(a[:i]))
# Use multiple GPU threadblocks
a = np.random.random((block_max_size+2,)).astype("float32")
a = np.random.random((block_max_size + 2,)).astype("float32")
utt.assert_allclose(np.cumsum(a), f(a))
# Use recursive cumsum
a = np.ones((block_max_size*(block_max_size+1)+2,),
a = np.ones((block_max_size * (block_max_size + 1) + 2,),
dtype="float32")
utt.assert_allclose(np.cumsum(a), f(a))
......@@ -159,21 +157,22 @@ class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
# Use multiple GPU threadblocks
a_shape = [5, 5]
a_shape[shape_axis] = block_max_size+2
a_shape[shape_axis] = block_max_size + 2
a = np.random.random(a_shape).astype("float32")
utt.assert_allclose(np.cumsum(a, axis=axis), f(a))
# Use multiple GPU gridblocks
a_shape = [4, 4]
a_shape[1-shape_axis] = self.max_grid_size1+1
a_shape[1 - shape_axis] = self.max_grid_size1 + 1
a = np.random.random(a_shape).astype("float32")
utt.assert_allclose(np.cumsum(a, axis=axis), f(a), rtol=5e-5)
# Use recursive cumsum
a_shape = [3, 3]
a_shape[shape_axis] = block_max_size*(block_max_size+1)+2
a_shape[shape_axis] = block_max_size * (
block_max_size + 1) + 2
a = np.random.random(a_shape).astype("float32")
a = np.sign(a-0.5).astype("float32") # Avoid floating point error
a = np.sign(a - 0.5).astype("float32") # Avoid floating point error
utt.assert_allclose(np.cumsum(a, axis=axis), f(a))
def test_GpuCumsum3D(self):
......@@ -198,32 +197,34 @@ class TestGpuCumsum(theano.tensor.tests.test_extra_ops.TestCumsumOp):
# Use multiple GPU threadblocks (along accumulation axis)
a_shape = [2, 2, 2]
a_shape[shape_axis] = block_max_size+2
a_shape[shape_axis] = block_max_size + 2
a = np.random.random(a_shape).astype("float32")
utt.assert_allclose(np.cumsum(a, axis=axis), f(a))
# Use multiple GPU gridblocks (not along accumulation axis)
a_shape = [5, 5, 5]
a_shape[(shape_axis+1) % 3] = self.max_grid_size1+1
a_shape[(shape_axis + 1) % 3] = self.max_grid_size1 + 1
a = np.random.random(a_shape).astype("float32")
if axis is None:
# Avoid floating point error
a = np.sign(a-0.5).astype("float32")
a = np.sign(a - 0.5).astype("float32")
utt.assert_allclose(np.cumsum(a, axis=axis), f(a))
a_shape = [5, 5, 5]
a_shape[(shape_axis+2) % 3] = self.max_grid_size1+1
a_shape[(shape_axis + 2) % 3] = self.max_grid_size1 + 1
a = np.random.random(a_shape).astype("float32")
if axis is None:
# Avoid floating point error
a = np.sign(a-0.5).astype("float32")
a = np.sign(a - 0.5).astype("float32")
utt.assert_allclose(np.cumsum(a, axis=axis), f(a))
# Use recursive cumsum (along accumulation axis)
a_shape = [3, 3, 3]
a_shape[shape_axis] = block_max_size*(block_max_size+1)+2
a_shape[shape_axis] = block_max_size * (
block_max_size + 1) + 2
a = np.random.random(a_shape).astype("float32")
a = np.sign(a-0.5).astype("float32") # Avoid floating point error
a = np.sign(a - 0.5).astype(
"float32") # Avoid floating point error
utt.assert_allclose(np.cumsum(a, axis=axis), f(a))
def test_GpuCumsum4D(self):
......
theano/sandbox/cuda/tests/test_gemmcorr3d.py
浏览文件 @ b69ad54d
from __future__ import absolute_import, print_function, division
import unittest
import numpy
import copy
import theano
from theano.tests import unittest_tools as utt
# Skip tests if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if not cuda_ndarray.cuda_available:
raise SkipTest('Optional package cuda not available')
from theano.sandbox.cuda import float32_shared_constructor as shared
from theano.sandbox.cuda.blas import (
GpuCorr3dMM, GpuCorr3dMM_gradWeights, GpuCorr3dMM_gradInputs)
from theano.sandbox.cuda.basic_ops import gpu_contiguous
import theano.sandbox.cuda as cuda_ndarray
if not cuda_ndarray.cuda_available:
raise SkipTest('Optional package cuda not available')
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
......@@ -122,7 +121,9 @@ class TestCorr3DMM(unittest.TestCase):
inputs = shared(inputs_val)
filters = shared(filters_val)
bias = shared(numpy.zeros(filters_shape[4]).astype('float32'))
conv = theano.tensor.nnet.convTransp3D(W=filters, b=bias, d=subsample,
conv = theano.tensor.nnet.convTransp3D(W=filters,
b=bias,
d=subsample,
H=inputs)
f_ref = theano.function([], conv)
res_ref = f_ref()
......
theano/sandbox/cuda/tests/test_gradient.py
浏览文件 @ b69ad54d
......@@ -8,7 +8,7 @@ from theano.sandbox import cuda
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
if cuda_ndarray.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
......
theano/sandbox/cuda/tests/test_memory.py
浏览文件 @ b69ad54d
......@@ -11,7 +11,7 @@ from theano import ifelse
# Skip test if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
if cuda.cuda_available == False:
if cuda.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
......@@ -39,7 +39,7 @@ def freemem(extra_alloc=0):
theano_alloc = cuda.cuda_ndarray.cuda_ndarray.theano_allocated()
return ("(n malloc/theano mem allocated in KB)",
n_mallocs + extra_alloc,
int(theano_alloc / 1024) + extra_size)
int(theano_alloc / 1024))
return ("n malloc on the gpu", n_mallocs + extra_alloc)
# I don't use the following by default as if there is other stuff running
......@@ -83,9 +83,12 @@ def test_memory():
variables = cuda.shared_constructor(np.ones((shapes[1],),
dtype='float32'))
derp = tensor.sum(tensor.dot(some_matrix[:shapes[0]], variables))
print("Shared took ", np.prod(variables.get_value(
borrow=True,
return_internal_type=True).shape) * 4 / 1024, "kB")
print("Shared took ",
np.prod(variables.get_value(
borrow=True,
return_internal_type=True).shape) *
4 / 1024,
"kB")
mem2 = freemem()
print("Before compilation", mem2)
......@@ -112,7 +115,7 @@ def test_memory():
del obj
# print "After deleting function 1", freemem()
#assert mem2 == freemem(), (mem2, freemem())
# assert mem2 == freemem(), (mem2, freemem())
del grad
print("After deleting function 2", freemem())
......@@ -155,16 +158,19 @@ def test_memory_lazy():
derp = ifelse.IfElse(1)(branch_select,
derp, some_matrix[:shapes[0]].sum())
derp += 1
print("Shared took ", np.prod(variables.get_value(
borrow=True,
return_internal_type=True).shape) * 4 / 1024, "kB")
print("Shared took ",
np.prod(variables.get_value(
borrow=True,
return_internal_type=True).shape) *
4 / 1024,
"kB")
mem2 = freemem()
print("Before compilation", mem2)
mem2_1 = freemem(extra_alloc=more_alloc1)
obj = theano.function([some_vector, branch_select], derp,
mode=mode_with_gpu)
#theano.printing.debugprint(obj, print_type=True)
# theano.printing.debugprint(obj, print_type=True)
mem3 = freemem()
print("After function compilation 1", mem3)
assert mem2_1 == mem3, (mem2_1, mem3)
......
theano/sandbox/cuda/tests/test_mlp.py
浏览文件 @ b69ad54d
......@@ -24,7 +24,7 @@ if theano.config.mode not in ['FAST_RUN', 'Mode', 'ProfileMode']:
'otherwise it is too slow!')
# Skip test if cuda_ndarray is not available.
if tcn.cuda_available == False:
if tcn.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
......@@ -68,7 +68,7 @@ def print_mode(mode):
def print_diff_mode(a, b):
if (a is not None and
isinstance(a, (theano.compile.ProfileMode,)) and
isinstance(b, (theano.compile.ProfileMode,))):
isinstance(b, (theano.compile.ProfileMode,))):
a.print_diff_summary(b)
......@@ -138,8 +138,8 @@ def test_run_nnet():
# print "cpu:", rval_cpu
# print "gpu:", rval_gpu
abs_diff, rel_diff = \
theano.gradient.numeric_grad.abs_rel_err(rval_gpu,
rval_cpu)
theano.gradient.numeric_grad.abs_rel_err(rval_gpu,
rval_cpu)
max_abs_diff = abs_diff.max()
# print "max abs diff=%e max rel diff=%e n_in=%d n_hid=%d" % (
# max_abs_diff, rel_diff.max(), n_in, n_hid)
......@@ -147,19 +147,20 @@ def test_run_nnet():
rtol = 1e-4
if n_in * n_hid >= 2048 * 4096:
rtol = 7e-4
assert numpy.allclose(rval_cpu, rval_gpu, rtol=rtol, atol=1e-6), \
("max_abs_diff, max_rel_diff, n_in, n_hid", max_abs_diff,
rel_diff.max(), n_in, n_hid)
assert numpy.allclose(
rval_cpu, rval_gpu, rtol=rtol, atol=1e-6), \
("max_abs_diff, max_rel_diff, n_in, n_hid", max_abs_diff,
rel_diff.max(), n_in, n_hid)
def test_run_nnet_med():
utt.seed_rng()
rval_cpu = run_nnet(False, 10, 128, 50, 4, n_train=10000)
run_nnet(False, 10, 128, 50, 4, n_train=10000)
def test_run_nnet_small():
utt.seed_rng()
rval_cpu = run_nnet(False, 10, 10, 4, 4, n_train=100000)
run_nnet(False, 10, 10, 4, 4, n_train=100000)
def run_conv_nnet1(use_gpu):
......@@ -203,8 +204,11 @@ def run_conv_nnet1(use_gpu):
mode = get_mode(use_gpu)
# print 'building pfunc ...'
train = pfunc([x, y, lr], [loss], mode=mode, updates=[(p, p - g) for p,
g in zip(params, gparams)])
train = pfunc(
[x, y, lr],
[loss],
mode=mode,
updates=[(p, p - g) for p, g in zip(params, gparams)])
# for i, n in enumerate(train.maker.fgraph.toposort()):
# print i, n
......@@ -279,7 +283,9 @@ def run_conv_nnet2(use_gpu): # pretend we are training LeNet for MNIST
conv_op = conv.ConvOp(shape_img[2:], shape_kern[2:], n_kern, n_batch, 1, 1)
conv_op1 = conv.ConvOp((n_kern, logical_hid_shape[0] // 2,
logical_hid_shape[1] // 2), shape_kern1[2:], n_kern1, n_batch, 1, 1)
logical_hid_shape[1] // 2),
shape_kern1[2:],
n_kern1, n_batch, 1, 1)
hid = tensor.tanh(conv_op(x, w0) + b0.dimshuffle((0, 'x', 'x')))
hid1 = tensor.tanh(conv_op1(hid[:, :, ::2, ::2], w1) + b1.dimshuffle((
......@@ -295,8 +301,11 @@ def run_conv_nnet2(use_gpu): # pretend we are training LeNet for MNIST
mode = get_mode(use_gpu)
# print 'building pfunc ...'
train = pfunc([x, y, lr], [loss], mode=mode, updates=[(p, p - g) for p,
g in zip(params, gparams)])
train = pfunc(
[x, y, lr],
[loss],
mode=mode,
updates=[(p, p - g) for p, g in zip(params, gparams)])
# for i, n in enumerate(train.maker.fgraph.toposort()):
# print i, n
......@@ -376,13 +385,14 @@ def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
if downsample_ops:
hid = tensor.tanh(ds_op(conv_op(x, w0) + b0.dimshuffle((0, 'x', 'x'))))
else:
hid = tensor.tanh((conv_op(x, w0) + b0.dimshuffle((0, 'x', 'x')
))[:, :, ::2, ::2])
hid = tensor.tanh(
(conv_op(x, w0) + b0.dimshuffle(
(0, 'x', 'x')))[:, :, ::2, ::2])
hid1 = tensor.tanh(conv_op1(hid, w1) + b1.dimshuffle((0, 'x', 'x')))
hid_flat = hid1.reshape((n_batch, n_hid))
out = tensor.nnet.softmax(tensor.dot(hid_flat, v) + c)
loss = tensor.sum(tensor.nnet.crossentropy_categorical_1hot(out,
tensor.argmax(y, axis=1)) * lr)
loss = tensor.sum(tensor.nnet.crossentropy_categorical_1hot(
out, tensor.argmax(y, axis=1)) * lr)
# print 'loss type', loss.type
params = [w0, b0, w1, b1, v, c]
......@@ -391,8 +401,11 @@ def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
mode = get_mode(use_gpu, check_isfinite)
# print 'building pfunc ...'
train = pfunc([x, y, lr], [loss], mode=mode, updates=[(p, p - g) for p,
g in zip(params, gparams)])
train = pfunc(
[x, y, lr],
[loss],
mode=mode,
updates=[(p, p - g) for p, g in zip(params, gparams)])
if verbose:
theano.printing.debugprint(train)
......@@ -422,13 +435,13 @@ def run_conv_nnet2_classif(use_gpu, seed, isize, ksize, bsize,
utt.seed_rng(seed) # Seeds numpy.random with seed
train, params, x_shape, y_shape, mode = build_conv_nnet2_classif(
use_gpu=use_gpu,
isize=isize,
ksize=ksize,
n_batch=bsize,
verbose=verbose,
version=version,
check_isfinite=check_isfinite)
use_gpu=use_gpu,
isize=isize,
ksize=ksize,
n_batch=bsize,
verbose=verbose,
version=version,
check_isfinite=check_isfinite)
if use_gpu:
device = 'GPU'
......@@ -440,10 +453,8 @@ def run_conv_nnet2_classif(use_gpu, seed, isize, ksize, bsize,
lr = theano._asarray(0.01, dtype='float32')
rvals = my_zeros(n_train)
t0 = time.time()
for i in xrange(n_train):
rvals[i] = train(xval, yval, lr)[0]
t1 = time.time()
print_mode(mode)
if pickle and isinstance(mode, theano.compile.ProfileMode):
......@@ -495,35 +506,36 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
compare = True
if not compare:
return run_conv_nnet2_classif(use_gpu=use_gpu,
seed=seed, isize=isize, ksize=ksize, bsize=bsize,
n_train=n_train,
check_isfinite=check_isfinite,
pickle=pickle,
verbose=verbose,
version=version)
return run_conv_nnet2_classif(
use_gpu=use_gpu,
seed=seed, isize=isize, ksize=ksize, bsize=bsize,
n_train=n_train,
check_isfinite=check_isfinite,
pickle=pickle,
verbose=verbose,
version=version)
utt.seed_rng(seed) # Seeds numpy.random with seed
train_cpu, params_cpu, x_shape, y_shape, mode_cpu = \
build_conv_nnet2_classif(
use_gpu=False,
isize=isize,
ksize=ksize,
n_batch=bsize,
verbose=verbose,
version=version,
check_isfinite=check_isfinite)
build_conv_nnet2_classif(
use_gpu=False,
isize=isize,
ksize=ksize,
n_batch=bsize,
verbose=verbose,
version=version,
check_isfinite=check_isfinite)
utt.seed_rng(seed) # Seeds numpy.random with seed
train_gpu, params_gpu, x_shape_gpu, y_shape_gpu, mode_gpu = \
build_conv_nnet2_classif(
use_gpu=True,
isize=isize,
ksize=ksize,
n_batch=bsize,
verbose=verbose,
version=version,
check_isfinite=check_isfinite)
build_conv_nnet2_classif(
use_gpu=True,
isize=isize,
ksize=ksize,
n_batch=bsize,
verbose=verbose,
version=version,
check_isfinite=check_isfinite)
assert x_shape == x_shape_gpu
assert y_shape == y_shape_gpu
......@@ -570,18 +582,6 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
finally:
theano.tensor.basic.float32_atol = orig_float32_atol
if pickle:
if isinstance(cpu_mode, theano.compile.ProfileMode):
import pickle
print("BEGIN CPU profile mode dump")
print(pickle.dumps(cpu_mode))
print("END CPU profile mode dump")
if isinstance(gpu_mode, theano.compile.ProfileMode):
import pickle
print("BEGIN GPU profile mode dump")
print(pickle.dumps(gpu_mode))
print("END GPU profile mode dump")
# print "CPU time: %.3f, GPU time: %.3f, speed up %f" % (
# (time_cpu, time_gpu, time_cpu/time_gpu))
# print "Estimated time for one pass through MNIST with CPU: %f" % (
......
theano/sandbox/cuda/tests/test_neighbours.py
浏览文件 @ b69ad54d
# Skip test if cuda_ndarray is not available.
from __future__ import absolute_import, print_function, division
from nose.plugins.skip import SkipTest
import unittest
import theano.tensor.nnet.tests.test_neighbours
from theano.sandbox.cuda.neighbours import GpuImages2Neibs
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
if cuda_ndarray.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
import theano.tensor.nnet.tests.test_neighbours
from theano.sandbox.cuda.neighbours import GpuImages2Neibs
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
......
theano/sandbox/cuda/tests/test_opt.py
浏览文件 @ b69ad54d
from __future__ import absolute_import, print_function, division
import operator
import sys
import unittest
import numpy
# Skip test if cuda_ndarray is not available.
......@@ -9,39 +8,28 @@ from nose.plugins.skip import SkipTest
from nose.tools import assert_raises
import theano
import theano.sandbox.cuda.cula as cula
from theano.sandbox.cuda import basic_ops
from theano.sandbox.cuda.type import CudaNdarrayType
from theano.scalar.basic_scipy import erfinv
from six.moves import reduce
from theano.compile.pfunc import pfunc
from theano import config, tensor
import theano.tensor.tests.test_nlinalg
import theano.tensor.tests.test_opt as test_opt
from theano.tensor.nnet.blocksparse import sparse_block_dot
from theano.sandbox.cuda.blocksparse import GpuSparseBlockGemv
from theano.sandbox.cuda.blocksparse import GpuSparseBlockOuter
from theano.tests.breakpoint import PdbBreakpoint
from theano.tests import unittest_tools as utt
import theano.tests.test_ifelse
import theano.sandbox.cuda as cuda
if not cuda.cuda_available:
raise SkipTest('Optional package cuda disabled')
import theano.sandbox.cuda.cula as cula
from theano.sandbox.cuda import basic_ops
from theano.sandbox.cuda.type import CudaNdarrayType
from theano.scalar.basic_scipy import erfinv
from theano.tensor.nnet.blocksparse import sparse_block_dot
from theano.sandbox.cuda.blocksparse import GpuSparseBlockGemv, GpuSparseBlockOuter
imported_scipy_special = False
try:
import scipy.special
imported_scipy_special = True
# Importing scipy.special may raise ValueError.
# See http://projects.scipy.org/scipy/ticket/1739
except (ImportError, ValueError):
pass
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
mode_without_gpu = theano.compile.mode.get_mode('FAST_RUN').excluding('gpu')
......@@ -136,7 +124,7 @@ def test_local_assert_no_cpu_op():
config.on_opt_error = 'ignore'
assert_raises(AssertionError, theano.function,
[], out, mode=mode_local_assert)
[], out, mode=mode_local_assert)
finally:
config.assert_no_cpu_op = old
config.on_opt_error = old2
......@@ -152,7 +140,7 @@ def test_local_assert_no_cpu_op():
def test_int_pow():
a = CudaNdarrayType([False])()
f = theano.function([a], (a*4).sum(), mode=mode_with_gpu)
f = theano.function([a], (a * 4).sum(), mode=mode_with_gpu)
op_names = [n.op.__class__.__name__ for n in f.maker.fgraph.toposort()]
assert op_names == ['GpuCAReduce', 'GpuElemwise', 'HostFromGpu']
......@@ -175,23 +163,30 @@ def test_gpualloc():
x = theano.shared(numpy.ones(3, dtype='float32'), 'x')
m = (x).dimshuffle(['x', 0])
v = tensor.alloc(1., *m.shape)
f = theano.function([], v + x,
mode=mode_with_gpu.excluding("local_elemwise_alloc"))
f = theano.function([],
v + x,
mode=mode_with_gpu.excluding(
"local_elemwise_alloc"))
l = f.maker.fgraph.toposort()
assert numpy.any([isinstance(x.op, cuda.GpuAlloc) for x in l])
assert numpy.any([isinstance(x.op, cuda.GpuAlloc) for y in l])
def test_gpuallocempty():
f_gpu = theano.function([], tensor.AllocEmpty('float32')(2,3),
mode=mode_with_gpu)
f_gpu = theano.function(
[],
tensor.AllocEmpty('float32')(2, 3),
mode=mode_with_gpu)
l_gpu = f_gpu.maker.fgraph.toposort()
assert numpy.any([isinstance(x.op, basic_ops.GpuAllocEmpty) for x in l_gpu])
assert numpy.any(
[isinstance(x.op, basic_ops.GpuAllocEmpty) for x in l_gpu])
f_cpu = theano.function([], tensor.AllocEmpty('int32')(2,3))
f_cpu = theano.function([], tensor.AllocEmpty('int32')(2, 3))
l_cpu = f_cpu.maker.fgraph.toposort()
assert not numpy.any([isinstance(x.op, basic_ops.GpuAllocEmpty) for x in l_cpu])
assert not numpy.any(
[isinstance(x.op, basic_ops.GpuAllocEmpty) for x in l_cpu])
class Test_local_elemwise_alloc(test_opt.Test_local_elemwise_alloc):
dtype = 'float32'
......@@ -269,7 +264,8 @@ def test_gpuspecifyshape():
f = theano.function([], updates=[(x, m * numpy.float32(2))],
mode=mode_with_gpu)
l = f.maker.fgraph.toposort()
assert not numpy.any([isinstance(x.op, cuda.HostFromGpu) for x in l])
assert not numpy.any(
[isinstance(x.op, cuda.HostFromGpu) for y in l])
def test_softmax():
......@@ -430,7 +426,7 @@ def test_local_gpu_subtensor():
# Test multiple use of the input
# We want the subtensor to be on the GPU to prevent multiple transfer.
t = tensor.fmatrix()
f = theano.function([t], [t[3:4], t+1], mode=mode_with_gpu)
f = theano.function([t], [t[3:4], t + 1], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert not any([type(node.op) is tensor.Subtensor for node in topo])
assert any([isinstance(node.op, cuda.GpuSubtensor) for node in topo])
......@@ -438,7 +434,7 @@ def test_local_gpu_subtensor():
# Test multiple use of the input + input as output
# We want the subtensor to be on the GPU to prevent multiple transfer.
t = tensor.fmatrix()
f = theano.function([t], [t[3:4], t+1, t], mode=mode_with_gpu)
f = theano.function([t], [t[3:4], t + 1, t], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert not any([type(node.op) is tensor.Subtensor for node in topo])
assert any([isinstance(node.op, cuda.GpuSubtensor) for node in topo])
......@@ -446,7 +442,7 @@ def test_local_gpu_subtensor():
# Test shared forced on CPU end we do computation on the output of
# the subtensor.
t = tensor._shared(numpy.zeros(20, "float32"))
f = theano.function([], t[3:4]+1, mode=mode_with_gpu)
f = theano.function([], t[3:4] + 1, mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert any([type(node.op) is tensor.Subtensor for node in topo])
assert not any([isinstance(node.op, cuda.GpuSubtensor) for node in topo])
......@@ -507,10 +503,11 @@ def test_local_gpu_split():
def test_print_op():
""" Test that print ops don't block gpu optimization"""
b = tensor.fmatrix()
f = theano.function([b], theano.printing.Print()(b)*2, mode=mode_with_gpu)
f = theano.function(
[b], theano.printing.Print()(b) * 2, mode=mode_with_gpu)
# theano.printing.debugprint(f)
# print f.maker.fgraph.toposort()
#[GpuFromHost(<TensorType(float32, matrix)>), <theano.printing.Print object at 0x3581210>(GpuFromHost.0), GpuElemwise{mul}(CudaNdarray{[[ 2.]]}, <theano.printing.Print object at 0x3581210>.0), HostFromGpu(GpuElemwise{mul}.0)]
# [GpuFromHost(<TensorType(float32, matrix)>), <theano.printing.Print object at 0x3581210>(GpuFromHost.0), GpuElemwise{mul}(CudaNdarray{[[ 2.]]}, <theano.printing.Print object at 0x3581210>.0), HostFromGpu(GpuElemwise{mul}.0)]
topo = f.maker.fgraph.toposort()
assert topo[0].op == cuda.gpu_from_host
assert isinstance(topo[1].op, theano.printing.Print)
......@@ -563,8 +560,10 @@ def test_huge_elemwise_fusion():
bytes limits.
"""
shape = (2, 3, 4, 5, 6)
ttype = tensor.tensor(dtype='float32', broadcastable=(False,) * len(shape))
gpu_ptr_size = theano.sandbox.cuda.opt.get_device_type_sizes()['gpu_ptr_size']
ttype = tensor.tensor(dtype='float32',
broadcastable=(False,) * len(shape))
gpu_ptr_size = theano.sandbox.cuda.opt.get_device_type_sizes()[
'gpu_ptr_size']
if gpu_ptr_size == 8:
nb_in = 7
len_topo = 10
......@@ -582,14 +581,19 @@ def test_huge_elemwise_fusion():
assert isinstance(topo[-3].op.scalar_op, theano.scalar.basic.Sub)
assert isinstance(topo[-2].op.scalar_op, theano.scalar.basic.Composite)
# let debugmode catch errors
gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')
# gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')
def gen():
return(
theano._asarray(numpy.random.rand(*shape), dtype='float32'))
f(*[gen() for i in range(nb_in)])
# Test the case where we can't put the computation on the gpu! their is too
# many dimensions to the input to have 2 inputs to the op!
shape = (1, 2, 3, 4, 5, 6, 7, 2, 2, 3, 2, 1, 2, 2, 2,)
ttype = tensor.tensor(dtype='float32', broadcastable=(False,) * len(shape))
ttype = tensor.tensor(
dtype='float32', broadcastable=(False,) * len(shape))
vars = [tensor.tanh(ttype) for x in range(7)]
f = pfunc(vars, [vars[0] - vars[1] - vars[2] - vars[3] - vars[4] -
vars[5] - vars[6]], mode=mode_with_gpu)
......@@ -598,7 +602,9 @@ def test_huge_elemwise_fusion():
assert sum([isinstance(node.op, cuda.GpuElemwise) for node in topo]) == 0
assert sum([isinstance(node.op, tensor.Elemwise) for node in topo]) == 1
# let debugmode catch errors
gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')
def gen():
return(theano._asarray(numpy.random.rand(*shape), dtype='float32'))
f(gen(), gen(), gen(), gen(), gen(), gen(), gen())
def gen(shape):
......@@ -611,9 +617,9 @@ def test_huge_elemwise_fusion():
(2, 2, 2, 2),
(2, 2, 2, 2, 2), # 5d
(2, 2, 2, 2, 2, 2),
# (2, 2, 2, 2, 2, 2, 2),
# (2, 2, 2, 2, 2, 2, 2, 2),
# (2, 2, 2, 1, 1, 1, 1, 2, 2), # 9d
# (2, 2, 2, 2, 2, 2, 2),
# (2, 2, 2, 2, 2, 2, 2, 2),
# (2, 2, 2, 1, 1, 1, 1, 2, 2), # 9d
]:
vals = [cuda.shared_constructor(gen(shape)) for x in range(max_var)]
for use_tan in [True, False]:
......@@ -676,7 +682,9 @@ def test_local_gpu_elemwise_0():
a = tensor.fmatrix()
from theano.scalar.basic import identity
out_s = theano.scalar.Composite([a_s, b_s, c_s],
[identity(a_s), identity(c_s), identity(b_s)])
[identity(a_s),
identity(c_s),
identity(b_s)])
outs_op = tensor.Elemwise(out_s)
f = theano.function([a, b, c], outs_op(a, b, c), mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
......@@ -725,9 +733,6 @@ def test_elemwise_fusion():
theano._asarray(numpy.random.rand(*shape), dtype='float32'))
import theano.tests.test_ifelse
class TestIfElse(theano.tests.test_ifelse.test_ifelse):
dtype = "float32"
mode = mode_with_gpu
......@@ -765,15 +770,17 @@ def test_incsubtensor_mixed():
def test_erfinvgpu():
""" Test that local_gpu_elemwise_0 replaces Erfinv with ErfinvGPU """
x = tensor.fmatrix()
f = theano.function([x], tensor.Elemwise(erfinv)(x), mode=mode_with_gpu)
f2 = theano.function([x], tensor.Elemwise(erfinv)(x),
mode=mode_without_gpu)
assert isinstance(f.maker.fgraph.toposort()[1].op, cuda.GpuElemwise)
f = theano.function([x],
tensor.Elemwise(erfinv)(x),
mode=mode_with_gpu)
theano.function([x],
tensor.Elemwise(erfinv)(x),
mode=mode_without_gpu)
assert isinstance(f.maker.fgraph.toposort()[1].op,
cuda.GpuElemwise)
assert isinstance(f.maker.fgraph.toposort()[1].op.scalar_op,
cuda.elemwise.ErfinvGPU)
xv = numpy.random.rand(7, 8).astype('float32')
if imported_scipy_special:
assert numpy.allclose(f(xv), f2(xv))
numpy.random.rand(7, 8).astype('float32')
def test_local_gpu_solve():
......@@ -887,10 +894,10 @@ def test_local_abstractconv_gemm():
image = tensor.ftensor4()
W = tensor.ftensor4()
conv = tensor.nnet.conv2d(image,
W,
input_shape=(1, 32, 32, 32),
filter_shape=(32, 32, 3, 3),
border_mode='half')
W,
input_shape=(1, 32, 32, 32),
filter_shape=(32, 32, 3, 3),
border_mode='half')
f = theano.function([image, W], [conv], mode=mode_with_gpu)
f(numpy.random.rand(1, 32, 32, 32).astype('float32'),
numpy.random.rand(32, 32, 3, 3).astype('float32'))
......
theano/sandbox/cuda/tests/test_rng_curand.py
浏览文件 @ b69ad54d
......@@ -8,7 +8,7 @@ from theano.sandbox.rng_mrg import MRG_RandomStreams
# Skip tests if cuda_ndarray is not available.
from nose.plugins.skip import SkipTest
import theano.sandbox.cuda as cuda_ndarray
if cuda_ndarray.cuda_available == False:
if cuda_ndarray.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
# The PyCObject that represents the cuda random stream object
......@@ -168,13 +168,13 @@ def compare_speed():
dest = theano.shared(numpy.zeros(N, dtype=theano.config.floatX))
mrg_u = theano.function([], [], updates={dest: mrg.uniform((N,))},
profile='mrg uniform')
profile='mrg uniform')
crn_u = theano.function([], [], updates={dest: crn.uniform((N,))},
profile='crn uniform')
profile='crn uniform')
mrg_n = theano.function([], [], updates={dest: mrg.normal((N,))},
profile='mrg normal')
profile='mrg normal')
crn_n = theano.function([], [], updates={dest: crn.normal((N,))},
profile='crn normal')
profile='crn normal')
for f in mrg_u, crn_u, mrg_n, crn_n:
# don't time the first call, it has some startup cost
......
theano/sandbox/cuda/tests/test_tensor_op.py
浏览文件 @ b69ad54d
......@@ -2,7 +2,6 @@
This file test tensor op that should also operate on CudaNdaray.
"""
from __future__ import absolute_import, print_function, division
import copy
from nose.plugins.skip import SkipTest
import numpy
......@@ -14,7 +13,7 @@ import theano.tensor as T
# Skip test if cuda_ndarray is not available.
import theano.sandbox.cuda as cuda
from theano.tensor.nnet.tests import test_conv3d2d
if cuda.cuda_available == False:
if cuda.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
......@@ -57,7 +56,7 @@ def test_softmax_optimizations():
one_of_n = tensor.lvector('one_of_n')
op = crossentropy_categorical_1hot
xe = op(x, one_of_n)
op(x, one_of_n)
fgraph = theano.gof.FunctionGraph(
[x, one_of_n],
......@@ -84,10 +83,10 @@ def test_may_share_memory_cuda():
# can't test the transpose as ta._strides = is not implemented
# manual transpose of a
#ta = a.reshape((4,3))
# ta = a.reshape((4,3))
# ta._strides = (ta._strides[1],ta._strides[0])#not implemented
#elem_size=elem_size = numpy.zeros(0,dtype=a.dtype).dtype.itemsize
#ta.gpudata += ta.size*elem_size
# elem_size=elem_size = numpy.zeros(0,dtype=a.dtype).dtype.itemsize
# ta.gpudata += ta.size*elem_size
for a_, b_, rep in [(a, a, True), (b, b, True), (a, b, False),
(a, na, False), (b, nb, False),
......@@ -95,8 +94,7 @@ def test_may_share_memory_cuda():
(a, va, True), (b, vb, True),
(va, b, False), (a, vb, False),
(a, ra, True), (b, rb, True),
(ra, b, False), (a, rb, False),
]:
(ra, b, False), (a, rb, False), ]:
assert may_share_memory(a_, b_) == rep
assert may_share_memory(b_, a_) == rep
......
theano/sandbox/cuda/tests/test_var.py
浏览文件 @ b69ad54d
......@@ -10,7 +10,7 @@ from theano.sandbox.cuda.var import float32_shared_constructor as f32sc
from theano.sandbox.cuda import CudaNdarrayType, cuda_available
import theano.sandbox.cuda as cuda
# Skip test if cuda_ndarray is not available.
if cuda_available == False:
if cuda_available is False:
raise SkipTest('Optional package cuda disabled')
......@@ -26,20 +26,19 @@ def test_float32_shared_constructor():
# test that broadcastable arg is accepted, and that they
# don't strictly have to be tuples
assert eq(
f32sc(npy_row, broadcastable=(True, False)).type,
CudaNdarrayType((True, False)))
assert eq(
f32sc(npy_row, broadcastable=[True, False]).type,
CudaNdarrayType((True, False)))
assert eq(
f32sc(npy_row, broadcastable=numpy.array([True, False])).type,
CudaNdarrayType([True, False]))
assert eq(f32sc(npy_row,
broadcastable=(True, False)).type,
CudaNdarrayType((True, False)))
assert eq(f32sc(npy_row,
broadcastable=[True, False]).type,
CudaNdarrayType((True, False)))
assert eq(f32sc(npy_row,
broadcastable=numpy.array([True, False])).type,
CudaNdarrayType([True, False]))
# test that we can make non-matrix shared vars
assert eq(
f32sc(numpy.zeros((2, 3, 4, 5), dtype='float32')).type,
CudaNdarrayType((False,) * 4))
assert eq(f32sc(numpy.zeros((2, 3, 4, 5), dtype='float32')).type,
CudaNdarrayType((False,) * 4))
def test_givens():
......@@ -72,13 +71,14 @@ class T_updates(unittest.TestCase):
# This test case uses code mentionned in #698
data = numpy.random.rand(10, 10).astype('float32')
output_var = f32sc(name="output",
value=numpy.zeros((10, 10), 'float32'))
value=numpy.zeros((10, 10), 'float32'))
x = tensor.fmatrix('x')
output_updates = [(output_var, x ** 2)]
output_givens = {x: data}
output_func = theano.function(inputs=[], outputs=[],
updates=output_updates, givens=output_givens)
output_func = theano.function(
inputs=[], outputs=[],
updates=output_updates, givens=output_givens)
output_func()
def test_err_ndim(self):
......
theano/sandbox/cuda/tests/test_viewop.py
浏览文件 @ b69ad54d
from __future__ import absolute_import, print_function, division
import numpy
import unittest
from nose.plugins.skip import SkipTest
import theano
......@@ -11,7 +10,7 @@ mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
def test_viewop_gpu():
from theano.sandbox import cuda
if cuda.cuda_available == False:
if cuda.cuda_available is False:
raise SkipTest('Optional package cuda disabled')
_x = theano.tensor.fvector('x')
x = cuda.gpu_from_host(_x)
......@@ -19,6 +18,6 @@ def test_viewop_gpu():
out = cuda.host_from_gpu(_out)
f = theano.function([x],
out,
mode=mode_with_gpu)
mode=mode_with_gpu)
data = numpy.array([1, 2, 3], dtype='float32')
assert numpy.allclose(f(data), data)
theano/sandbox/cuda/tests/walltime.py
浏览文件 @ b69ad54d
from __future__ import absolute_import, print_function, division
from __future__ import print_function
import sys, time
import sys
import time
from six import iteritems
from theano.compile.pfunc import pfunc
from theano import tensor
......@@ -35,35 +36,47 @@ def showtimes(times):
def cmp_sigmoids(shape):
def numpy_sigmoid(input):
rval = 1.0 / (1.0 + numpy.exp(-input))
sinput = tensor.Tensor(dtype='float32', broadcastable=(0,)*len(shape))()
shared_input = tcn.shared_constructor(numpy.random.rand(*shape), 'shared_input')
times = compare_fns(
dict( numpy=numpy_sigmoid
, theano_cpu=pfunc([sinput], 1.0 / (1.0 + tensor.exp(-sinput)))
, theano_gpu_onboard=pfunc([sinput], [], updates=[(shared_input, 1.0 / (1.0 + tensor.exp(-shared_input)))])
),
input=shared_input.value)
1.0 / (1.0 + numpy.exp(-input))
sinput = tensor.Tensor(
dtype='float32', broadcastable=(0,) * len(shape))()
shared_input = tcn.shared_constructor(
numpy.random.rand(*shape),
'shared_input')
times = compare_fns(dict(
numpy=numpy_sigmoid,
theano_cpu=pfunc([sinput], 1.0 / (1.0 + tensor.exp(-sinput))),
theano_gpu_onboard=pfunc(
[sinput],
[],
updates=[(
shared_input,
1.0 / (1.0 + tensor.exp(-shared_input)))])),
input=shared_input.value)
showtimes(times)
def cmp_sigmoids_T(shape):
def numpy_sigmoid(input):
rval = 1.0 / (1.0 + numpy.exp(-input.T))
sinput = tensor.Tensor(dtype='float32', broadcastable=(0,)*len(shape))()
shared_input = tcn.shared_constructor(numpy.random.rand(*shape), 'shared_input')
times = compare_fns(
dict( numpy=numpy_sigmoid
, theano_cpu=pfunc([sinput], 1.0 / (1.0 + tensor.exp(-sinput.T)))
, theano_gpu_onboard=pfunc([sinput], [], updates=[(shared_input, 1.0 / (1.0 +
tensor.exp(-shared_input.T)))])
),
input=shared_input.value)
1.0 / (1.0 + numpy.exp(-input.T))
sinput = tensor.Tensor(
dtype='float32', broadcastable=(0,) * len(shape))()
shared_input = tcn.shared_constructor(
numpy.random.rand(*shape),
'shared_input')
times = compare_fns(dict(
numpy=numpy_sigmoid,
theano_cpu=pfunc([sinput], 1.0 / (1.0 + tensor.exp(-sinput.T))),
theano_gpu_onboard=pfunc(
[sinput],
[],
updates=[(
shared_input,
1.0 / (1.0 + tensor.exp(-shared_input.T)))])),
input=shared_input.value)
showtimes(times)
if __name__ == '__main__':
eval(sys.argv[1])
# cmp_sigmoids((640, 64*64)) # looks great in profiler
#cmp_sigmoids((173, 74*49))
#cmp_sigmoids_T((173, 74*49))
# cmp_sigmoids((173, 74*49))
# cmp_sigmoids_T((173, 74*49))
theano/sandbox/cuda/type.py
浏览文件 @ b69ad54d
......@@ -130,10 +130,10 @@ class CudaNdarrayType(Type):
type(data) is float and
self.dtype == theano.config.floatX):
return cuda.filter(converted_data, self.broadcastable,
strict, old_data)
strict, old_data)
elif numpy.all(data == converted_data):
return cuda.filter(converted_data, self.broadcastable,
strict, old_data)
strict, old_data)
else:
raise TypeError(
'%s, with dtype %s, cannot store accurately value %s, '
......@@ -259,8 +259,8 @@ class CudaNdarrayType(Type):
'complex64': (complex, 'theano_complex64',
'NPY_COMPLEX64')}[self.dtype]
except KeyError:
raise TypeError("Unsupported dtype for %s: %s" % (
self.__class__.__name__, self.dtype))
raise TypeError("Unsupported dtype for %s: %s" %
(self.__class__.__name__, self.dtype))
def __eq__(self, other):
"""
......@@ -271,10 +271,11 @@ class CudaNdarrayType(Type):
other.broadcastable == self.broadcastable)
def convert_variable(self, var):
if (type(self) == type(var.type) and
self.ndim == var.type.ndim and
all(sb == ob or ob for sb, ob in zip(self.broadcastable,
var.type.broadcastable))):
if (isinstance(self, type(var.type)) and
self.ndim == var.type.ndim and
all(sb == ob or ob for sb, ob in zip(
self.broadcastable,
var.type.broadcastable))):
return theano.tensor.patternbroadcast(var, self.broadcastable)
def __hash__(self):
......@@ -312,7 +313,7 @@ class CudaNdarrayType(Type):
return self.name
else:
b = self.broadcastable
#bcast = str(self.broadcastable)
# bcast = str(self.broadcastable)
if not numpy.any(b):
s = "%iD" % len(b)
else:
......@@ -327,7 +328,7 @@ class CudaNdarrayType(Type):
def __repr__(self):
return str(self)
#"CudaNdarrayType{%s, %s}" % (str(self.dtype), str(self.broadcastable))
# "CudaNdarrayType{%s, %s}" % (str(self.dtype), str(self.broadcastable))
def c_declare(self, name, sub, check_input=True):
return """ CudaNdarray * %(name)s;""" % locals()
......@@ -417,7 +418,7 @@ class CudaNdarrayType(Type):
return sio.getvalue()
def c_extract_out(self, name, sub, check_input=True, check_broadcast=True):
"""
"""
To allow the hack to skip check_broadcast.
"""
......@@ -528,13 +529,13 @@ theano.compile.ops.expandable_types += (CudaNdarrayType,)
# Register C code for ViewOp on CudaNdarrayType
theano.compile.register_view_op_c_code(
CudaNdarrayType,
"""
Py_XDECREF(%(oname)s);
%(oname)s = %(iname)s;
Py_XINCREF(%(oname)s);
""",
version=1)
CudaNdarrayType,
"""
Py_XDECREF(%(oname)s);
%(oname)s = %(iname)s;
Py_XINCREF(%(oname)s);
""",
version=1)
theano.compile.register_shape_i_c_code(
CudaNdarrayType,
......@@ -555,16 +556,15 @@ theano.compile.register_shape_i_c_code(
# Register CudaNdarrayType to the DeepCopyOp list of types with c code.
theano.compile.register_deep_copy_op_c_code(
CudaNdarrayType,
"""
int alloc = %(oname)s == NULL;
for(int i=0; !alloc && i<CudaNdarray_NDIM(%(oname)s); i++) {
if(CudaNdarray_HOST_DIMS(%(iname)s)[i] !=
CudaNdarray_HOST_DIMS(%(oname)s)[i]) {
alloc = true;
break;
}
}
CudaNdarrayType,
"""
int alloc = %(oname)s == NULL;
for(int i=0; !alloc && i<CudaNdarray_NDIM(%(oname)s); i++) {
if(CudaNdarray_HOST_DIMS(%(iname)s)[i] !=
CudaNdarray_HOST_DIMS(%(oname)s)[i]) {
alloc = true;
break;
}}
if(alloc) {
Py_XDECREF(%(oname)s);
%(oname)s = (CudaNdarray*)CudaNdarray_Copy(%(iname)s);
......@@ -581,8 +581,7 @@ theano.compile.register_deep_copy_op_c_code(
%(fail)s;
}
}
""",
version=3)
""", version=3)
# THIS WORKS But CudaNdarray instances don't compare equal to one
......@@ -608,5 +607,5 @@ def CudaNdarray_pickler(cnda):
# In case cuda is not imported.
if cuda is not None:
copyreg.pickle(cuda.CudaNdarray, CudaNdarray_pickler,
CudaNdarray_unpickler)
copyreg.pickle(
cuda.CudaNdarray, CudaNdarray_pickler, CudaNdarray_unpickler)
theano/sandbox/cuda/var.py
浏览文件 @ b69ad54d
......@@ -13,7 +13,7 @@ try:
# We must do those import to be able to create the full doc when nvcc
# is not available
from theano.sandbox.cuda import filter as type_support_filter
from theano.sandbox.cuda.basic_ops import HostFromGpu, GpuFromHost
from theano.sandbox.cuda.basic_ops import HostFromGpu
except ImportError:
pass
......@@ -33,6 +33,7 @@ class _operators(tensor.basic._tensor_py_operators):
def _as_TensorVariable(self):
return HostFromGpu()(self)
def _as_CudaNdarrayVariable(self):
return self
......@@ -54,6 +55,7 @@ class CudaNdarrayConstantSignature(tensor.TensorConstantSignature):
class CudaNdarrayConstant(_operators, Constant):
def signature(self):
return CudaNdarrayConstantSignature((self.type, numpy.asarray(self.data)))
def __str__(self):
if self.name is not None:
return self.name
......@@ -61,7 +63,7 @@ class CudaNdarrayConstant(_operators, Constant):
data = str(numpy.asarray(self.data))
except Exception as e:
data = "error while transferring the value: " + str(e)
return "CudaNdarrayConstant{"+data+"}"
return "CudaNdarrayConstant{" + data + "}"
CudaNdarrayType.Constant = CudaNdarrayConstant
......
theano/tests/test_flake8.py
浏览文件 @ b69ad54d
......@@ -87,42 +87,8 @@ whitelist_flake8 = [
"sandbox/tests/test_theano_object.py",
"sandbox/tests/test_scan.py",
"sandbox/tests/__init__.py",
"sandbox/cuda/var.py",
"sandbox/cuda/GpuConvGrad3D.py",
"sandbox/cuda/basic_ops.py",
"sandbox/cuda/nnet.py",
"sandbox/cuda/elemwise.py",
"sandbox/cuda/type.py",
"sandbox/cuda/__init__.py",
"sandbox/cuda/opt.py",
"sandbox/cuda/blas.py",
"sandbox/cuda/blocksparse.py",
"sandbox/cuda/rng_curand.py",
"sandbox/cuda/fftconv.py",
"sandbox/cuda/kernel_codegen.py",
"sandbox/cuda/GpuConvTransp3D.py",
"sandbox/cuda/nvcc_compiler.py",
"sandbox/cuda/neighbours.py",
"sandbox/cuda/tests/__init__.py",
"sandbox/cuda/tests/walltime.py",
"sandbox/cuda/tests/test_gradient.py",
"sandbox/cuda/tests/test_neighbours.py",
"sandbox/cuda/tests/test_conv_cuda_ndarray.py",
"sandbox/cuda/tests/test_var.py",
"sandbox/cuda/tests/test_opt.py",
"sandbox/cuda/tests/test_blas.py",
"sandbox/cuda/tests/test_driver.py",
"sandbox/cuda/tests/test_rng_curand.py",
"sandbox/cuda/tests/test_basic_ops.py",
"sandbox/cuda/tests/test_memory.py",
"sandbox/cuda/tests/test_mlp.py",
"sandbox/cuda/tests/test_bench_loopfusion.py",
"sandbox/cuda/tests/test_blocksparse.py",
"sandbox/cuda/tests/test_cuda_ndarray.py",
"sandbox/cuda/tests/test_tensor_op.py",
"sandbox/cuda/tests/test_extra_ops.py",
"sandbox/cuda/tests/test_gemmcorr3d.py",
"sandbox/cuda/tests/test_viewop.py",
"sandbox/gpuarray/tests/__init__.py",
"sandbox/scan_module/scan_utils.py",
"sandbox/scan_module/scan.py",
......
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