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提交 61168784 authored 作者: Olivier Delalleau's avatar Olivier Delalleau
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Merge pull request #179 from delallea/typos

Typos
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正在显示 包含 65 行增加 和 61 行删除
theano/sandbox/cuda/opt.py
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......@@ -40,7 +40,7 @@ optdb.register('gpu_opt',
optdb.__position__.get('add_destroy_handler', 49.5) - 1,
'gpu', 'fast_run')
# This second pass is needed as the fusion can put all the non float32 code
# inside the elemwise. When it there is no float64 op, this is working.
# inside the elemwise. When there is no float64 op, this is working.
optdb.register('gpu_after_fusion',
ProxyDB(gpu_seqopt),
optdb.__position__.get('elemwise_fusion', 71) + .1,
......
theano/scan_module/scan_utils.py
浏览文件 @ 61168784
......@@ -370,8 +370,8 @@ def infer_shape(outs, inputs, input_shapes):
Compute the shape of the outputs given the shape of the inputs
of a theano graph.
We do it this way to don't compile the inner function just to get
the shape. Change to ShapeFeature could request change in this function.
We do it this way to avoid compiling the inner function just to get
the shape. Changes to ShapeFeature could require changes in this function.
'''
# We use a ShapeFeature because it has all the necessary logic
# inside. We don't use the full ShapeFeature interface, but we
......
theano/tensor/opt.py
浏览文件 @ 61168784
......@@ -141,24 +141,26 @@ def inplace_elemwise_optimizer_op(OP):
x + y + z -> x += y += z
(x + y) * (x * y) -> (x += y) *= (x * y) or (x + y) *= (x *= y)
"""
#we should not validate too often as this take much time to execute!
#It is the _dfs_toposort() fct in theano/gof/destroyhandler.py
#that take so much time.
#Should we try to use another lib that do toposort?
# We should not validate too often as this takes too much time to
# execute!
# It is the _dfs_toposort() fct in theano/gof/destroyhandler.py
# that takes so much time.
# Should we try to use another lib that does toposort?
# igraph: http://igraph.sourceforge.net/
# networkx: https://networkx.lanl.gov/
#Should we try to use cython?
# compiling only that fct is not enought, should we try to
# add the deque class too? and init the deque and other
# list to an upper bound number of element?
#Should Theano do online toposort as in
# http://code.google.com/p/acyclic/?
# Should we try to use cython?
# Compiling only that fct is not enough, should we try to add the
# deque class too?
# And init the deque and other list to an upper bound number of
# elements?
# Maybe Theano should do online toposort as in
# http://code.google.com/p/acyclic
#
#The next longuest optimizer is the canonizer phase
#Then I think it is the [io_?]toposort(need to validate) so
#check if the solution is also applicable their.
# The next longest optimizer is the canonizer phase.
# Then I think it is the [io_?]toposort (need to validate) so check if
# the solution is also applicable there.
#we execute validate after this number of change.
# We execute `validate` after this number of change.
check_each_change = config.tensor.insert_inplace_optimizer_validate_nb
if check_each_change == -1:
if len(env.nodes) > 500:
......@@ -229,10 +231,9 @@ def inplace_elemwise_optimizer_op(OP):
nb_change_no_validate = 0
except (ValueError, TypeError, InconsistencyError), e:
if check_each_change != 1 and not raised_warning:
print >> sys.stderr, ("Their was some inplace"
" optimization that was not"
" done due to unexpected"
" error:")
print >> sys.stderr, (
"Some inplace optimization was not "
"performed due to unexpected error:")
print >> sys.stderr, e
raised_warning = True
env.revert(chk)
......@@ -245,11 +246,10 @@ def inplace_elemwise_optimizer_op(OP):
if nb_change_no_validate > 0:
try:
env.validate()
except Exception, e:
except Exception:
if not raised_warning:
print >> sys.stderr, ("Their was some inplace optimization"
" that was not done due to"
" unexpected error")
print >> sys.stderr, ("Some inplace optimization was not "
"performed due to unexpected error")
env.revert(chk)
return inplace_elemwise_optimizer
......@@ -1031,8 +1031,8 @@ def local_fill_to_alloc(node):
@gof.local_optimizer([T.alloc])
def local_useless_alloc(node):
"""
if the input type is the same as the output type(dtype and broadcast)
their is no change in the shape of the input. So this is just a simple copy
If the input type is the same as the output type (dtype and broadcast)
there is no change in the shape of the input. So this is just a simple copy
of the input. This is not needed.
"""
if node.op == T.alloc:
......@@ -2958,8 +2958,8 @@ def local_sum_sum(node):
assert len(newaxis) == len(list(summed.owner.op.axis) +
list(node.op.axis))
#The old bugged logic. We keep it their to generate a
#warning when we generated bad code.
# The old bugged logic. We keep it there to generate a warning
# when we generated bad code.
alldims = range(summed.owner.inputs[0].type.ndim)
alldims = [d for i, d in enumerate(alldims) if i
in summed.owner.op.axis]
......@@ -2969,19 +2969,22 @@ def local_sum_sum(node):
xrange(summed.owner.inputs[0].type.ndim)
if i not in alldims]
if (theano.config.warn.sum_sum_bug and newaxis != newaxis_old
and len(newaxis) == len(newaxis_old)):
_logger.warn("WARNING: YOUR CURRENT CODE IS FINE. Theano"
" version between version 9923a40c7b7a and"
" the 2 august 2010(fixation date), generated"
" an error in that case. This happen when"
" their is 2 consecutive sum in the graph"
" and the intermediate sum is not used"
" elsewhere in the code. Some safe guard"
" removed some bad code, but not in all case."
" You are in one such case. To disable this"
" warning, set the theano flags"
" warn.sum_sum_bug to False.")
if (theano.config.warn.sum_sum_bug and
newaxis != newaxis_old and
len(newaxis) == len(newaxis_old):
_logger.warn(
"WARNING (YOUR CURRENT CODE IS FINE): Theano "
"versions between version 9923a40c7b7a and August "
"2nd, 2010 generated bugged code in this case. "
"This happens when there are two consecutive sums "
"in the graph and the intermediate sum is not "
"used elsewhere in the code. Some safeguard "
"removed some bad code, but not in all cases. You "
"are in one such case. To disable this warning "
"(that you can safely ignore since this bug has "
"been fixed) set the theano flag "
"`warn.sum_sum_bug` to False.")
combined_sum = T.Sum(newaxis)
return [combined_sum(summed.owner.inputs[0])]
......@@ -3857,12 +3860,11 @@ def local_log_erfc(node):
# sqrt(pi)*-x/(1-1/(2*x**2)+3/(4*x**4)-15/(8*x**6)))
#for float64: threshold=26.63 see at the end of the fct for the explaination
#for float32: threshold=9.3 see at the end of the fct for the explaination
#TODO: remove the contraint that their is only 2 inputs to mul and the
# exp(x**2) is the second.
#TODO: at the test point 10 in float32, their is instability in the
# original value. the original give -30.0, the stab -20.1 and in
# float64 -18.1. Make the test don't generate error in that
# case!
#TODO: remove the contraint that there are only 2 inputs to mul and exp(x**2)
# is the second.
#TODO: at the test point 10 in float32, there is instability in the original
# value. The original gives -30.0, the stab -20.1 and in float64 -18.1.
# Make it so that the test does not generate an error in that case!
@register_stabilize
@register_specialize
@gof.local_optimizer([T.true_div])
......@@ -3904,15 +3906,15 @@ def local_grad_log_erfc_neg(node):
sqr = neg.owner.inputs[0]
x = sqr.owner.inputs[0]
elif exp.owner.inputs[0].owner.op == T.mul:
#We should compare that -(erfc_x**2) is equivalent to mul_neg
#Their is currently not easy way to do this in the general case
#So we implement some common case for now.
# We should compare that -(erfc_x**2) is equivalent to mul_neg.
# There is currently no easy way to do this in the general case,
# so we implement some common case for now.
#in many case the neg are replaced by mul in the graph.
#This also allow to stabilize log(erfc(cst*x))
# In many cases the neg are replaced by mul in the graph.
# This also allows to stabilize log(erfc(cst*x)).
mul_neg = exp.owner.inputs[0]
#in case that multiple mul are not fused together, we do it here.
# In case that multiple mul are not fused together, we do it here.
def check_input(inputs):
new_inputs = []
for i in inputs:
......@@ -3923,7 +3925,7 @@ def local_grad_log_erfc_neg(node):
return new_inputs
mul_inputs = check_input(mul_neg.owner.inputs)
#put the constant first
# Put the constant first.
for i in xrange(len(mul_inputs)):
if isinstance(i, Constant):
if i == 0:
......@@ -4004,7 +4006,7 @@ The libm used for the test is amdlibm
#exp(x**2)/erfc(-x) => when x>threashold,
#-x*(1-1/(2*x**2)+3/(4*x**4)-15/(8*x**6))*sqrt(pi) for float64:
#threshold=26.63 see below for float32: threshold=9.3 see below TODO
#remove the contraint that their is only 2 inputs to mul TODO: should
#remove the contraint that there are only 2 inputs to mul TODO: should
#we cast numpy.pi to x.dtype?
#float32 threshold 9.3 as the approximation is more precise at that
......@@ -4234,12 +4236,14 @@ def local_elemwise_fusion_op(OP, max_input_fct=lambda node: 1024):
# There is a hard limit of 256 bytes for the formal argument list to a
# GPU kernel function.
max_nb_input = max_input_fct(node)
# The number of input to the new fused op if we don't fuse more inputs.
# The number of inputs to the new fused op if we do not fuse more
# inputs.
new_nb_input = len(node.inputs)
# Did we fused something. Needed as we can fuse unary op that
# don't change the number of input. And their is case where the
# input inputs are the same as the current node. That won't
# change the number of inputs of the new op.
# Did we fuse something?
# Needed as we can fuse unary op that don't change the number of
# inputs.
# And there is a case where the inputs are the same as the current
# node. That won't change the number of inputs of the new op.
fused = False
for i in node.inputs:
......
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