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提交 bdb5144c authored 作者: Frederic's avatar Frederic
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theano/compile/debugmode.py
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......@@ -48,19 +48,22 @@ AddConfigVar('DebugMode.check_finite',
AddConfigVar('DebugMode.check_strides',
("Check that Python- and C-produced ndarrays have same strides. "
"On difference: (0) - ignore, (1) warn, or (2) raise error"),
IntParam(1, lambda i: i in (0,1,2)),
IntParam(1, lambda i: i in (0, 1, 2)),
in_c_key=False)
AddConfigVar('DebugMode.warn_input_not_reused',
("Generate a warning when the destroy_map or view_map tell that an op work inplace, but the op did not reuse the input for its output."
("Generate a warning when the destroy_map or view_map tell that an"
" op work inplace, but the op did not reuse the input for its output."
),
BoolParam(True),
in_c_key=False)
def is_valid_check_preallocated_output_param(param):
if not isinstance(param, basestring):
return False
valid = ["previous", "c_contiguous", "f_contiguous", "neg_strides", "ALL", ""]
valid = ["previous", "c_contiguous", "f_contiguous",
"neg_strides", "ALL", ""]
for p in param.split(":"):
if p not in valid:
return False
......@@ -77,12 +80,14 @@ AddConfigVar('DebugMode.check_preallocated_output',
in_c_key=False)
import logging
_logger=logging.getLogger("theano.compile.debugmode")
_logger = logging.getLogger("theano.compile.debugmode")
_logger.setLevel(logging.WARNING)
# Filter to avoid duplicating optimization warnings
class NoDuplicateOptWarningFilter(logging.Filter):
prev_msgs = set([])
def filter(self, record):
msg = record.getMessage()
if msg.startswith('Optimization Warning: '):
......@@ -95,16 +100,17 @@ class NoDuplicateOptWarningFilter(logging.Filter):
_logger.addFilter(NoDuplicateOptWarningFilter())
########################
#
# Exceptions
#
########################
class DebugModeError(Exception):
"""Generic Exception raised to indicate an internal theano problem"""
pass
class BadCLinkerOutput(DebugModeError):
"""Exception: an Op's c_code and perform implementations don't agree."""
......@@ -119,20 +125,22 @@ class BadCLinkerOutput(DebugModeError):
def __init__(self, r, val_py, val_c):
"""Initialize members"""
DebugModeError.__init__(self)#to be compatible with python2.4
DebugModeError.__init__(self) # to be compatible with python2.4
self.r = r
self.val_py = val_py
self.val_c = val_c
def offending_op(self):
"""Return the Op class whose c_code and perform implementations didn't match"""
"""Return the Op class whose c_code and perform
implementations didn't match"""
return type(self.r.owner.op)
def __str__(self):
return self.str_diagnostic()
def str_diagnostic(self):
"""Return a pretty multiline string representating the cause of the exception"""
"""Return a pretty multiline string representating the cause
of the exception"""
sio = StringIO()
print >> sio, "BadCLinkerOutput"
print >> sio, " variable:", self.r
......@@ -171,15 +179,16 @@ class BadCLinkerOutput(DebugModeError):
except Exception:
pass
try:
ov=numpy.asarray(self.val_c)
nv=numpy.asarray(self.val_py)
ov = numpy.asarray(self.val_c)
nv = numpy.asarray(self.val_py)
ssio = StringIO()
absdiff = numpy.absolute(nv-ov)
absdiff = numpy.absolute(nv - ov)
print >> ssio, " Max Abs Diff: ", numpy.max(absdiff)
print >> ssio, " Mean Abs Diff: ", numpy.mean(absdiff)
print >> ssio, " Median Abs Diff: ", numpy.median(absdiff)
print >> ssio, " Std Abs Diff: ", numpy.std(absdiff)
reldiff = numpy.absolute(nv-ov) / (numpy.absolute(nv)+numpy.absolute(ov))
reldiff = numpy.absolute(nv - ov) / (numpy.absolute(nv) +
numpy.absolute(ov))
print >> ssio, " Max Rel Diff: ", numpy.max(reldiff)
print >> ssio, " Mean Rel Diff: ", numpy.mean(reldiff)
print >> ssio, " Median Rel Diff: ", numpy.median(reldiff)
......@@ -190,12 +199,15 @@ class BadCLinkerOutput(DebugModeError):
pass
return sio.getvalue()
class BadOptimization(DebugModeError):
"""Exception: some variable and its substitute take different runtime values.
"""Exception: some variable and its substitute take different
runtime values.
"""
new_r = None
"""A `Variable` instance that took a different value from `old_r`, but which replaced `old_r`."""
"""A `Variable` instance that took a different value from `old_r`,
but which replaced `old_r`."""
old_r = None
"""A `Variable` instance that was replaced by `new_r`."""
......@@ -209,18 +221,22 @@ class BadOptimization(DebugModeError):
reason = None
"""An object that indicates why old_r was turned into new_r.
Convention is that this is the name of the optimization that requested the replacement.
Convention is that this is the name of the optimization that
requested the replacement.
"""
old_graph = ""
"""A multiline string representation of the graph leading to old_r, at the time of the replacement."""
"""A multiline string representation of the graph leading to
old_r, at the time of the replacement."""
new_graph = ""
"""A multiline string representation of the graph leading to new_r, at the time of the replacement."""
"""A multiline string representation of the graph leading to
new_r, at the time of the replacement."""
def __init__(self, old_r, new_r, old_r_val, new_r_val, reason, old_graph, new_graph):
def __init__(self, old_r, new_r, old_r_val, new_r_val, reason,
old_graph, new_graph):
"""Initialize members"""
DebugModeError.__init__(self)#to be compatible with python2.4
DebugModeError.__init__(self) # to be compatible with python2.4
self.old_r = old_r
self.new_r = new_r
self.old_r_val = old_r_val
......@@ -233,10 +249,12 @@ class BadOptimization(DebugModeError):
return self.str_diagnostic()
def str_diagnostic(self):
"""Return a pretty multiline string representating the cause of the exception"""
"""Return a pretty multiline string representating the cause
of the exception"""
sio = StringIO()
val_str_len_limit = 800
print >> sio, "BadOptimization Error", super(BadOptimization, self).__str__()
print >> sio, "BadOptimization Error", super(BadOptimization,
self).__str__()
print >> sio, " Variable: id", id(self.new_r), self.new_r
print >> sio, " Op", self.new_r.owner
print >> sio, " Value Type:", type(self.new_r_val)
......@@ -253,7 +271,8 @@ class BadOptimization(DebugModeError):
str_old_r_val = str(self.old_r_val)
if len(str_old_r_val) > val_str_len_limit:
print >> sio, " Old Value: ", str(self.old_r_val)[:val_str_len_limit], '...'
print >> sio, " Old Value: ", str(self.old_r_val)[
:val_str_len_limit], '...'
else:
print >> sio, " Old Value: ", str(self.old_r_val)
......@@ -269,18 +288,23 @@ class BadOptimization(DebugModeError):
pass
str_new_r_val = str(self.new_r_val)
if len(str_new_r_val) > val_str_len_limit:
print >> sio, " New Value: ", str(self.new_r_val)[:val_str_len_limit], '...'
print >> sio, " New Value: ", str(self.new_r_val)[
:val_str_len_limit], '...'
else:
print >> sio, " New Value: ", str(self.new_r_val)
try:
ov=numpy.asarray(self.old_r_val)
nv=numpy.asarray(self.new_r_val)
ov = numpy.asarray(self.old_r_val)
nv = numpy.asarray(self.new_r_val)
ssio = StringIO()
print >> ssio, " Max Abs Diff: ", numpy.max(numpy.absolute(nv-ov))
print >> ssio, " Mean Abs Diff: ", numpy.mean(numpy.absolute(nv-ov))
print >> ssio, " Median Abs Diff: ", numpy.median(numpy.absolute(nv-ov))
print >> ssio, " Std Abs Diff: ", numpy.std(numpy.absolute(nv-ov))
print >> ssio, " Max Abs Diff: ", numpy.max(numpy.absolute(nv -
ov))
print >> ssio, " Mean Abs Diff: ", numpy.mean(numpy.absolute(nv -
ov))
print >> ssio, " Median Abs Diff: ", numpy.median(numpy.absolute(
nv - ov))
print >> ssio, " Std Abs Diff: ", numpy.std(numpy.absolute(
nv - ov))
# N.B. the maximum(..., 1e-8) protects against div by 0 when
# nv == ov == 0
......@@ -307,8 +331,10 @@ class BadOptimization(DebugModeError):
print >> sio, " or even tensor.cmp_sloppy=2 for less-strict comparison"
return sio.getvalue()
class BadDestroyMap(DebugModeError):
"""Exception: Some perform() or c_code() modified an input that wasn't in the destroy_map"""
"""Exception: Some perform() or c_code() modified an input that
wasn't in the destroy_map"""
def __init__(self, node, idx, old_val, new_val, perform):
#super(BadDestroyMap, self).__init__()
DebugModeError.__init__(self)#to be compatible with python2.4
......@@ -322,8 +348,10 @@ class BadDestroyMap(DebugModeError):
sio = StringIO()
print >> sio, " node:", self.node
print >> sio, " perform:", self.perform
print >> sio, " node.inputs:", [(str(i), id(i)) for i in self.node.inputs]
print >> sio, " destroy_map:", getattr(self.node.op, 'destroy_map', {})
print >> sio, " node.inputs:", [(str(i), id(i))
for i in self.node.inputs]
print >> sio, " destroy_map:", getattr(self.node.op,
'destroy_map', {})
print >> sio, " changed input idx:", self.idx
print >> sio, " changed input type:", self.node.inputs[self.idx].type
print >> sio, " repr (old val):", repr(self.old_val)
......@@ -347,11 +375,14 @@ class BadDestroyMap(DebugModeError):
print >> sio, " Hint: this can also be caused by a deficient values_eq_approx() or __eq__() implementation [which compared input values]"
return sio.getvalue()
class BadViewMap(DebugModeError):
"""Exception: Some perform() or c_code() created a memory alias that wasn't in the view_map"""
def __init__(self, node, output_idx, out_storage, in_alias_idx=None, out_alias_idx=None):
"""Exception: Some perform() or c_code() created a memory alias
that wasn't in the view_map"""
def __init__(self, node, output_idx, out_storage,
in_alias_idx=None, out_alias_idx=None):
#super(BadViewMap, self).__init__()
DebugModeError.__init__(self)#to be compatible with python2.4
DebugModeError.__init__(self) # to be compatible with python2.4
self.node = node
self.output_idx = output_idx
self.out_storage = out_storage
......@@ -361,10 +392,13 @@ class BadViewMap(DebugModeError):
def __str__(self):
sio = StringIO()
print >> sio, " node:", self.node
print >> sio, " node.inputs:", [(str(i), id(i)) for i in self.node.inputs]
print >> sio, " node.outputs:", [(str(i), id(i)) for i in self.node.outputs]
print >> sio, " node.inputs:", [(str(i), id(i))
for i in self.node.inputs]
print >> sio, " node.outputs:", [(str(i), id(i))
for i in self.node.outputs]
print >> sio, " view_map:", getattr(self.node.op, 'view_map', {})
print >> sio, " destroy_map:", getattr(self.node.op, 'destroy_map', {})
print >> sio, " destroy_map:", getattr(self.node.op,
'destroy_map', {})
print >> sio, " aliased output:", self.output_idx
print >> sio, " aliased output storage:", self.out_storage
if self.in_alias_idx:
......@@ -373,27 +407,33 @@ class BadViewMap(DebugModeError):
print >> sio, " aliased to outputs:", self.out_alias_idx
return sio.getvalue()
class StochasticOrder(DebugModeError):
"""Exception: Repeated Optimizations of the same graph do not give identical results.
"""Exception: Repeated Optimizations of the same graph do not give
identical results.
The most common cause is that an Optimization iterates over some objects in a
memory-address-dependent order (such as id() or object.hash()). If you see this error and
you think it is related to optimizations within Theano, email theano-dev with the message
The most common cause is that an Optimization iterates over some
objects in a memory-address-dependent order (such as id() or
object.hash()). If you see this error and you think it is related
to optimizations within Theano, email theano-dev with the message
attached to this exception.
"""
pass
class InvalidValueError(DebugModeError):
"""Exception: some Op an output value that is inconsistent with the Type of that output"""
def __init__(self, r, v, client_node=None, hint='none', specific_hint='none'):
"""Exception: some Op an output value that is inconsistent with
the Type of that output"""
def __init__(self, r, v, client_node=None, hint='none',
specific_hint='none'):
#super(InvalidValueError, self).__init__()
DebugModeError.__init__(self)#to be compatible with python2.4
DebugModeError.__init__(self) # to be compatible with python2.4
self.r = r
self.v = v
self.client_node = client_node
self.hint=hint
self.specific_hint=specific_hint
self.hint = hint
self.specific_hint = specific_hint
def __str__(self):
r, v = self.r, self.v
......@@ -455,7 +495,7 @@ def debugprint(r, prefix='', depth=-1, done=None, print_type=False,
:param print_view_map: wether to print the op view_map after ofther info
:param order: If not empty will print the index in the toposort.
"""
if depth==0:
if depth == 0:
return
if done is None:
......@@ -478,40 +518,41 @@ def debugprint(r, prefix='', depth=-1, done=None, print_type=False,
r_name = ''
if print_destroy_map:
destroy_map_str=str(getattr(r.owner.op,'destroy_map',''))
destroy_map_str = str(getattr(r.owner.op, 'destroy_map', ''))
else:
destroy_map_str=''
destroy_map_str = ''
if print_view_map:
view_map_str=str(getattr(r.owner.op,'view_map',''))
view_map_str = str(getattr(r.owner.op, 'view_map', ''))
else:
view_map_str=''
if destroy_map_str and destroy_map_str!='{}':
destroy_map_str='d='+destroy_map_str
if view_map_str and view_map_str!='{}':
view_map_str='v='+view_map_str
view_map_str = ''
if destroy_map_str and destroy_map_str != '{}':
destroy_map_str = 'd=' + destroy_map_str
if view_map_str and view_map_str != '{}':
view_map_str = 'v=' + view_map_str
o=''
o = ''
if order:
o = str(order.index(r.owner))
if len(a.outputs) == 1:
print >> file, '%s%s [@%i]%s \'%s\' %s %s %s' % (prefix, a.op, id(r),
type_str, r_name,
destroy_map_str,
view_map_str,
o)
else:
print >> file, '%s%s.%i [@%i]%s \'%s\' %s %s %s' % (prefix, a.op,
a.outputs.index(r),
id(r), type_str,
r_name,
print >> file, '%s%s [@%i]%s \'%s\' %s %s %s' % (prefix, a.op,
id(r),
type_str, r_name,
destroy_map_str,
view_map_str,
o)
else:
print >> file, '%s%s.%i [@%i]%s \'%s\' %s %s %s' % (prefix, a.op,
a.outputs.index(r),
id(r), type_str,
r_name,
destroy_map_str,
view_map_str,
o)
if id(a) not in done:
done.add(id(a))
for i in a.inputs:
debugprint(i, prefix+' |', depth=depth-1, done=done,
debugprint(i, prefix + ' |', depth=depth-1, done=done,
print_type=print_type, file=file, order=order)
else:
#this is a variable
......@@ -519,7 +560,7 @@ def debugprint(r, prefix='', depth=-1, done=None, print_type=False,
return file
def _optcheck_env(input_specs, output_specs, accept_inplace = False):
def _optcheck_env(input_specs, output_specs, accept_inplace=False):
"""Create an Env for debugging.
:param input_specs: env inputs
......@@ -547,18 +588,23 @@ def _optcheck_env(input_specs, output_specs, accept_inplace = False):
if not accept_inplace:
for node in env.nodes:
if getattr(node.op, 'destroy_map', None):
raise TypeError("Graph must not contain inplace operations", node)
raise TypeError("Graph must not contain inplace operations",
node)
# We need to protect all immutable inputs from inplace operations.
env.extend(Supervisor(input for spec, input in zip(input_specs, inputs) if not (spec.mutable or (hasattr(env, 'destroyers') and env.destroyers(input)))))
env.extend(Supervisor(input for spec, input in zip(input_specs, inputs)
if not (spec.mutable or (hasattr(env, 'destroyers')
and env.destroyers(input)))))
# If named nodes are replaced, keep the name
env.extend(gof.toolbox.PreserveNames())
return env, map(SymbolicOutput, updates), equivalence_tracker
def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes, clobber_dr_vals=True,
perform=None, warn_input_not_reused=True):
def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes,
clobber_dr_vals=True,
perform=None, warn_input_not_reused=True):
"""Raise BadDestroyMap if necessary, update dr_vals"""
destroyed_idx_list = []
destroy_map = getattr(node.op, 'destroy_map', {})
......@@ -567,11 +613,11 @@ def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes, clobber_dr_v
destroyed_res_list = [node.inputs[i] for i in destroyed_idx_list]
if warn_input_not_reused and destroyed_res_list:
dmap=getattr(node.op,'destroy_map',{})
for oo,ii in dmap.iteritems():
dmap = getattr(node.op, 'destroy_map', {})
for oo, ii in dmap.iteritems():
out_var = storage_map[node.outputs[oo]][0]
in_var = storage_map[node.inputs[ii[0]]][0]
if isinstance (node.op, theano.compile.mode.OutputGuard):
if isinstance(node.op, theano.compile.mode.OutputGuard):
# The point of OutputGuard is to be declared as destructive
# while not destroying anything
continue
......@@ -581,15 +627,15 @@ def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes, clobber_dr_v
ii[0], str(node))
if warn_input_not_reused:
vmap=getattr(node.op,'view_map',{})
for oo,ii in vmap.iteritems():
vmap = getattr(node.op, 'view_map', {})
for oo, ii in vmap.iteritems():
out_var = storage_map[node.outputs[oo]][0]
in_var = storage_map[node.inputs[ii[0]]][0]
# We don't try to optimize simple scalar and empty ndarray,
# as this is not worth our time. This happen at least in
# Subtensor when the output is a scalar But this depend on
# the version of numpy!
if getattr(out_var,'size',2)<=1:
if getattr(out_var, 'size', 2) <= 1:
continue
if isinstance(node.op, theano.compile.mode.OutputGuard):
# This class is not in the final graph.
......@@ -613,7 +659,8 @@ def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes, clobber_dr_v
dr_vals[r] = (storage_map[r][0], node) #no copy, this is the last use of this variable
storage_map[r][0] = None #make sure that dr_vals[r] doens't get used again
else:
raise BadDestroyMap(node, r_idx, r_vals[r], storage_map[r][0], perform)
raise BadDestroyMap(node, r_idx, r_vals[r],
storage_map[r][0], perform)
def _check_viewmap(node, storage_map):
......@@ -641,8 +688,9 @@ def _check_viewmap(node, storage_map):
view_map = getattr(node.op, 'view_map', {})
destroy_map = getattr(node.op, 'destroy_map', {})
# In theory, theano's view_map only allows for 1 output to alias 1 input
# Checking for multiple aliases just in case...
# In theory, theano's view_map only allows for 1 output to
# alias 1 input. Checking for multiple aliases just in
# case...
for ii, inode in enumerate(node.inputs):
......@@ -660,7 +708,7 @@ def _check_viewmap(node, storage_map):
#TODO: make sure this is correct
# According to OB, duplicate inputs are rejected on build graph time
# if they cause problems. So if they are here it should be ok.
for key,val in good_alias.iteritems():
for key, val in good_alias.iteritems():
bad_alias.pop(key, None)
if bad_alias:
raise BadViewMap(node, oi, outstorage, bad_alias.values())
......@@ -668,33 +716,39 @@ def _check_viewmap(node, storage_map):
#if its not aliased to input, check output->output aliasing
if not good_alias and _is_used_in_graph(onode):
for other_oi, other_onode in enumerate(node.outputs):
if other_oi==oi: continue
if other_oi == oi: continue
other_storage = storage_map[other_onode][0]
# check to see if we share memory with this other output
# this is not a problem if the node is not actually used
if _is_used_in_graph(other_onode) and \
_may_share_memory(outstorage, other_storage):
raise BadViewMap(node, oi, outstorage, out_alias_idx=other_oi)
raise BadViewMap(node, oi, outstorage,
out_alias_idx=other_oi)
def _may_share_memory(a, b):
from theano.misc.may_share_memory import may_share_memory
return may_share_memory(a,b,False)
return may_share_memory(a, b, False)
def _is_function_output(node):
"""
Returns True if the node in question is the a final output of the graph
"""
return node.clients==[('output', 1)]
return node.clients == [('output', 1)]
def _is_used_in_graph(node):
return not(_is_function_output(node) or node.clients==[])
return not(_is_function_output(node) or node.clients == [])
def _check_strides_match(a, b, warn_err, op):
"""
param: warn_err: if 0, no warning, if 1 warning, if 2 error
"""
if warn_err==0: return
if warn_err == 0:
return
try:
strides_eq = a.strides == b.strides
......@@ -702,12 +756,14 @@ def _check_strides_match(a, b, warn_err, op):
return # no strides
if not strides_eq:
e = TypeError('Stride mismatch', (a.shape, b.shape, a.strides, b.strides, str(op)))
if warn_err==2:
e = TypeError('Stride mismatch', (a.shape, b.shape, a.strides,
b.strides, str(op)))
if warn_err == 2:
raise e
else:
print >> sys.stderr, 'WARNING:', e
def _lessbroken_deepcopy(a):
"""
:param a: any object
......@@ -727,15 +783,19 @@ def _lessbroken_deepcopy(a):
assert rval.dtype == a.dtype
return rval
def _find_bad_optimizations0(order, reasons, r_vals):
"""Use a simple algorithm to find broken optimizations.
This algorithm is simple to understand, but sometimes when there's a problem it identifies
the wrong optimization as the culprit. The problem stems from the fact that results are
not evaluated in chronological order (looking at when they were introduced to the graph).
This algorithm is simple to understand, but sometimes when there's
a problem it identifies the wrong optimization as the culprit.
The problem stems from the fact that results are not evaluated in
chronological order (looking at when they were introduced to the
graph).
"""
# iterate over variables looking for values that don't match the values of the
# variables they replaced. This is the sign of a broken optimization.
# iterate over variables looking for values that don't match the
# values of the variables they replaced. This is the sign of a
# broken optimization.
for i, node in enumerate(order):
for new_r in node.outputs:
for reason, r, old_graph_str, new_graph_str in reasons[new_r]:
......@@ -746,7 +806,7 @@ def _find_bad_optimizations0(order, reasons, r_vals):
r_val = r_vals[r]
assert r.type == new_r.type
if hasattr(new_r,'values_eq_approx'):
if hasattr(new_r, 'values_eq_approx'):
check = new_r.values_eq_approx(r_val, new_r_val)
else:
check = r.type.values_eq_approx(r_val, new_r_val)
......@@ -759,30 +819,34 @@ def _find_bad_optimizations0(order, reasons, r_vals):
old_graph=old_graph_str,
new_graph=new_graph_str)
def _find_bad_optimizations1(order, reasons, r_vals):
# iterate over variables looking for values that don't match the values of the
# variables they replaced. This is the sign of a broken optimization.
# iterate over variables looking for values that don't match the
# values of the variables they replaced. This is the sign of a
# broken optimization.
#identify sets of variables that are supposed to be equivalent
equivalence_sets = {}
program_position = {} #node -> order idx
program_position = {} # node -> order idx
for i, node in enumerate(order):
program_position[node] = i
for new_r in node.outputs:
equivalence_sets.setdefault(new_r, set([new_r]))
for reason, r, old_graph_str, new_graph_str in reasons[new_r]:
equivalence_sets[new_r].update(equivalence_sets.setdefault(r, set([r])))
equivalence_sets[new_r].update(equivalence_sets.setdefault(
r, set([r])))
for er in equivalence_sets[r]:
equivalence_sets[er] = equivalence_sets[new_r]
#identify equivalence sets that are broken
equivalence_sets_broken = {} #id(set) -> Bool
equivalence_sets_broken = {} # id(set) -> Bool
there_is_a_problem = False
for r, r_equiv in equivalence_sets.iteritems():
if id(r_equiv) not in equivalence_sets_broken:
equivalence_sets_broken[id(r_equiv)] = False
#loop over the variables in the set comparing them to be equal enough
#loop over the variables in the set comparing them to be
#equal enough
re0 = None
for re in r_equiv:
if re0:
......@@ -806,12 +870,15 @@ def _find_bad_optimizations1(order, reasons, r_vals):
print first_broken_set
raise Exception('broken')
def _find_bad_optimizations2(order, reasons, r_vals):
"""Use a simple algorithm to find broken optimizations.
This algorithm is simple to understand, but sometimes when there's a problem it identifies
the wrong optimization as the culprit. The problem stems from the fact that results are
not evaluated in chronological order (looking at when they were introduced to the graph).
This algorithm is simple to understand, but sometimes when there's
a problem it identifies the wrong optimization as the culprit.
The problem stems from the fact that results are not evaluated in
chronological order (looking at when they were introduced to the
graph).
"""
checked_variables = set()
......@@ -822,7 +889,8 @@ def _find_bad_optimizations2(order, reasons, r_vals):
new_r_val = r_vals[new_r]
r_val = r_vals[r]
if (r.type != new_r.type) or (not r.type.values_eq_approx(r_val, new_r_val)):
if (r.type != new_r.type) or (not r.type.values_eq_approx(
r_val, new_r_val)):
raise BadOptimization(old_r=r,
new_r=new_r,
old_r_val=r_val,
......@@ -850,15 +918,16 @@ def _find_bad_optimizations2(order, reasons, r_vals):
check_variable_norec(r)
# iterate over variables looking for values that don't match the values of the
# variables they replaced. This is the sign of a broken optimization.
# iterate over variables looking for values that don't match the
# values of the variables they replaced. This is the sign of a
# broken optimization.
for i, node in enumerate(order):
for new_r in node.outputs:
check_variable(new_r)
_find_bad_optimizations = _find_bad_optimizations0
def _check_preallocated_output(node, thunk, prealloc_modes, def_val,
storage_map, r_vals, dr_vals, perform, active_order_set):
'''Try to apply thunk() on different output storages'''
......@@ -924,7 +993,8 @@ def _check_preallocated_output(node, thunk, prealloc_modes, def_val,
prealloc_maps.append(('f_contiguous', f_cont_outputs))
if 'neg_strides' in prealloc_maps:
raise NotImplementedError('Negative strides in check_preallocated_output')
raise NotImplementedError('Negative strides in'
' check_preallocated_output')
for (name, out_map) in prealloc_maps:
# _logger.debug('name = %s, perform = %s', name, perform)
......@@ -944,7 +1014,8 @@ def _check_preallocated_output(node, thunk, prealloc_modes, def_val,
if not r.type.is_valid_value(storage_map[r][0]):
raise InvalidValueError(r, storage_map[r][0],
hint='%s with %s output' % (perform, name),
specific_hint=r.type.value_validity_msg(storage_map[r][0]))
specific_hint=r.type.value_validity_msg(
storage_map[r][0]))
_check_inputs(node, storage_map, r_vals, dr_vals, active_order_set,
clobber_dr_vals=False,
......@@ -956,16 +1027,19 @@ def _check_preallocated_output(node, thunk, prealloc_modes, def_val,
for r in node.outputs:
if not r.type.values_eq_approx(r_vals[r], storage_map[r][0]):
# TODO: indicate it is not a C/Py problem
raise BadCLinkerOutput(r, val_py=r_vals[r], val_c=storage_map[r][0])
raise BadCLinkerOutput(r, val_py=r_vals[r],
val_c=storage_map[r][0])
# Clear storage_map
for r in node.outputs:
storage_map[r][0] = None
class _EnvEvent(object):
"""A record of an event in the life of an Env.
The __eq__ function is important here, as it is the basis for comparing optimization runs.
The __eq__ function is important here, as it is the basis for
comparing optimization runs.
"""
kind = ""
......@@ -1014,8 +1088,9 @@ class _EnvEvent(object):
def __eq__(self, other):
rval = type(self) == type(other)
if rval:
# nodes are not compared because this comparison is supposed to be true for
# corresponding events that happen in different Env instances (different graphs)
# nodes are not compared because this comparison is
# supposed to be true for corresponding events that happen
# in different Env instances (different graphs)
for attr in ['kind', 'op', 'idx', 'reason']:
rval = rval and getattr(self, attr) == getattr(other, attr)
return rval
......@@ -1023,6 +1098,7 @@ class _EnvEvent(object):
def __ne__(self, other):
return not (self == other)
class _VariableEquivalenceTracker(object):
"""A Env Feature that keeps tabs on an Env and tries to detect problems."""
......@@ -1100,7 +1176,8 @@ class _VariableEquivalenceTracker(object):
def on_change_input(self, env, node, i, r, new_r, reason=None):
#print 'CHANGE by', reason, 'to use', new_r, type(new_r)
self.event_list.append(_EnvEvent('change', node, reason=str(reason), idx=i))
self.event_list.append(_EnvEvent('change', node,
reason=str(reason), idx=i))
self.reasons.setdefault(new_r, [])
self.replaced_by.setdefault(new_r, [])
......@@ -1111,12 +1188,12 @@ class _VariableEquivalenceTracker(object):
append_reason = False
if append_reason:
# N.B. compute the debugprint now, because future optimizations will change the
# graph
self.reasons[new_r].append((reason
, r
, debugprint(r, prefix=' ', depth=6, file=StringIO()).getvalue()
, debugprint(new_r, prefix=' ', depth=6, file=StringIO()).getvalue()))
# N.B. compute the debugprint now, because future
# optimizations will change the graph
self.reasons[new_r].append((reason,
r,
debugprint(r, prefix=' ', depth=6, file=StringIO()).getvalue(),
debugprint(new_r, prefix=' ', depth=6, file=StringIO()).getvalue()))
self.replaced_by[r].append((reason, new_r))
if r in self.equiv:
......@@ -1134,7 +1211,6 @@ class _VariableEquivalenceTracker(object):
assert new_r in new_r_set
assert r in r_set
# update one equivalence set to contain the other
# transfer all the elements of the old one to the new one
r_set.update(new_r_set)
......@@ -1165,7 +1241,7 @@ class _Linker(gof.link.LocalLinker):
self.env = None
self.maker = maker
def accept(self, env, no_recycling = []):
def accept(self, env, no_recycling=[]):
if self.env is not None and self.env is not env:
assert type(self) is _Linker
return type(self)(self.env, self.maker).accept(env, no_recycling)
......@@ -1173,13 +1249,15 @@ class _Linker(gof.link.LocalLinker):
self.no_recycling = no_recycling
return self
def make_all(self, profiler = None, input_storage = None, output_storage = None):
def make_all(self, profiler = None, input_storage = None
, output_storage = None):
if 1:
#can't import at toplevel because of circular import
# TODO: don't do this ugly hacky way of setting the filter_checks_isfinite
from theano.tensor import TensorType #to set filter_check_isfinite
from theano import tests # for config.unittests.rseed
#can't import at toplevel because of circular import TODO:
# don't do this ugly hacky way of setting the
# filter_checks_isfinite
from theano.tensor import TensorType # to set filter_check_isfinite
from theano import tests # for config.unittests.rseed
env = self.env
input_storage_ = input_storage
output_storage_ = output_storage
......@@ -1191,16 +1269,16 @@ class _Linker(gof.link.LocalLinker):
order_outputs.reverse()
order = graph.io_toposort(env.inputs, order_outputs)
active_order = env.toposort() #an ordering of just the active nodes
active_order = env.toposort() # an ordering of just the active nodes
active_order_set = set(active_order)
no_recycling = self.no_recycling
input_storage, output_storage, storage_map = link.map_storage(env, order,
input_storage_, output_storage_)
input_storage, output_storage, storage_map = link.map_storage(
env, order, input_storage_, output_storage_)
thunks_py = [] #python thunks
thunks_c = [] #c thunks
thunks_py = [] # python thunks
thunks_c = [] # c thunks
for node in order:
node_input_storage = [storage_map[r] for r in node.inputs]
......@@ -1216,14 +1294,16 @@ class _Linker(gof.link.LocalLinker):
if not isinstance(node.op, gof.op.Op):
raise utils.MethodNotDefined()
e = Env(*graph.clone(node.inputs, node.outputs))
e.toposort = lambda: e.nodes #WARNING: STOCHASTIC ORDER
e.toposort = lambda: e.nodes # WARNING: STOCHASTIC ORDER
# Specifically... e.nodes is a set, but of only 1 element
cl = CLinker().accept(e, [r for r, r2 in zip(e.outputs, node.outputs) if r2 in no_recycling])
cl = CLinker().accept(e, [r for r, r2 in zip(e.outputs,
node.outputs)
if r2 in no_recycling])
thunk, node_input_filters, node_output_filters = cl.make_thunk(
input_storage = node_input_storage,
output_storage = node_output_storage)
input_storage=node_input_storage,
output_storage=node_output_storage)
thunk.inputs = node_input_storage
thunk.outputs = node_output_storage
thunks_c.append(thunk)
......@@ -1236,8 +1316,9 @@ class _Linker(gof.link.LocalLinker):
if ((self.maker.mode.check_py_code or thunks_c[-1] is None) and
node.op.perform.func_code != gof.op.PureOp.perform.func_code):
p = node.op.perform
thunk = (lambda p = p, i = node_input_storage, o = node_output_storage, n =
node: p(n, [x[0] for x in i], o))
thunk = (lambda p=p, i=node_input_storage,
o=node_output_storage,
n=node: p(n, [x[0] for x in i], o))
thunk.inputs = node_input_storage
thunk.outputs = node_output_storage
thunk.perform = p
......@@ -1283,14 +1364,15 @@ class _Linker(gof.link.LocalLinker):
elif thunks_c[-1] is None:
thunks_c[-1] = thunk
else:
_logger.warn("We won't check the perform function of node '%s' but we will check its make_thunk function"%node)
_logger.warn("We won't check the perform function of node '%s' but we will check its make_thunk function" % node)
thunks_py[-1] = thunk
if no_recycling is True:
no_recycling = storage_map.values()
no_recycling = utils.difference(no_recycling, input_storage)
else:
no_recycling = [storage_map[r] for r in no_recycling if r not in env.inputs]
no_recycling = [storage_map[r] for r in no_recycling
if r not in env.inputs]
# Precompute some things for storage pre-allocation
prealloc_modes = config.DebugMode.check_preallocated_output.split(':')
......@@ -1312,16 +1394,18 @@ class _Linker(gof.link.LocalLinker):
for x in no_recycling:
x[0] = None
# nest all this in try-finally to put storage *back* into storage_map when an
# exception is raised
original_storage_map_keys = [r for r in storage_map if r.owner is None]
# nest all this in try-finally to put storage *back* into
# storage_map when an exception is raised
original_storage_map_keys = [r for r in storage_map
if r.owner is None]
try:
equiv_vals = {}
problematic = set()
# r_vals are the true values associated with each variable in the graph
# they should not change during the evaluation of this function, even when the
# graph has destructive ops in it
# r_vals are the true values associated with each
# variable in the graph they should not change during
# the evaluation of this function, even when the graph
# has destructive ops in it
#
# This dictionary is used to populate the storage_map as necessary
r_vals = {}
......@@ -1343,7 +1427,7 @@ class _Linker(gof.link.LocalLinker):
# an error if it is not valid.
if (storage_map[r][0] is None):
raise InvalidValueError(r, storage_map[r][0],
hint="Graph Input '%s' is missing" % str(r))
hint="Graph Input '%s' is missing" % str(r))
raise InvalidValueError(r, storage_map[r][0],
hint=("Graph Input '%s' has invalid value "
"%s" % (r, storage_map[r][0])))
......@@ -1358,7 +1442,8 @@ class _Linker(gof.link.LocalLinker):
storage_map[r][0] = None
#####
# Precondition: the storage map is empty, transferred completely to r_vals
# Precondition: the storage map is empty, transferred
# completely to r_vals
#####
for r, s in storage_map.iteritems():
if s[0] is not None:
......@@ -1367,7 +1452,9 @@ class _Linker(gof.link.LocalLinker):
#try:
# compute the value of all variables
for i, (thunk_py, thunk_c, node) in enumerate(zip(thunks_py, thunks_c, order)):
for i, (thunk_py, thunk_c, node) in enumerate(zip(thunks_py,
thunks_c,
order)):
this_node_destroyed_variables = set()
_logger.debug("%i - starting node %i %s", i, i, node)
......@@ -1380,24 +1467,33 @@ class _Linker(gof.link.LocalLinker):
# print >> sys.stderr,i, "DEBUGMODE: deepcopy input ", r
storage_map[r][0] = _lessbroken_deepcopy(r_vals[r])
if not r.type.is_valid_value(storage_map[r][0]):
raise InvalidValueError(r, storage_map[r][0], client_node=node)
raise InvalidValueError(r, storage_map[r][0],
client_node=node)
## On the first call to thunk_py(), its output storage will be None
## On the first call to thunk_py(), its output
## storage will be None
if thunk_py:
_logger.debug("%i - running thunk_py with None as "
"output storage", i)
try:
thunk_py()
except utils.MethodNotDefined:
thunk_py = None #shouldn't have put it into the list in the first place
# shouldn't have put it into the list in
# the first place
thunk_py = None
if thunk_py:
# check output values for type-correctness
for r in node.outputs:
if not r.type.is_valid_value(storage_map[r][0]):
raise InvalidValueError(r, storage_map[r][0], hint='perform output', specific_hint = r.type.value_validity_msg(storage_map[r][0]))
hint2 = r.type.value_validity_msg(
storage_map[r][0])
raise InvalidValueError(r, storage_map[r][0],
hint='perform output',
specific_hint=hint2)
_check_inputs(node, storage_map, r_vals, dr_vals, active_order_set,
_check_inputs(node, storage_map, r_vals, dr_vals,
active_order_set,
clobber_dr_vals=True, perform='py',
warn_input_not_reused=config.DebugMode.warn_input_not_reused)
......@@ -1409,7 +1505,8 @@ class _Linker(gof.link.LocalLinker):
assert r not in r_vals
# print >> sys.stderr, i, "DEBUGMODE storing reference output %x" % id(storage_map[r][0])
r_vals[r] = storage_map[r][0]
storage_map[r][0] = None #clear the storage_map of outputs for the thunk_c
# clear the storage_map of outputs for the thunk_c
storage_map[r][0] = None
if config.DebugMode.check_preallocated_output:
_logger.debug(
......@@ -1470,10 +1567,13 @@ class _Linker(gof.link.LocalLinker):
if thunk_py:
assert r in r_vals #because we put it in during the thunk_py branch
# check for stride correctness (may raise exception)
_check_strides_match(r_vals[r], storage_map[r][0],
self.maker.mode.require_matching_strides, node.op)
_check_strides_match(r_vals[r],
storage_map[r][0],
self.maker.mode.require_matching_strides,
node.op)
_check_inputs(node, storage_map, r_vals, dr_vals, active_order_set,
_check_inputs(node, storage_map, r_vals,
dr_vals, active_order_set,
clobber_dr_vals=clobber, perform='c',
warn_input_not_reused=config.DebugMode.warn_input_not_reused)
......@@ -1520,7 +1620,6 @@ class _Linker(gof.link.LocalLinker):
#[(id(o), numpy.asarray(storage_map[o][0])[0,0]) for o in node.outputs])
sys.stdout.flush()
# we're done with this thunk
# clear everything out of the storage_map
for r in node.inputs:
......@@ -1533,14 +1632,16 @@ class _Linker(gof.link.LocalLinker):
#But it is very slow and it is not sure it will help.
gc.collect()
_find_bad_optimizations(order, env.equivalence_tracker.reasons, r_vals)
_find_bad_optimizations(order, env.equivalence_tracker.reasons,
r_vals)
#####
# Postcondition: the input and output variables are in the storage map, nothing more
# Postcondition: the input and output variables are
# in the storage map, nothing more
#####
# Nothing should be in storage map after evaluating each the thunk (specifically the
# last one)
# Nothing should be in storage map after evaluating
# each the thunk (specifically the last one)
for r, s in storage_map.iteritems():
assert type(s) is list
assert s[0] is None
......@@ -1592,7 +1693,6 @@ class _Linker(gof.link.LocalLinker):
if not r.type.is_valid_value(None):
assert storage_map[r][0] is not None
###############
# Done debugmode function call 'f'
##############
......@@ -1617,17 +1717,20 @@ class _Linker(gof.link.LocalLinker):
assert len(env.inputs) == len(input_storage)
assert len(env.outputs) == len(output_storage)
#print 'make_all returning output', [id(z) for z in output_storage]
return f, [link.Container(input, storage, readonly=False) for input, storage in zip(env.inputs, input_storage)], \
[link.Container(output, storage, readonly=True) for output, storage in zip(env.outputs, output_storage)], \
thunks_py, order
return f, [link.Container(input, storage, readonly=False)
for input, storage in zip(env.inputs, input_storage)], \
[link.Container(output, storage, readonly=True)
for output, storage in zip(env.outputs, output_storage)], \
thunks_py, order
_NODEFAULT = ['NODEFAULT']
class _Maker(FunctionMaker): #inheritance buys a few helper functions
class _Maker(FunctionMaker): # inheritance buys a few helper functions
"""Special debugging FunctionMaker
"""
verbose = 0
"""Verbosity level of compile-time and run-time checks. (Default 0: silent)"""
"""Verbosity level of compile-time and run-time checks. (Default
0: silent)"""
def __init__(self, inputs, outputs, optimizer, mode,
accept_inplace = False,
......@@ -1641,14 +1744,17 @@ class _Maker(FunctionMaker): #inheritance buys a few helper functions
case the functions produced by FunctionMaker will return
their output value directly
:param accept_inplace: True iff it is acceptable to have inplace operations
in the graph from the inputs to the outputs
:param accept_inplace: True iff it is acceptable to have
inplace operations in the graph from the inputs to
the outputs
:note: this function sets TensorType.filter_checks_isfinite when `mode.check_isfinite` is True
:note: this function sets TensorType.filter_checks_isfinite
when `mode.check_isfinite` is True
"""
self.profile = profile
# Handle the case where inputs and/or outputs is a single Variable (not in a list)
# Handle the case where inputs and/or outputs is a single
# Variable (not in a list)
unpack_single = False
return_none = False
if outputs is None:
......@@ -1662,17 +1768,21 @@ class _Maker(FunctionMaker): #inheritance buys a few helper functions
# Wrap them in In or Out instances if needed.
inputs, outputs = map(self.wrap_in, inputs), map(self.wrap_out, outputs)
_inputs = gof.graph.inputs([o.variable for o in outputs] + [i.update for i in inputs if getattr(i, 'update', False)])
_inputs = gof.graph.inputs([o.variable for o in outputs] +
[i.update for i in inputs
if getattr(i, 'update', False)])
#TODO: REMOVE THIS CRUFT - it's complicated for SymbolicInputKits
indices = [[input] + self.expand_in(input, _inputs) for input in inputs]
expanded_inputs = reduce(list.__add__, [list(z) for x, y, z in indices], [])
expanded_inputs = reduce(list.__add__, [list(z)
for x, y, z in indices], [])
assert expanded_inputs == inputs #JB - I added this to make sure we could delete above
# make the env
for i in xrange(mode.stability_patience):
env, additional_outputs, equivalence_tracker = _optcheck_env(expanded_inputs, outputs, accept_inplace)
env, additional_outputs, equivalence_tracker = _optcheck_env(
expanded_inputs, outputs, accept_inplace)
env.equivalence_tracker = equivalence_tracker
# optimize the env
......@@ -1681,7 +1791,8 @@ class _Maker(FunctionMaker): #inheritance buys a few helper functions
theano.config.compute_test_value = "off"
optimizer(env)
theano.compile.function_module.insert_deepcopy(env, inputs, outputs+additional_outputs)
theano.compile.function_module.insert_deepcopy(env, inputs,
outputs + additional_outputs)
finally:
theano.config.compute_test_value = compute_test_value_orig
......@@ -1750,7 +1861,7 @@ class _Maker(FunctionMaker): #inheritance buys a few helper functions
self.function_builder = function_builder
self.mode = mode
def create(self, defaults = None, trustme = False):
def create(self, defaults=None, trustme=False):
"""
Create a function.
......@@ -1761,7 +1872,7 @@ class _Maker(FunctionMaker): #inheritance buys a few helper functions
trustme -> disables some exceptions, used internally
"""
if defaults is None:
defaults = [None]*len(self.inputs)
defaults = [None] * len(self.inputs)
input_storage = [] # list of independent one-element lists, will be passed to the linker
_defaults = []
......@@ -1814,10 +1925,11 @@ class _Maker(FunctionMaker): #inheritance buys a few helper functions
else:
_defaults.append((False, False, default))
elif input.update is not None:
# If the input has an update, then (logically) it is not required since
# it is just a parameter and of course we don't want to refeed the default
# back into the storage as it would defeat the point of updating it. We
# always do this policy.
# If the input has an update, then (logically) it is
# not required since it is just a parameter and of
# course we don't want to refeed the default back into
# the storage as it would defeat the point of updating
# it. We always do this policy.
if default is None:
if trustme or isinstance(__default, gof.Container):
_defaults.append((False, False, None))
......@@ -1831,19 +1943,26 @@ class _Maker(FunctionMaker): #inheritance buys a few helper functions
if trustme or isinstance(__default, gof.Container):
_defaults.append((False, False, None))
else:
# No default, so this is a required input. Nothing to feed back, initial value is None.
# No default, so this is a required
# input. Nothing to feed back, initial value
# is None.
_defaults.append((True, False, None))
else:
# Default value. It is not required, but we want to put it back into the storage
# everytime so it behaves like most programming languages' default values
# Default value. It is not required, but we want
# to put it back into the storage everytime so it
# behaves like most programming languages' default
# values
_defaults.append((False, True, default))
defaults = _defaults
# Get a function instance
_fn, _i, _o = self.linker.make_thunk(input_storage = input_storage)
fn = self.function_builder(_fn, _i, _o, self.indices, self.outputs, defaults, self.unpack_single, self.return_none, self)
_fn, _i, _o = self.linker.make_thunk(input_storage=input_storage)
fn = self.function_builder(_fn, _i, _o, self.indices,
self.outputs, defaults, self.unpack_single,
self.return_none, self)
return fn
def _pickle_DebugMode_Maker(maker):
raise NotImplementedError('DebugMode is not picklable (yet)')
copy_reg.pickle(_Maker, _pickle_DebugMode_Maker)
......@@ -1854,6 +1973,7 @@ copy_reg.pickle(_Maker, _pickle_DebugMode_Maker)
#
########################
class DebugMode(Mode):
"""Evaluation Mode that detects internal theano errors.
......@@ -1861,22 +1981,24 @@ class DebugMode(Mode):
- inconsistent c_code and perform implementations (see `BadCLinkerOutput`)
- a variable replacing another when their runtime values don't match. This is a symptom of
an incorrect optimization step, or faulty Op implementation (raises `BadOptimization`)
- a variable replacing another when their runtime values don't
match. This is a symptom of an incorrect optimization step, or
faulty Op implementation (raises `BadOptimization`)
- stochastic optimization ordering (raises `StochasticOrder`)
- incomplete `destroy_map` specification (raises `BadDestroyMap`)
- an op that returns an illegal value not matching the output Variable Type (raises
InvalidValueError)
- an op that returns an illegal value not matching the output
Variable Type (raises InvalidValueError)
Each of these exceptions inherits from the more generic `DebugModeError`.
If there are no internal errors, this mode behaves like FAST_RUN or FAST_COMPILE, but takes
a little longer and uses more memory.
If there are no internal errors, this mode behaves like FAST_RUN
or FAST_COMPILE, but takes a little longer and uses more memory.
If there are internal errors, this mode will raise an `DebugModeError` exception.
If there are internal errors, this mode will raise an
`DebugModeError` exception.
:remark: The work of debugging is implemented by the `_Maker`, `_Linker`, and
`_VariableEquivalenceTracker` classes.
......@@ -1885,7 +2007,8 @@ class DebugMode(Mode):
stability_patience = config.DebugMode.patience
"""
When checking for the stability of optimization, recompile the graph this many times.
When checking for the stability of optimization, recompile the
graph this many times.
"""
check_c_code = config.DebugMode.check_c
......@@ -1905,13 +2028,14 @@ class DebugMode(Mode):
require_matching_strides = config.DebugMode.check_strides
"""
Should we check for (and complain about) Ops whose python and C outputs are ndarrays with
different strides? (This can catch bugs, but is generally overly strict.) 0 no check, 1 warn, 2 err.
Should we check for (and complain about) Ops whose python and C
outputs are ndarrays with different strides? (This can catch bugs,
but is generally overly strict.) 0 no check, 1 warn, 2 err.
"""
# This function will be used to create a FunctionMaker in
# function_module.function
def function_maker(self, i,o,m, *args, **kwargs):
def function_maker(self, i, o, m, *args, **kwargs):
"""Return an instance of `_Maker` which handles much of the debugging work"""
assert m is self
return _Maker(i, o, self.optimizer, self, *args, **kwargs)
......@@ -1954,4 +2078,4 @@ class DebugMode(Mode):
if not (self.check_c_code or self.check_py_code):
raise ValueError('DebugMode has to check at least one of c and py code')
register_mode('DEBUG_MODE',DebugMode(optimizer='fast_run'))
register_mode('DEBUG_MODE', DebugMode(optimizer='fast_run'))
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