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提交 3578c80c authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron
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Flake8 for compile/debugmode.py

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theano/compile/debugmode.py
浏览文件 @ 3578c80c
......@@ -7,7 +7,10 @@ from __future__ import print_function
__docformat__ = "restructuredtext en"
import copy, sys, copy_reg, gc
import copy
import sys
import copy_reg
import gc
from itertools import izip
import numpy
......@@ -16,10 +19,9 @@ import theano
from theano import gof
from theano.compat import get_unbound_function, product as itertools_product
from theano.compat.six import StringIO
from theano.gof import (FunctionGraph, graph, utils, link,
from theano.gof import (graph, utils, link,
ops_with_inner_function)
from theano.gof.link import raise_with_op
from theano.gof.cc import CLinker
from theano.configparser import (config, AddConfigVar, BoolParam, IntParam,
StrParam)
from theano.compile.function_module import (
......@@ -30,37 +32,37 @@ from theano.compile.mode import Mode, register_mode
from theano.compile.ops import OutputGuard
AddConfigVar('DebugMode.patience',
"Optimize graph this many times to detect inconsistency",
IntParam(10, lambda i: i > 0),
in_c_key=False)
"Optimize graph this many times to detect inconsistency",
IntParam(10, lambda i: i > 0),
in_c_key=False)
AddConfigVar('DebugMode.check_c',
"Run C implementations where possible",
BoolParam(bool(theano.config.cxx)),
in_c_key=False)
"Run C implementations where possible",
BoolParam(bool(theano.config.cxx)),
in_c_key=False)
AddConfigVar('DebugMode.check_py',
"Run Python implementations where possible",
BoolParam(True),
in_c_key=False)
"Run Python implementations where possible",
BoolParam(True),
in_c_key=False)
AddConfigVar('DebugMode.check_finite',
"True -> complain about NaN/Inf results",
BoolParam(True),
in_c_key=False)
"True -> complain about NaN/Inf results",
BoolParam(True),
in_c_key=False)
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)),
in_c_key=False)
("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)),
in_c_key=False)
AddConfigVar('DebugMode.warn_input_not_reused',
("Generate a warning when destroy_map or view_map says that an "
"op works inplace, but the op did not reuse the input for its output."
),
BoolParam(True),
in_c_key=False)
("Generate a warning when destroy_map or view_map says that an "
"op works 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):
......@@ -74,25 +76,25 @@ def is_valid_check_preallocated_output_param(param):
return True
AddConfigVar('DebugMode.check_preallocated_output',
('Test thunks with pre-allocated memory as output storage. '
'This is a list of strings separated by ":". Valid values are: '
'"initial" (initial storage in storage map, happens with Scan),'
'"previous" (previously-returned memory), '
'"c_contiguous", "f_contiguous", '
'"strided" (positive and negative strides), '
'"wrong_size" (larger and smaller dimensions), and '
'"ALL" (all of the above).'),
StrParam('', is_valid=is_valid_check_preallocated_output_param),
in_c_key=False)
('Test thunks with pre-allocated memory as output storage. '
'This is a list of strings separated by ":". Valid values are: '
'"initial" (initial storage in storage map, happens with Scan),'
'"previous" (previously-returned memory), '
'"c_contiguous", "f_contiguous", '
'"strided" (positive and negative strides), '
'"wrong_size" (larger and smaller dimensions), and '
'"ALL" (all of the above).'),
StrParam('', is_valid=is_valid_check_preallocated_output_param),
in_c_key=False)
AddConfigVar('DebugMode.check_preallocated_output_ndim',
('When testing with "strided" preallocated output memory, '
'test all combinations of strides over that number of '
'(inner-most) dimensions. You may want to reduce that number '
'to reduce memory or time usage, but it is advised to keep a '
'minimum of 2.'),
IntParam(4, lambda i: i > 0),
in_c_key=False)
('When testing with "strided" preallocated output memory, '
'test all combinations of strides over that number of '
'(inner-most) dimensions. You may want to reduce that number '
'to reduce memory or time usage, but it is advised to keep a '
'minimum of 2.'),
IntParam(4, lambda i: i > 0),
in_c_key=False)
import logging
_logger = logging.getLogger("theano.compile.debugmode")
......@@ -148,7 +150,7 @@ class BadThunkOutput(DebugModeError):
def __init__(self, r, thunk1, val1, thunk2, val2, inputs_val=()):
"""Initialize members"""
DebugModeError.__init__(self) # to be compatible with python2.4
super(BadThunkOutput, self).__init__()
self.r = r
self.thunk1 = thunk1
self.val1 = val1
......@@ -173,12 +175,14 @@ class BadThunkOutput(DebugModeError):
print(" op :", self.offending_op(), file=sio)
print(" Outputs Type:", self.r.type, file=sio)
print(" Outputs Shape:", getattr(self.val1, 'shape', None), file=sio)
print(" Outputs Strides:", getattr(self.val1, 'strides', None), file=sio)
print(" Inputs Type :", [i.type for i in self.r.owner.inputs], file=sio)
print(" Outputs Strides:", getattr(self.val1, 'strides', None),
file=sio)
print(" Inputs Type :", [i.type for i in self.r.owner.inputs],
file=sio)
print(" Inputs Shape:", [getattr(val, 'shape', None)
for val in self.inputs_val], file=sio)
for val in self.inputs_val], file=sio)
print(" Inputs Strides:", [getattr(val, 'strides', None)
for val in self.inputs_val], file=sio)
for val in self.inputs_val], file=sio)
print(" Bad Variable:", self.r, file=sio)
print(" thunk1 :", self.thunk1, file=sio)
print(" thunk2 :", self.thunk2, file=sio)
......@@ -226,7 +230,7 @@ class BadOptimization(DebugModeError):
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
super(BadOptimization, self).__init__()
self.old_r = old_r
self.new_r = new_r
self.old_r_val = old_r_val
......@@ -244,7 +248,7 @@ class BadOptimization(DebugModeError):
sio = StringIO()
val_str_len_limit = 800
print("BadOptimization Error", super(BadOptimization,
self).__str__(), file=sio)
self).__str__(), file=sio)
print(" Variable: id", id(self.new_r), self.new_r, file=sio)
print(" Op", self.new_r.owner, file=sio)
print(" Value Type:", type(self.new_r_val), file=sio)
......@@ -287,25 +291,21 @@ class BadOptimization(DebugModeError):
ov = numpy.asarray(self.old_r_val)
nv = numpy.asarray(self.new_r_val)
ssio = StringIO()
print(" Max Abs Diff: ", numpy.max(numpy.absolute(nv -
ov)), file=ssio)
print(" Mean Abs Diff: ", numpy.mean(numpy.absolute(nv -
ov)), file=ssio)
print(" Median Abs Diff: ", numpy.median(numpy.absolute(
nv - ov)), file=ssio)
print(" Std Abs Diff: ", numpy.std(numpy.absolute(
nv - ov)), file=ssio)
arg_max_val = numpy.argmax(numpy.absolute(nv - ov))
abs_diff = numpy.absolute(nv - ov)
print(" Max Abs Diff: ", numpy.max(abs_diff), file=ssio)
print(" Mean Abs Diff: ", numpy.mean(abs_diff), file=ssio)
print(" Median Abs Diff: ", numpy.median(abs_diff), file=ssio)
print(" Std Abs Diff: ", numpy.std(abs_diff), file=ssio)
arg_max_val = numpy.argmax(abs_diff)
values_at_max = (nv.flatten()[arg_max_val],
ov.flatten()[arg_max_val])
print(" Value at Max Diff: ", values_at_max, file=ssio)
# N.B. the maximum(..., 1e-8) protects against div by 0 when
# nv == ov == 0
reldiff = (numpy.absolute(nv - ov)
/ numpy.maximum(
numpy.absolute(nv) + numpy.absolute(ov),
1e-8))
reldiff = (abs_diff
/ numpy.maximum(numpy.absolute(nv) + numpy.absolute(ov),
1e-8))
print(" Max Rel Diff: ", numpy.max(reldiff), file=ssio)
print(" Mean Rel Diff: ", numpy.mean(reldiff), file=ssio)
print(" Median Rel Diff: ", numpy.median(reldiff), file=ssio)
......@@ -325,8 +325,10 @@ class BadOptimization(DebugModeError):
print(" New Graph:", file=sio)
print(self.new_graph, file=sio)
print("", file=sio)
print("Hint: relax the tolerance by setting tensor.cmp_sloppy=1", file=sio)
print(" or even tensor.cmp_sloppy=2 for less-strict comparison", file=sio)
print("Hint: relax the tolerance by setting tensor.cmp_sloppy=1",
file=sio)
print(" or even tensor.cmp_sloppy=2 for less-strict comparison",
file=sio)
return sio.getvalue()
......@@ -334,8 +336,7 @@ class BadDestroyMap(DebugModeError):
"""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
super(BadDestroyMap, self).__init__()
self.node = node
self.idx = idx
self.old_val = old_val
......@@ -347,30 +348,42 @@ class BadDestroyMap(DebugModeError):
print(" node:", self.node, file=sio)
print(" perform:", self.perform, file=sio)
print(" node.inputs:", [(str(i), id(i))
for i in self.node.inputs], file=sio)
for i in self.node.inputs], file=sio)
print(" destroy_map:", getattr(self.node.op,
'destroy_map', {}), file=sio)
'destroy_map', {}), file=sio)
print(" changed input idx:", self.idx, file=sio)
print(" changed input type:", self.node.inputs[self.idx].type, file=sio)
print(" changed input type:", self.node.inputs[self.idx].type,
file=sio)
print(" repr (old val):", repr(self.old_val), file=sio)
print(" repr (new val):", repr(self.new_val), file=sio)
try:
npy_old_val = numpy.asarray(self.old_val)
npy_new_val = numpy.asarray(self.new_val)
print(" value dtype (new <space> old):", npy_new_val.dtype, npy_old_val.dtype, file=sio)
print(" value shape (new <space> old):", npy_new_val.shape, npy_old_val.shape, file=sio)
print(" value min (new <space> old):", npy_new_val.min(), npy_old_val.min(), file=sio)
print(" value max (new <space> old):", npy_new_val.max(), npy_old_val.max(), file=sio)
print(" value dtype (new <space> old):", npy_new_val.dtype,
npy_old_val.dtype, file=sio)
print(" value shape (new <space> old):", npy_new_val.shape,
npy_old_val.shape, file=sio)
print(" value min (new <space> old):", npy_new_val.min(),
npy_old_val.min(), file=sio)
print(" value max (new <space> old):", npy_new_val.max(),
npy_old_val.max(), file=sio)
delta = npy_new_val - npy_old_val
print(" value min (new-old):", delta.min(), file=sio)
print(" value max (new-old):", delta.max(), file=sio)
print(" value argmin (new-old):", numpy.unravel_index(delta.argmin(), npy_new_val.shape), file=sio)
print(" value argmax (new-old):", numpy.unravel_index(delta.argmax(), npy_new_val.shape), file=sio)
print(" location of first 10 mismatches:", numpy.transpose(numpy.nonzero(delta))[:10], file=sio)
print(" value argmin (new-old):",
numpy.unravel_index(delta.argmin(), npy_new_val.shape),
file=sio)
print(" value argmax (new-old):",
numpy.unravel_index(delta.argmax(), npy_new_val.shape),
file=sio)
print(" location of first 10 mismatches:",
numpy.transpose(numpy.nonzero(delta))[:10], file=sio)
print("", file=sio)
except Exception as e:
print("(Numpy-hints failed with: %s)" % str(e), file=sio)
print(" Hint: this can also be caused by a deficient values_eq_approx() or __eq__() implementation [which compared input values]", file=sio)
print(" Hint: this can also be caused by a deficient "
"values_eq_approx() or __eq__() implementation "
"[which compared input values]", file=sio)
return sio.getvalue()
......@@ -379,8 +392,7 @@ class BadViewMap(DebugModeError):
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
super(BadViewMap, self).__init__()
self.node = node
self.output_idx = output_idx
self.out_storage = out_storage
......@@ -391,12 +403,12 @@ class BadViewMap(DebugModeError):
sio = StringIO()
print(" node:", self.node, file=sio)
print(" node.inputs:", [(str(i), id(i))
for i in self.node.inputs], file=sio)
for i in self.node.inputs], file=sio)
print(" node.outputs:", [(str(i), id(i))
for i in self.node.outputs], file=sio)
for i in self.node.outputs], file=sio)
print(" view_map:", getattr(self.node.op, 'view_map', {}), file=sio)
print(" destroy_map:", getattr(self.node.op,
'destroy_map', {}), file=sio)
'destroy_map', {}), file=sio)
print(" aliased output:", self.output_idx, file=sio)
print(" aliased output storage:", self.out_storage, file=sio)
if self.in_alias_idx:
......@@ -425,8 +437,7 @@ class InvalidValueError(DebugModeError):
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
super(InvalidValueError, self).__init__()
self.r = r
self.v = v
self.client_node = client_node
......@@ -454,7 +465,8 @@ class InvalidValueError(DebugModeError):
client_node = self.client_node
hint = self.hint
specific_hint = self.specific_hint
context = debugprint(r, prefix=' ', depth=12, file=StringIO()).getvalue()
context = debugprint(r, prefix=' ', depth=12,
file=StringIO()).getvalue()
return """InvalidValueError
type(variable) = %(type_r)s
variable = %(r)s
......@@ -512,7 +524,8 @@ def debugprint(r, prefix='', depth=-1, done=None, print_type=False,
and their associated printed ids
:param print_type: whether to print the Variable type after the other infos
:param file: file-like object to which to print
:param print_destroy_map: whether to print the op destroy_map after other info
:param print_destroy_map: whether to print the op destroy_map after
other info
:param print_view_map: whether to print the op view_map after other info
:param order: If not empty will print the index in the toposort.
:param ids: How do we print the identifier of the variable
......@@ -592,23 +605,23 @@ def debugprint(r, prefix='', depth=-1, done=None, print_type=False,
already_printed = a in done # get_id_str put it in the dict
id_str = get_id_str(a)
if profile == None or a not in profile.apply_time:
if profile is None or a not in profile.apply_time:
if len(a.outputs) == 1:
print('%s%s %s%s \'%s\' %s %s %s' % (prefix, a.op,
id_str,
type_str,
r_name,
destroy_map_str,
view_map_str,
o), file=file)
id_str,
type_str,
r_name,
destroy_map_str,
view_map_str,
o), file=file)
else:
print('%s%s.%i %s%s \'%s\' %s %s %s' % (prefix, a.op,
a.outputs.index(r),
id_str, type_str,
r_name,
destroy_map_str,
view_map_str,
o), file=file)
a.outputs.index(r),
id_str, type_str,
r_name,
destroy_map_str,
view_map_str,
o), file=file)
else:
op_time = profile.apply_time[a]
op_time_percent = (op_time / profile.fct_call_time) * 100
......@@ -617,33 +630,35 @@ def debugprint(r, prefix='', depth=-1, done=None, print_type=False,
tot_time_percent = (tot_time_dict[a] / profile.fct_call_time) * 100
if len(a.outputs) == 1:
print('%s%s %s%s \'%s\' %s %s %s --> %8.2es %4.1f%% %8.2es %4.1f%%'\
% (prefix, a.op,
id_str,
type_str,
r_name,
destroy_map_str,
view_map_str,
o, op_time,
op_time_percent,
tot_time,
tot_time_percent), file=file)
print("%s%s %s%s '%s' %s %s %s --> "
"%8.2es %4.1f%% %8.2es %4.1f%%"
% (prefix, a.op,
id_str,
type_str,
r_name,
destroy_map_str,
view_map_str,
o, op_time,
op_time_percent,
tot_time,
tot_time_percent), file=file)
else:
print('%s%s.%i %s%s \'%s\' %s %s %s --> %8.2es %4.1f%% %8.2es %4.1f%%'\
% (prefix, a.op,
a.outputs.index(r),
id_str, type_str,
r_name,
destroy_map_str,
view_map_str,
o, op_time,
op_time_percent,
tot_time,
tot_time_percent), file=file)
print("%s%s.%i %s%s '%s' %s %s %s --> "
"%8.2es %4.1f%% %8.2es %4.1f%%"
% (prefix, a.op,
a.outputs.index(r),
id_str, type_str,
r_name,
destroy_map_str,
view_map_str,
o, op_time,
op_time_percent,
tot_time,
tot_time_percent), file=file)
if not already_printed:
if (not stop_on_name or
not (hasattr(r, 'name') and r.name is not None)):
not (hasattr(r, 'name') and r.name is not None)):
new_prefix = prefix_child + ' |'
new_prefix_child = prefix_child + ' |'
......@@ -652,14 +667,15 @@ def debugprint(r, prefix='', depth=-1, done=None, print_type=False,
new_prefix_child = prefix_child + ' '
if hasattr(i, 'owner') and hasattr(i.owner, 'op'):
if isinstance(i.owner.op, theano.scan_module.scan_op.Scan):
if isinstance(i.owner.op,
theano.scan_module.scan_op.Scan):
scan_ops.append(i)
debugprint(i, new_prefix, depth=depth - 1, done=done,
print_type=print_type, file=file, order=order,
ids=ids, stop_on_name=stop_on_name,
prefix_child=new_prefix_child, scan_ops=scan_ops,
profile=profile)
prefix_child=new_prefix_child,
scan_ops=scan_ops, profile=profile)
else:
# this is an input variable
......@@ -679,7 +695,8 @@ def _optcheck_fgraph(input_specs, output_specs, accept_inplace=False):
:param accept_inplace: are inplace ops permitted in the original graph?
:type accept_inplace: Bool
:rtype: `FunctionGraph`
:returns: a new FunctionGraph with a cloned graph, with debugging `Feature` instances already installed.
:returns: a new FunctionGraph with a cloned graph, with debugging
`Feature` instances already installed.
"""
orig_inputs = [spec.variable for spec in input_specs]
updates = [spec.update for spec in input_specs if spec.update]
......@@ -687,15 +704,15 @@ def _optcheck_fgraph(input_specs, output_specs, accept_inplace=False):
equivalence_tracker = _VariableEquivalenceTracker()
fgraph = gof.fg.FunctionGraph(orig_inputs, orig_outputs,
# DestroyHandler may not be needed yet, as there is usually no
# inplace operation in the graph at this stage. DestroyHandler
# will be installed by an optimization after canonicalization,
# before the inplace operations are applied.
# This results in a big speed gain.
# If inplace operations are accepted and present, however,
# DestroyHandler will be inserted in the loop below.
# features=[equivalence_tracker, gof.DestroyHandler(do_imports_on_attach=False)])
features=[equivalence_tracker])
features=[equivalence_tracker])
# DestroyHandler may not be needed yet, as there is usually no
# inplace operation in the graph at this stage. DestroyHandler
# will be installed by an optimization after canonicalization,
# before the inplace operations are applied. This results in a big
# speed gain.
#
# If inplace operations are accepted and present, however,
# DestroyHandler will be inserted in the loop below.
if not accept_inplace:
for node in fgraph.apply_nodes:
......@@ -711,9 +728,10 @@ def _optcheck_fgraph(input_specs, output_specs, accept_inplace=False):
break
# We need to protect all immutable inputs from inplace operations.
fgraph.attach_feature(Supervisor(input for spec, input in zip(input_specs, fgraph.inputs)
if not (spec.mutable or (hasattr(fgraph, 'destroyers')
and fgraph.destroyers(input)))))
fgraph.attach_feature(Supervisor(
input for spec, input in zip(input_specs, fgraph.inputs)
if not (spec.mutable or (hasattr(fgraph, 'destroyers')
and fgraph.destroyers(input)))))
for feature in std_fgraph.features:
fgraph.attach_feature(feature())
......@@ -762,7 +780,7 @@ def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes,
out_var = storage_map[var][0]
in_var = storage_map[node.inputs[ii[0]]][0]
if (hasattr(var.type, 'may_share_memory') and
var.type.may_share_memory(out_var, in_var)):
var.type.may_share_memory(out_var, in_var)):
actually_inplace_outputs.append(node.outputs[oo])
if warn_input_not_reused and destroyed_res_list:
......@@ -772,8 +790,8 @@ def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes,
continue
if out_var is not in_var:
_logger.warning("Optimization Warning: input idx %d marked "
"as destroyed was not changed for node '%s'",
ii[0], str(node))
"as destroyed was not changed for node '%s'",
ii[0], str(node))
vmap = getattr(node.op, 'view_map', {})
for oo, ii in vmap.iteritems():
......@@ -797,8 +815,8 @@ def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes,
continue
if not may_share:
_logger.warning("Optimization Warning: input idx %d marked "
"as viewed but new memory allocated by node '%s'",
ii[0], str(node))
"as viewed but new memory allocated by node "
"'%s'", ii[0], str(node))
for r_idx, r in enumerate(node.inputs):
if not r.type.values_eq(r_vals[r], storage_map[r][0]):
......@@ -808,10 +826,12 @@ def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes,
# ok, we expected r to be destroyed
if node in active_nodes:
if dr_vals.get(r, (0, node))[1] is not node:
# bad: there should only be one active node that destroys any variable
# bad: there should only be one active node
# that destroys any variable
raise Exception('failure in topological ordering')
if clobber_dr_vals:
dr_vals[r] = (storage_map[r][0], node) # no copy, this is the last use of this variable
# no copy, this is the last use of this variable
dr_vals[r] = (storage_map[r][0], node)
# make sure that dr_vals[r] doens't get used again
storage_map[r][0] = data_destroyed
else:
......@@ -823,8 +843,10 @@ def _check_inputs(node, storage_map, r_vals, dr_vals, active_nodes,
def _check_viewmap(node, storage_map):
"""
This functions raises a BadViewMap exception when it detects the following:
- output node storages aliased to input storage, with no declaration in view_map
This functions raises a BadViewMap exception when it detects the
following:
- output node storages aliased to input storage, with no declaration
in view_map
- if not aliased to an input, check if two outputs are aliased together
and used subsequently in the graph
"""
......@@ -851,14 +873,14 @@ def _check_viewmap(node, storage_map):
# useless check.
continue
if (hasattr(inode.type, 'may_share_memory') and
inode.type.may_share_memory(outstorage, in_storage)):
inode.type.may_share_memory(outstorage, in_storage)):
nodeid = id(inode)
bad_alias[nodeid] = ii
# check that the aliasing was declared in [view|destroy]_map
if ([ii] == view_map.get(oi, None) or
[ii] == destroy_map.get(oi, None)):
[ii] == destroy_map.get(oi, None)):
good_alias[nodeid] = bad_alias.pop(nodeid)
......@@ -970,12 +992,12 @@ def _find_bad_optimizations0(order, reasons, r_vals):
check = r.type.values_eq_approx(r_val, new_r_val)
if not check:
raise BadOptimization(old_r=r,
new_r=new_r,
old_r_val=r_val,
new_r_val=new_r_val,
reason=reason,
old_graph=old_graph_str,
new_graph=new_graph_str)
new_r=new_r,
old_r_val=r_val,
new_r_val=new_r_val,
reason=reason,
old_graph=old_graph_str,
new_graph=new_graph_str)
def _find_bad_optimizations1(order, reasons, r_vals):
......@@ -993,7 +1015,7 @@ def _find_bad_optimizations1(order, reasons, r_vals):
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])))
r, set([r])))
for er in equivalence_sets[r]:
equivalence_sets[er] = equivalence_sets[new_r]
......@@ -1050,28 +1072,25 @@ def _find_bad_optimizations2(order, reasons, r_vals):
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,
new_r_val=new_r_val,
reason=reason,
old_graph=old_graph_str,
new_graph=new_graph_str)
new_r=new_r,
old_r_val=r_val,
new_r_val=new_r_val,
reason=reason,
old_graph=old_graph_str,
new_graph=new_graph_str)
def check_variable(r):
if r in checked_variables:
return
checked_variables.add(r)
# (recursively) first check all the variables that could make r look bad:
# (recursively) first check all the variables that could make
# r look bad:
list_of_vars = [old_r for (reason, old_r, olds, news) in reasons[r]]
if (None is not r.owner):
list_of_vars += r.owner.inputs
for var_that_could_make_r_look_bad in \
list_of_vars:
# backport
#[old_r for (reason, old_r, olds, news) in reasons[r]] \
#+ ([] if (None is r.owner) else r.owner.inputs):
for var_that_could_make_r_look_bad in list_of_vars:
check_variable(var_that_could_make_r_look_bad)
check_variable_norec(r)
......@@ -1087,8 +1106,8 @@ _find_bad_optimizations = _find_bad_optimizations0
def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
storage_map, r_vals, dr_vals, perform, active_order_set,
inplace_outs, init_outputs):
storage_map, r_vals, dr_vals, perform,
active_order_set, inplace_outs, init_outputs):
'''Preallocate outputs in different memory layouts'''
# To avoid circular imports
......@@ -1137,7 +1156,7 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
# inplace.
if isinstance(r.type, (TensorType, CudaNdarrayType)):
reuse_outputs[r][...] = numpy.asarray(
def_val).astype(r.type.dtype)
def_val).astype(r.type.dtype)
if reuse_outputs:
yield ('previous', reuse_outputs)
......@@ -1169,16 +1188,16 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
for r in considered_outputs:
if isinstance(r.type, (TensorType, CudaNdarrayType)):
new_buf = numpy.zeros(
shape=r_vals[r].shape,
dtype=r_vals[r].dtype,
order='F')
shape=r_vals[r].shape,
dtype=r_vals[r].dtype,
order='F')
new_buf[...] = def_val
if isinstance(r.type, CudaNdarrayType):
# When the CudaNdarray is built, the underlying memory
# is c-contiguous, so we transpose it before and after.
new_buf = CudaNdarray(new_buf.T)
new_buf = cuda_dimshuffle(new_buf,
range(new_buf.ndim)[::-1])
range(new_buf.ndim)[::-1])
f_cont_outputs[r] = new_buf
......@@ -1195,8 +1214,8 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
# Dimensions should be align by the innermost index, so we iterate
# from the end of shapes.
if ('strided' in prealloc_modes or
'wrong_size' in prealloc_modes or
'ALL' in prealloc_modes):
'wrong_size' in prealloc_modes or
'ALL' in prealloc_modes):
max_ndim = 0
rev_out_broadcastable = []
for r in considered_outputs:
......@@ -1297,11 +1316,11 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
if isinstance(r.type, (TensorType, CudaNdarrayType)):
r_shape_diff = shape_diff[:r.ndim]
out_shape = [max((s + sd), 0)
for s, sd in zip(r_vals[r].shape,
r_shape_diff)]
for s, sd in zip(r_vals[r].shape,
r_shape_diff)]
new_buf = r.type.value_zeros(out_shape)
new_buf[...] = numpy.asarray(
def_val).astype(r.type.dtype)
def_val).astype(r.type.dtype)
wrong_size[r] = new_buf
if wrong_size:
......@@ -1310,8 +1329,8 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
def _check_preallocated_output(node, thunk, prealloc_modes, def_val,
storage_map, r_vals, dr_vals, perform, active_order_set,
inplace_outs, init_outputs):
storage_map, r_vals, dr_vals, perform,
active_order_set, inplace_outs, init_outputs):
'''Try to apply thunk() on different output storages'''
# If node has an inner compiled Theano function with mode DebugMode,
......@@ -1325,7 +1344,7 @@ def _check_preallocated_output(node, thunk, prealloc_modes, def_val,
or not hasattr(fn, 'maker')
or not hasattr(fn.maker, 'mode')):
_logger.warn('Expected theano function not found in %s.%s',
node.op, fn_attr_name)
node.op, fn_attr_name)
else:
if isinstance(fn.maker.mode, DebugMode):
backup_mode = fn.maker.mode
......@@ -1378,9 +1397,10 @@ def _check_preallocated_output(node, thunk, prealloc_modes, def_val,
# Check outputs
for r in node.outputs:
if not r.type.is_valid_value(storage_map[r][0]):
raise InvalidValueError(r, storage_map[r][0],
hint=thunk_name,
specific_hint=r.type.value_validity_msg(
raise InvalidValueError(
r, storage_map[r][0],
hint=thunk_name,
specific_hint=r.type.value_validity_msg(
storage_map[r][0]))
_check_inputs(node, storage_map, r_vals, dr_vals, active_order_set,
......@@ -1393,11 +1413,14 @@ def _check_preallocated_output(node, thunk, prealloc_modes, def_val,
for r in node.outputs:
if not check_eq(r, r_vals[r], storage_map[r][0]):
# TODO: indicate it is not a C/Py problem
inputs_val = [storage_map[inp][0] for inp in r.owner.inputs]
inputs_val = [storage_map[inp][0] for inp in
r.owner.inputs]
raise BadThunkOutput(r,
thunk1='Reference value', val1=r_vals[r],
thunk2=thunk_name, val2=storage_map[r][0],
inputs_val=inputs_val)
thunk1='Reference value',
val1=r_vals[r],
thunk2=thunk_name,
val2=storage_map[r][0],
inputs_val=inputs_val)
# Clear storage_map
for r in node.outputs:
......@@ -1451,12 +1474,10 @@ class _FunctionGraphEvent(object):
msg = ''
return ' '.join(['change',
self.reason,
str(self.op),
str(self.idx),
msg])
# backport
# str(len(self.node.inputs)) if (self.op != 'output') else ''])
self.reason,
str(self.op),
str(self.idx),
msg])
else:
return str(self.__dict__)
......@@ -1475,7 +1496,8 @@ class _FunctionGraphEvent(object):
class _VariableEquivalenceTracker(object):
"""A FunctionGraph Feature that keeps tabs on an FunctionGraph and tries to detect problems."""
"""A FunctionGraph Feature that keeps tabs on an FunctionGraph and
tries to detect problems."""
fgraph = None
"""WRITEME"""
......@@ -1524,7 +1546,6 @@ class _VariableEquivalenceTracker(object):
def on_prune(self, fgraph, node, reason):
self.event_list.append(_FunctionGraphEvent('prune', node,
reason=reason))
# print 'PRUNING NODE', node, id(node)
assert node in self.active_nodes
assert node not in self.inactive_nodes
self.active_nodes.remove(node)
......@@ -1534,7 +1555,6 @@ class _VariableEquivalenceTracker(object):
self.event_list.append(_FunctionGraphEvent('import', node,
reason=reason))
# print 'NEW NODE', node, id(node)
assert node not in self.active_nodes
self.active_nodes.add(node)
......@@ -1554,9 +1574,8 @@ class _VariableEquivalenceTracker(object):
self.replaced_by.setdefault(r, [])
def on_change_input(self, fgraph, node, i, r, new_r, reason=None):
# print 'CHANGE by', reason, 'to use', new_r, type(new_r)
self.event_list.append(_FunctionGraphEvent('change', node,
reason=str(reason), idx=i))
reason=str(reason), idx=i))
self.reasons.setdefault(new_r, [])
self.replaced_by.setdefault(new_r, [])
......@@ -1570,12 +1589,13 @@ class _VariableEquivalenceTracker(object):
# N.B. compute the debugprint now, because future
# optimizations will change the graph
done = dict()
self.reasons[new_r].append((reason,
r,
debugprint(r, prefix=' ', depth=6,
file=StringIO(), done=done).getvalue(),
debugprint(new_r, prefix=' ', depth=6,
file=StringIO(), done=done).getvalue()))
self.reasons[new_r].append(
(reason,
r,
debugprint(r, prefix=' ', depth=6,
file=StringIO(), done=done).getvalue(),
debugprint(new_r, prefix=' ', depth=6,
file=StringIO(), done=done).getvalue()))
self.replaced_by[r].append((reason, new_r))
if r in self.equiv:
......@@ -1647,26 +1667,28 @@ 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):
# 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
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
fgraph = self.fgraph
input_storage_ = input_storage
output_storage_ = output_storage
#order = self.schedule(fgraph)
# Compute a topological ordering that IGNORES the destroy_map of destructive Ops.
# This will be OK, because every thunk is evaluated on a copy of its input.
order_outputs = copy.copy(fgraph.equivalence_tracker.all_variables_ever)
# Compute a topological ordering that IGNORES the destroy_map
# of destructive Ops. This will be OK, because every thunk is
# evaluated on a copy of its input.
fgraph_equiv = fgraph.equivalence_tracker
order_outputs = copy.copy(fgraph_equiv.all_variables_ever)
del fgraph_equiv
order_outputs.reverse()
order = graph.io_toposort(fgraph.inputs, order_outputs)
active_order = self.schedule(fgraph) # an ordering of just the active nodes
# an ordering of just the active nodes
active_order = self.schedule(fgraph)
active_order_set = set(active_order)
# Disable no_recycling, in order to be able to use
......@@ -1682,9 +1704,6 @@ class _Linker(gof.link.LocalLinker):
thunks_c = [] # c thunks
for node in order:
node_input_storage = [storage_map[r] for r in node.inputs]
node_output_storage = [storage_map[r] for r in node.outputs]
compute_map = {}
for k in node.inputs:
compute_map[k] = [True]
......@@ -1696,7 +1715,8 @@ class _Linker(gof.link.LocalLinker):
# the compilation of some dependency is triggered there.
thunk_other = None
if get_unbound_function(node.op.make_thunk) not in default_make_thunk:
if (get_unbound_function(node.op.make_thunk) not in
default_make_thunk):
thunk = node.op.make_thunk(node,
storage_map,
compute_map,
......@@ -1725,7 +1745,8 @@ class _Linker(gof.link.LocalLinker):
# raises an not implemented exception), so in those cases we
# consider that we don't have a python implementation
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):
(node.op.perform.func_code !=
gof.op.PureOp.perform.func_code)):
thunk = node.op.make_py_thunk(node, storage_map, compute_map,
no_recycling)
thunks_py.append(thunk)
......@@ -1739,7 +1760,9 @@ class _Linker(gof.link.LocalLinker):
elif thunks_c[-1] is None:
thunks_c[-1] = thunk_other
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_other
# Use self.no_recycling (that was passed in accept()) to always
......@@ -1747,10 +1770,11 @@ class _Linker(gof.link.LocalLinker):
# function's outputs. no_recycling_map will be used in f() below.
if self.no_recycling is True:
no_recycling_map = storage_map.values()
no_recycling_map = utils.difference(no_recycling_map, input_storage)
no_recycling_map = utils.difference(no_recycling_map,
input_storage)
else:
no_recycling_map = [storage_map[r] for r in self.no_recycling
if r not in fgraph.inputs]
if r not in fgraph.inputs]
# Precompute some things for storage pre-allocation
try:
......@@ -1769,7 +1793,7 @@ class _Linker(gof.link.LocalLinker):
#####
_logger.debug("starting a DebugMode call")
_logger.debug("self.maker.mode.check_preallocated_output: %s",
self.maker.mode.check_preallocated_output)
self.maker.mode.check_preallocated_output)
for x in no_recycling_map:
x[0] = None
......@@ -1784,14 +1808,17 @@ class _Linker(gof.link.LocalLinker):
# 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
# This dictionary is used to populate the storage_map
# as necessary
r_vals = {}
# dr_vals are the values taken by variables after being destroyed
# dr_vals are the values taken by variables after
# being destroyed
dr_vals = {}
assert len(thunks_py) == len(order)
# transfer the initial values from the storage_map to the r_vals
# transfer the initial values from the storage_map to
# the r_vals
_logger.debug("DEBUGMODE: transfer initial values")
# r_vals_initialized keeps track of the values that have
# actually been transferred from storage_map to r_vals
......@@ -1803,17 +1830,20 @@ class _Linker(gof.link.LocalLinker):
# for a Generic object). We only want to raise
# 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))
raise InvalidValueError(r, storage_map[r][0],
hint=("Graph Input '%s' has invalid value "
"%s" % (r, storage_map[r][0])))
raise InvalidValueError(
r, storage_map[r][0],
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])))
r_vals[r] = storage_map[r][0]
storage_map[r][0] = None
r_vals_initialized.append(r)
# store preallocated outputs in another map, and test the thunks on
# them as output storages.
# store preallocated outputs in another map, and test
# the thunks on them as output storages.
init_outputs = {}
for r in storage_map:
if r in fgraph.outputs:
......@@ -1835,8 +1865,6 @@ class _Linker(gof.link.LocalLinker):
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)
# put a copy of each input into the storage_map
......@@ -1844,7 +1872,6 @@ class _Linker(gof.link.LocalLinker):
for r in node.inputs:
assert isinstance(r, gof.Variable)
assert r in r_vals
# 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],
......@@ -1854,7 +1881,7 @@ class _Linker(gof.link.LocalLinker):
# storage will be None
if thunk_py:
_logger.debug("%i - running thunk_py with None as "
"output storage", i)
"output storage", i)
try:
thunk_py()
except utils.MethodNotDefined:
......@@ -1872,10 +1899,12 @@ class _Linker(gof.link.LocalLinker):
raise
opt = str(reason[0][0])
msg = (
"An optimization (probably %s ) inserted an apply node that raise an error." % opt +
"\nThe information we have about this optimizations is:" + str(reason[0][1]) +
"\n" + reason[0][2] +
"\n\nThe original exception: \n" + str(e))
"An optimization (probably %s) inserted an "
"apply node that raise an error." % opt +
"\nThe information we have about this "
"optimizations is:" + str(reason[0][1]) +
"\n" + reason[0][2] +
"\n\nThe original exception: \n" + str(e))
new_e = e.__class__(msg)
exc_type, exc_value, exc_trace = sys.exc_info()
exc_value = new_e
......@@ -1891,45 +1920,42 @@ class _Linker(gof.link.LocalLinker):
raise InvalidValueError(r, storage_map[r][0],
hint='perform output',
specific_hint=hint2)
warn_inp = config.DebugMode.warn_input_not_reused
py_inplace_outs = _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)
node, storage_map, r_vals, dr_vals,
active_order_set,
clobber_dr_vals=True, perform='py',
warn_input_not_reused=warn_inp)
_check_viewmap(node, storage_map)
# Retrieve each output from the storage_map
# The return values of this first run will be the reference ones
# Retrieve each output from the storage_map.
# The return values of this first run will be
# the reference ones
for r in node.outputs:
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]
# clear the storage_map of outputs for the thunk_c
storage_map[r][0] = None
if self.maker.mode.check_preallocated_output:
prealloc_modes = \
self.maker.mode.check_preallocated_output
self.maker.mode.check_preallocated_output
_logger.debug(
'%i - calling _check_preallocated_output '
'with thunk_py', i)
'%i - calling _check_preallocated_output '
'with thunk_py', i)
_check_preallocated_output(
node=node,
thunk=thunk_py,
prealloc_modes=prealloc_modes,
def_val=def_val,
storage_map=storage_map,
r_vals=r_vals,
dr_vals=dr_vals,
perform='py',
active_order_set=active_order_set,
inplace_outs=py_inplace_outs,
init_outputs=init_outputs)
# print >> sys.stderr, i, "DEBUGMODE thunk_py %100s %50s %30s" % (node,
#[(id(o), numpy.asarray(storage_map[o][0])[0,0]) for o in node.inputs],
#[(id(o), numpy.asarray(storage_map[o][0])[0,0]) for o in node.outputs])
node=node,
thunk=thunk_py,
prealloc_modes=prealloc_modes,
def_val=def_val,
storage_map=storage_map,
r_vals=r_vals,
dr_vals=dr_vals,
perform='py',
active_order_set=active_order_set,
inplace_outs=py_inplace_outs,
init_outputs=init_outputs)
sys.stdout.flush()
if thunk_c:
......@@ -1939,18 +1965,22 @@ class _Linker(gof.link.LocalLinker):
dmap = getattr(node.op, 'destroy_map', {})
vmap = getattr(node.op, 'view_map', {})
for i, r in enumerate(node.inputs):
# if thunk_py ran, and we still got this far,
# it means that the destroy_map of the Op (and view_map) are
# accurate
# so we can assume that inputs not marked as destroyed have in
# fact not been destroyed.
# Therefore... we only need to overwrite inputs that *have*
# been marked as destroyed.
# Inputs marked as viewd are unsafe too,
# because the corresponding output can
# be destroyed.
if any(i in v for v in (dmap.values() + vmap.values())):
storage_map[r][0] = _lessbroken_deepcopy(r_vals[r])
# if thunk_py ran, and we still got
# this far, it means that the
# destroy_map of the Op (and view_map)
# are accurate so we can assume that
# inputs not marked as destroyed have
# in fact not been destroyed.
# Therefore... we only need to
# overwrite inputs that *have* been
# marked as destroyed. Inputs marked
# as viewd are unsafe too, because the
# corresponding output can be
# destroyed.
if any(i in v for v in (dmap.values() +
vmap.values())):
storage_map[r][0] = _lessbroken_deepcopy(
r_vals[r])
clobber = False
......@@ -1969,10 +1999,12 @@ class _Linker(gof.link.LocalLinker):
raise
opt = str(reason[0][0])
msg = (
"An optimization (probably %s ) inserted an apply node that raise an error." % opt +
"\nThe information we have about this optimizations is:" + str(reason[0][1]) +
"\n" + reason[0][2] +
"\n\nThe original exception: \n" + str(e))
"An optimization (probably %s) inserted "
"an apply node that raise an error." % opt +
"\nThe information we have about this "
"optimizations is:" + str(reason[0][1]) +
"\n" + reason[0][2] +
"\n\nThe original exception: \n" + str(e))
new_e = e.__class__(msg)
exc_type, exc_value, exc_trace = sys.exc_info()
exc_value = new_e
......@@ -1982,21 +2014,26 @@ class _Linker(gof.link.LocalLinker):
for r in node.outputs:
# check output values for type-correctness
if not r.type.is_valid_value(storage_map[r][0]):
raise InvalidValueError(r, storage_map[r][0], hint='c output')
raise InvalidValueError(r, storage_map[r][0],
hint='c output')
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],
# because we put it in during the
# thunk_py branch
assert r in r_vals
# 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)
warn_inp = config.DebugMode.warn_input_not_reused
c_inplace_outs = _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)
node, storage_map, r_vals,
dr_vals, active_order_set,
clobber_dr_vals=clobber, perform='c',
warn_input_not_reused=warn_inp)
_check_viewmap(node, storage_map)
......@@ -2006,11 +2043,14 @@ class _Linker(gof.link.LocalLinker):
# compares the version from thunk_py
# (in r_vals) to the version produced
# by thunk_c (in storage_map)
if not check_eq(r, r_vals[r], storage_map[r][0]):
inputs_val = [storage_map[inp][0] for inp in r.owner.inputs]
if not check_eq(r, r_vals[r],
storage_map[r][0]):
inputs_val = [storage_map[inp][0]
for inp in r.owner.inputs]
raise BadThunkOutput(
r, thunk1='perform', val1=r_vals[r],
thunk2='c_code', val2=storage_map[r][0],
thunk2='c_code',
val2=storage_map[r][0],
inputs_val=inputs_val)
else:
# retrieve each output from the storage_map
......@@ -2020,37 +2060,34 @@ class _Linker(gof.link.LocalLinker):
if self.maker.mode.check_preallocated_output:
prealloc_modes = \
self.maker.mode.check_preallocated_output
self.maker.mode.check_preallocated_output
def thunk():
try:
thunk_c()
except Exception:
raise_with_op(node, thunk_c)
_logger.debug(
'%i - calling _check_preallocated_output '
'with thunk_c', i)
'%i - calling _check_preallocated_output '
'with thunk_c', i)
_check_preallocated_output(
node=node,
thunk=thunk,
prealloc_modes=prealloc_modes,
def_val=def_val,
storage_map=storage_map,
r_vals=r_vals,
dr_vals=dr_vals,
perform='c code',
active_order_set=active_order_set,
inplace_outs=c_inplace_outs,
init_outputs=init_outputs)
# print >> sys.stderr, i, "DEBUGMODE thunk_c %100s %50s %30s" % (node,
#[(id(o), numpy.asarray(storage_map[o][0])[0,0]) for o in node.inputs],
#[(id(o), numpy.asarray(storage_map[o][0])[0,0]) for o in node.outputs])
node=node,
thunk=thunk,
prealloc_modes=prealloc_modes,
def_val=def_val,
storage_map=storage_map,
r_vals=r_vals,
dr_vals=dr_vals,
perform='c code',
active_order_set=active_order_set,
inplace_outs=c_inplace_outs,
init_outputs=init_outputs)
sys.stdout.flush()
# we're done with this thunk
# clear everything out of the storage_map
for r in node.inputs:
#print >> sys.stderr, i, "DEBUGMODE clearing input", r
storage_map[r][0] = None
_logger.debug("%i - done with node", i)
......@@ -2059,7 +2096,8 @@ class _Linker(gof.link.LocalLinker):
# But it is very slow and it is not sure it will help.
gc.collect()
_find_bad_optimizations(order, fgraph.equivalence_tracker.reasons,
_find_bad_optimizations(order,
fgraph.equivalence_tracker.reasons,
r_vals)
#####
......@@ -2081,18 +2119,24 @@ class _Linker(gof.link.LocalLinker):
for r in r_vals:
if r.owner is None:
if r in fgraph.inputs:
assert storage_map[r] is input_storage[fgraph.inputs.index(r)]
assert (storage_map[r] is
input_storage[fgraph.inputs.index(r)])
storage_map[r][0] = r_vals[r]
# if an input was destroyed, the destroyed value should be returned
# if an input was destroyed, the destroyed value
# should be returned
for r in dr_vals:
assert dr_vals[r][0] is not None
if r.owner is None:
assert r in fgraph.inputs
# HACK TO LOOK LIKE A REAL DESTRUCTIVE ACTION TOOK PLACE
if type(dr_vals[r][0]) in (numpy.ndarray, numpy.memmap) \
and dr_vals[r][0].dtype == storage_map[r][0].dtype \
and dr_vals[r][0].shape == storage_map[r][0].shape:
# HACK TO LOOK LIKE A REAL DESTRUCTIVE ACTION
# TOOK PLACE
if ((type(dr_vals[r][0]) in
(numpy.ndarray, numpy.memmap)) and
(dr_vals[r][0].dtype ==
storage_map[r][0].dtype) and
(dr_vals[r][0].shape ==
storage_map[r][0].shape)):
if len(dr_vals[r][0].shape):
storage_map[r][0][:] = dr_vals[r][0]
else:
......@@ -2111,10 +2155,6 @@ class _Linker(gof.link.LocalLinker):
storage_map[r][0] = None
raise
# print ""
# print output_storage
# print dr_vals
# print storage_map
for r in storage_map:
if (r.owner is None):
if not r.type.is_valid_value(None):
......@@ -2130,12 +2170,14 @@ class _Linker(gof.link.LocalLinker):
# so it will screw up if we are trying to use
# multiple modes at once.
old_filter_checks_isfinite = TensorType.filter_checks_isfinite
TensorType.filter_checks_isfinite = self.maker.mode.check_isfinite
TensorType.filter_checks_isfinite = \
self.maker.mode.check_isfinite
try:
return f()
finally:
# put back the filter_checks_isfinite
TensorType.filter_checks_isfinite = old_filter_checks_isfinite
TensorType.filter_checks_isfinite = \
old_filter_checks_isfinite
return deco
f = run_with_tensortype_filter_check(f)
......@@ -2143,12 +2185,12 @@ class _Linker(gof.link.LocalLinker):
f.allow_gc = True
assert len(fgraph.inputs) == len(input_storage)
assert len(fgraph.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(fgraph.inputs, input_storage)], \
[link.Container(output, storage, readonly=True)
for output, storage in zip(fgraph.outputs, output_storage)], \
thunks_py, order
return (f,
[link.Container(input, storage, readonly=False)
for input, storage in zip(fgraph.inputs, input_storage)],
[link.Container(output, storage, readonly=True)
for output, storage in zip(fgraph.outputs, output_storage)],
thunks_py, order)
_NODEFAULT = ['NODEFAULT']
......@@ -2162,33 +2204,37 @@ class _Maker(FunctionMaker): # inheritance buys a few helper functions
0: silent)"""
def __init__(self, inputs, outputs, optimizer, mode,
accept_inplace=False,
function_builder=Function,
profile=None,
on_unused_input=None,
output_keys=None):
accept_inplace=False,
function_builder=Function,
profile=None,
on_unused_input=None,
output_keys=None):
"""
:type inputs: a list of SymbolicInput instances
:type outputs: a list of SymbolicOutput instances
outputs may also be a single Variable (not a list), in which
case the functions produced by FunctionMaker will return
their output value directly
:type outputs: a list of SymbolicOutput instances outputs may
also be a single Variable (not a list), in
which 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 on_unused_input: What to do if a variable in the 'inputs' list is
not used in the graph. Possible values are 'raise', 'warn', and 'ignore'.
:param on_unused_input: What to do if a variable in the
'inputs' list is not used in the
graph. Possible values are 'raise',
'warn', and 'ignore'.
:param output_keys: If the outputs argument for theano.function was a
list, then output_keys is None. If the outputs argument was a dict,
then output_keys is a sorted list of the keys from that dict.
:param output_keys: If the outputs argument for
theano.function was a list, then
output_keys is None. If the outputs
argument was a dict, then output_keys is a
sorted list of the keys from that dict.
:note: this function sets TensorType.filter_checks_isfinite
when `mode.check_isfinite` is True
when `mode.check_isfinite` is True
"""
self.profile = profile
# Handle the case where inputs and/or outputs is a single
......@@ -2205,7 +2251,8 @@ class _Maker(FunctionMaker): # inheritance buys a few helper functions
inputs = [inputs]
# Wrap them in In or Out instances if needed.
inputs, outputs = map(self.wrap_in, inputs), map(self.wrap_out, outputs)
inputs = map(self.wrap_in, inputs),
outputs = 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)])
......@@ -2213,9 +2260,11 @@ class _Maker(FunctionMaker): # inheritance buys a few helper functions
# Check if some input variables are unused
self._check_unused_inputs(inputs, outputs, on_unused_input)
# Make a list of (SymbolicInput|SymblicInputKits, indices, [SymbolicInput,...]), one
# tuple for each input. (See Function.indices for more details)
indices = [[input] + self.expand_in(input, _inputs) for input in inputs]
# Make a list of (SymbolicInput|SymblicInputKits, indices,
# [SymbolicInput,...]), one tuple for each input. (See
# Function.indices for more details)
indices = [[input] + self.expand_in(input, _inputs)
for input in inputs]
# make the fgraph
for i in xrange(mode.stability_patience):
......@@ -2226,58 +2275,53 @@ class _Maker(FunctionMaker): # inheritance buys a few helper functions
# optimize the fgraph
compute_test_value_orig = theano.config.compute_test_value
try:
theano.config.compute_test_value = theano.config.compute_test_value_opt
theano.config.compute_test_value = \
theano.config.compute_test_value_opt
optimizer(fgraph)
theano.compile.function_module.insert_deepcopy(fgraph, inputs,
outputs + additional_outputs)
theano.compile.function_module.insert_deepcopy(
fgraph, inputs, outputs + additional_outputs)
finally:
theano.config.compute_test_value = compute_test_value_orig
if i:
if i == 0:
fgraph0 = fgraph
else:
li = fgraph.equivalence_tracker.event_list
l0 = fgraph0.equivalence_tracker.event_list
if li != l0 :
if li != l0:
infolog = StringIO()
print("WARNING: Optimization process is unstable...", file=infolog)
print(" (HINT: Ops that the nodes point to must compare equal)", file=infolog)
print("(event index) (one event trace) (other event trace)", file=infolog)
print("-----------------------------------------------------", file=infolog)
print("WARNING: Optimization process is unstable...",
file=infolog)
print(" (HINT: Ops that the nodes point to must compare "
"equal)", file=infolog)
print("(event index) (one event trace) (other event "
"trace)", file=infolog)
print("-------------------------------------------------"
"----", file=infolog)
for j in xrange(max(len(li), len(l0))):
if j >= len(li):
print('trailing event in optimization 0 :', j, file=infolog)
print('trailing event in optimization 0 :', j,
file=infolog)
print(' ', str(l0[j]), file=infolog)
elif j >= len(l0):
print('trailing event in optimization', i, ':', j, file=infolog)
print('trailing event in optimization', i, ':',
j, file=infolog)
print(' ', str(li[j]), file=infolog)
elif li[j] != l0[j]:
print('non-equal optimization events', i, ':', j, file=infolog)
print('non-equal optimization events', i, ':',
j, file=infolog)
print(' ', str(l0[j]), file=infolog)
print(' ', str(li[j]), file=infolog)
#print >> infolog, "* ", j,
# if j < len(li):
# msg = str(li[j])
# else:
# msg = '-'
#print >> infolog, " ", msg
# if j < len(l0):
# msg = str(l0[j])
# else:
# msg = '-'
#print >> infolog, " ", msg
else:
pass
raise StochasticOrder(infolog.getvalue())
else:
if self.verbose:
print("OPTCHECK: optimization", i, \
"of", len(li), "events was stable.", file=sys.stderr)
else:
fgraph0 = fgraph
del fgraph0
print("OPTCHECK: optimization", i,
"of", len(li), "events was stable.",
file=sys.stderr)
self.fgraph = fgraph
# equivalence_tracker.printstuff()
linker = _Linker(self)
......@@ -2285,14 +2329,14 @@ class _Maker(FunctionMaker): # inheritance buys a few helper functions
# the internal storage pointer.
no_borrow = [
output
for output, spec in izip(fgraph.outputs,
outputs + additional_outputs)
if not spec.borrow]
output
for output, spec in izip(fgraph.outputs,
outputs + additional_outputs)
if not spec.borrow]
if no_borrow:
self.linker = linker.accept(
fgraph,
no_recycling=infer_reuse_pattern(fgraph, no_borrow))
fgraph,
no_recycling=infer_reuse_pattern(fgraph, no_borrow))
else:
self.linker = linker.accept(fgraph)
......@@ -2351,8 +2395,8 @@ class _Maker(FunctionMaker): # inheritance buys a few helper functions
input_storage += [default[i].storage for i in indices]
else:
raise ValueError(
'Not enough storage for SymbolicInputKit',
input, indices, default)
'Not enough storage for SymbolicInputKit',
input, indices, default)
default = _NODEFAULT
else:
input_storage += [[None] for i in indices]
......@@ -2389,8 +2433,8 @@ class _Maker(FunctionMaker): # inheritance buys a few helper functions
else:
# This might catch some bugs early
raise ValueError(
"A default (initial) value is required for an "
"input which can update itself.", input)
"A default (initial) value is required for an "
"input which can update itself.", input)
else:
_defaults.append((False, False, default))
else:
......@@ -2512,14 +2556,14 @@ class DebugMode(Mode):
return _Maker(i, o, self.optimizer, self, *args, **kwargs)
def __init__(self,
optimizer='fast_run',
stability_patience=None,
check_c_code=None,
check_py_code=None,
check_isfinite=None,
check_preallocated_output=None,
require_matching_strides=None,
linker=_DummyLinker()):
optimizer='fast_run',
stability_patience=None,
check_c_code=None,
check_py_code=None,
check_isfinite=None,
check_preallocated_output=None,
require_matching_strides=None,
linker=_DummyLinker()):
"""Initialize member variables.
If any of these arguments (except optimizer) is not None, it overrides
......@@ -2532,9 +2576,8 @@ class DebugMode(Mode):
raise Exception("DebugMode can only use its own linker! You "
"should not provide one.", linker)
super(DebugMode, self).__init__(
optimizer=optimizer,
linker=linker)
super(DebugMode, self).__init__(optimizer=optimizer,
linker=linker)
if stability_patience is not None:
self.stability_patience = stability_patience
......@@ -2561,6 +2604,6 @@ class DebugMode(Mode):
def __str__(self):
return "DebugMode(linker=%s, optimizer=%s)" % (
self.provided_linker, self.provided_optimizer)
self.provided_linker, self.provided_optimizer)
register_mode('DEBUG_MODE', DebugMode(optimizer='fast_run'))
theano/tests/test_flake8.py
浏览文件 @ 3578c80c
......@@ -38,7 +38,6 @@ whitelist_flake8 = [
"tests/test_tutorial.py",
"tests/disturb_mem.py",
"tests/unittest_tools.py",
"compile/debugmode.py",
"compile/function.py",
"compile/pfunc.py",
"compile/mode.py",
......
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