def checkpoint():
if checkpoint_asserts:
self.assert_integrity_idxs_take()
if node in self.idxs_memo:
toposort(self.idxs_memo[node])
if node in self.take_memo:
for take in self.take_memo[node]:
toposort(take)
def checkpoint():
if checkpoint_asserts:
self.assert_integrity_idxs_take()
if node in self.idxs_memo:
toposort(self.idxs_memo[node])
if node in self.take_memo:
for take in self.take_memo[node]:
toposort(take)
def __init__(self, bandit, seed=seed, cmd=None, workdir=None):
self.bandit = bandit
self.seed = seed
self.rng = np.random.RandomState(self.seed)
self.cmd = cmd
self.workdir = workdir
self.s_new_ids = pyll.Literal('new_ids') # -- list at eval-time
before = pyll.dfs(self.bandit.expr)
# -- raises exception if expr contains cycles
pyll.toposort(self.bandit.expr)
vh = self.vh = VectorizeHelper(self.bandit.expr, self.s_new_ids)
# -- raises exception if v_expr contains cycles
pyll.toposort(vh.v_expr)
idxs_by_label = vh.idxs_by_label()
vals_by_label = vh.vals_by_label()
after = pyll.dfs(self.bandit.expr)
# -- try to detect if VectorizeHelper screwed up anything inplace
assert before == after
assert set(idxs_by_label.keys()) == set(vals_by_label.keys())
assert set(idxs_by_label.keys()) == set(self.bandit.params.keys())
# -- make the graph runnable and SON-encodable
# N.B. operates inplace
self.s_idxs_vals = recursive_set_rng_kwarg(
scope.pos_args(idxs_by_label, vals_by_label),
pyll.as_apply(self.rng))
# -- raises an exception if no topological ordering exists
pyll.toposort(self.s_idxs_vals)
def __init__(self, bandit, seed=seed, cmd=None, workdir=None):
self.bandit = bandit
self.seed = seed
self.rng = np.random.RandomState(self.seed)
self.cmd = cmd
self.workdir = workdir
self.s_new_ids = pyll.Literal('new_ids') # -- list at eval-time
before = pyll.dfs(self.bandit.expr)
# -- raises exception if expr contains cycles
pyll.toposort(self.bandit.expr)
vh = self.vh = VectorizeHelper(self.bandit.expr, self.s_new_ids)
# -- raises exception if v_expr contains cycles
pyll.toposort(vh.v_expr)
idxs_by_label = vh.idxs_by_label()
vals_by_label = vh.vals_by_label()
after = pyll.dfs(self.bandit.expr)
# -- try to detect if VectorizeHelper screwed up anything inplace
assert before == after
assert set(idxs_by_label.keys()) == set(vals_by_label.keys())
assert set(idxs_by_label.keys()) == set(self.bandit.params.keys())
# -- make the graph runnable and SON-encodable
# N.B. operates inplace
self.s_idxs_vals = recursive_set_rng_kwarg(
scope.pos_args(idxs_by_label, vals_by_label),
pyll.as_apply(self.rng))
# -- raises an exception if no topological ordering exists
pyll.toposort(self.s_idxs_vals)
def space_eval(space, hp_assignment):
"""Compute a point in a search space from a hyperparameter assignment.
Parameters:
-----------
space - a pyll graph involving hp nodes (see `pyll_utils`).
hp_assignment - a dictionary mapping hp node labels to values.
"""
nodes = pyll.toposort(space)
memo = {}
for node in nodes:
if node.name == 'hyperopt_param':
label = node.arg['label'].eval()
if label in hp_assignment:
memo[node] = hp_assignment[label]
rval = pyll.rec_eval(space, memo=memo)
return rval
def space_eval(space, hp_assignment):
"""Compute a point in a search space from a hyperparameter assignment.
Parameters:
-----------
space - a pyll graph involving hp nodes (see `pyll_utils`).
hp_assignment - a dictionary mapping hp node labels to values.
"""
nodes = pyll.toposort(space)
memo = {}
for node in nodes:
if node.name == 'hyperopt_param':
label = node.arg['label'].eval()
if label in hp_assignment:
memo[node] = hp_assignment[label]
rval = pyll.rec_eval(space, memo=memo)
return rval
def __init__(self,
fn,
expr,
args=[],
workdir=None,
pass_expr_memo_ctrl=None,
**bandit_kwargs):
self.cmd = ('domain_attachment', 'FMinIter_Domain')
self.fn = fn
self.expr = expr
self.args = args
if pass_expr_memo_ctrl is None:
self.pass_expr_memo_ctrl = getattr(fn, 'fmin_pass_expr_memo_ctrl',
False)
else:
self.pass_expr_memo_ctrl = pass_expr_memo_ctrl
base.Bandit.__init__(self, expr, do_checks=False, **bandit_kwargs)
# -- This code was stolen from base.BanditAlgo, a class which may soon
# be gone
self.workdir = workdir
self.s_new_ids = pyll.Literal('new_ids') # -- list at eval-time
before = pyll.dfs(self.expr)
# -- raises exception if expr contains cycles
pyll.toposort(self.expr)
vh = self.vh = VectorizeHelper(self.expr, self.s_new_ids)
# -- raises exception if v_expr contains cycles
pyll.toposort(vh.v_expr)
idxs_by_label = vh.idxs_by_label()
vals_by_label = vh.vals_by_label()
after = pyll.dfs(self.expr)
# -- try to detect if VectorizeHelper screwed up anything inplace
assert before == after
assert set(idxs_by_label.keys()) == set(vals_by_label.keys())
assert set(idxs_by_label.keys()) == set(self.params.keys())
# -- make the graph runnable and SON-encodable
# N.B. operates inplace
self.s_idxs_vals = recursive_set_rng_kwarg(
pyll.scope.pos_args(idxs_by_label, vals_by_label),
pyll.as_apply(self.rng))
# -- raises an exception if no topological ordering exists
pyll.toposort(self.s_idxs_vals)
def __init__(self, fn, expr, args=[],
workdir=None,
pass_expr_memo_ctrl=None,
**bandit_kwargs):
self.cmd = ('domain_attachment', 'FMinIter_Domain')
self.fn = fn
self.expr = expr
self.args = args
if pass_expr_memo_ctrl is None:
self.pass_expr_memo_ctrl = getattr(fn,
'fmin_pass_expr_memo_ctrl', False)
else:
self.pass_expr_memo_ctrl = pass_expr_memo_ctrl
base.Bandit.__init__(self, expr, do_checks=False, **bandit_kwargs)
# -- This code was stolen from base.BanditAlgo, a class which may soon
# be gone
self.workdir = workdir
self.s_new_ids = pyll.Literal('new_ids') # -- list at eval-time
before = pyll.dfs(self.expr)
# -- raises exception if expr contains cycles
pyll.toposort(self.expr)
vh = self.vh = VectorizeHelper(self.expr, self.s_new_ids)
# -- raises exception if v_expr contains cycles
pyll.toposort(vh.v_expr)
idxs_by_label = vh.idxs_by_label()
vals_by_label = vh.vals_by_label()
after = pyll.dfs(self.expr)
# -- try to detect if VectorizeHelper screwed up anything inplace
assert before == after
assert set(idxs_by_label.keys()) == set(vals_by_label.keys())
assert set(idxs_by_label.keys()) == set(self.params.keys())
# -- make the graph runnable and SON-encodable
# N.B. operates inplace
self.s_idxs_vals = recursive_set_rng_kwarg(
pyll.scope.pos_args(idxs_by_label, vals_by_label),
pyll.as_apply(self.rng))
# -- raises an exception if no topological ordering exists
pyll.toposort(self.s_idxs_vals)
def __init__(self, fn, expr,
workdir=None,
pass_expr_memo_ctrl=None,
name=None,
loss_target=None,
):
"""
Paramaters
----------
fn : callable
This stores the `fn` argument to `fmin`. (See `hyperopt.fmin.fmin`)
expr : hyperopt.pyll.Apply
This is the `space` argument to `fmin`. (See `hyperopt.fmin.fmin`)
workdir : string (or None)
If non-None, the current working directory will be `workdir`while
`expr` and `fn` are evaluated. (XXX Currently only respected by
jobs run via MongoWorker)
pass_expr_memo_ctrl : bool
If True, `fn` will be called like this:
`fn(self.expr, memo, ctrl)`,
where `memo` is a dictionary mapping `Apply` nodes to their
computed values, and `ctrl` is a `Ctrl` instance for communicating
with a Trials database. This lower-level calling convention is
useful if you want to call e.g. `hyperopt.pyll.rec_eval` yourself
in some customized way.
name : string (or None)
Label, used for pretty-printing.
loss_target : float (or None)
The actual or estimated minimum of `fn`.
Some optimization algorithms may behave differently if their first
objective is to find an input that achieves a certain value,
rather than the more open-ended objective of pure minimization.
XXX: Move this from Domain to be an fmin arg.
"""
self.fn = fn
if pass_expr_memo_ctrl is None:
self.pass_expr_memo_ctrl = getattr(fn,
'fmin_pass_expr_memo_ctrl',
False)
else:
self.pass_expr_memo_ctrl = pass_expr_memo_ctrl
self.expr = pyll.as_apply(expr)
self.params = {}
for node in pyll.dfs(self.expr):
if node.name == 'hyperopt_param':
label = node.arg['label'].obj
if label in self.params:
raise DuplicateLabel(label)
self.params[label] = node.arg['obj']
self.loss_target = loss_target
self.name = name
self.workdir = workdir
self.s_new_ids = pyll.Literal('new_ids') # -- list at eval-time
before = pyll.dfs(self.expr)
# -- raises exception if expr contains cycles
pyll.toposort(self.expr)
vh = self.vh = VectorizeHelper(self.expr, self.s_new_ids)
# -- raises exception if v_expr contains cycles
pyll.toposort(vh.v_expr)
idxs_by_label = vh.idxs_by_label()
vals_by_label = vh.vals_by_label()
after = pyll.dfs(self.expr)
# -- try to detect if VectorizeHelper screwed up anything inplace
assert before == after
assert set(idxs_by_label.keys()) == set(vals_by_label.keys())
assert set(idxs_by_label.keys()) == set(self.params.keys())
self.s_rng = pyll.Literal('rng-placeholder')
# -- N.B. operates inplace:
self.s_idxs_vals = recursive_set_rng_kwarg(
pyll.scope.pos_args(idxs_by_label, vals_by_label),
self.s_rng)
# -- raises an exception if no topological ordering exists
pyll.toposort(self.s_idxs_vals)
# -- Protocol for serialization.
# self.cmd indicates to e.g. MongoWorker how this domain
# should be [un]serialized.
# XXX This mechanism deserves review as support for ipython
# workers improves.
self.cmd = ('domain_attachment', 'FMinIter_Domain')
def __init__(
self,
fn,
expr,
workdir=None,
pass_expr_memo_ctrl=None,
name=None,
loss_target=None,
):
"""
Paramaters
----------
fn : callable
This stores the `fn` argument to `fmin`. (See `hyperopt.fmin.fmin`)
expr : hyperopt.pyll.Apply
This is the `space` argument to `fmin`. (See `hyperopt.fmin.fmin`)
workdir : string (or None)
If non-None, the current working directory will be `workdir`while
`expr` and `fn` are evaluated. (XXX Currently only respected by
jobs run via MongoWorker)
pass_expr_memo_ctrl : bool
If True, `fn` will be called like this:
`fn(self.expr, memo, ctrl)`,
where `memo` is a dictionary mapping `Apply` nodes to their
computed values, and `ctrl` is a `Ctrl` instance for communicating
with a Trials database. This lower-level calling convention is
useful if you want to call e.g. `hyperopt.pyll.rec_eval` yourself
in some customized way.
name : string (or None)
Label, used for pretty-printing.
loss_target : float (or None)
The actual or estimated minimum of `fn`.
Some optimization algorithms may behave differently if their first
objective is to find an input that achieves a certain value,
rather than the more open-ended objective of pure minimization.
XXX: Move this from Domain to be an fmin arg.
"""
self.fn = fn
if pass_expr_memo_ctrl is None:
self.pass_expr_memo_ctrl = getattr(fn, 'fmin_pass_expr_memo_ctrl',
False)
else:
self.pass_expr_memo_ctrl = pass_expr_memo_ctrl
self.expr = pyll.as_apply(expr)
self.params = {}
for node in pyll.dfs(self.expr):
if node.name == 'hyperopt_param':
label = node.arg['label'].obj
if label in self.params:
raise DuplicateLabel(label)
self.params[label] = node.arg['obj']
self.loss_target = loss_target
self.name = name
self.workdir = workdir
self.s_new_ids = pyll.Literal('new_ids') # -- list at eval-time
before = pyll.dfs(self.expr)
# -- raises exception if expr contains cycles
pyll.toposort(self.expr)
vh = self.vh = VectorizeHelper(self.expr, self.s_new_ids)
# -- raises exception if v_expr contains cycles
pyll.toposort(vh.v_expr)
idxs_by_label = vh.idxs_by_label()
vals_by_label = vh.vals_by_label()
after = pyll.dfs(self.expr)
# -- try to detect if VectorizeHelper screwed up anything inplace
assert before == after
assert set(idxs_by_label.keys()) == set(vals_by_label.keys())
assert set(idxs_by_label.keys()) == set(self.params.keys())
self.s_rng = pyll.Literal('rng-placeholder')
# -- N.B. operates inplace:
self.s_idxs_vals = recursive_set_rng_kwarg(
pyll.scope.pos_args(idxs_by_label, vals_by_label), self.s_rng)
# -- raises an exception if no topological ordering exists
pyll.toposort(self.s_idxs_vals)
# -- Protocol for serialization.
# self.cmd indicates to e.g. MongoWorker how this domain
# should be [un]serialized.
# XXX This mechanism deserves review as support for ipython
# workers improves.
self.cmd = ('domain_attachment', 'FMinIter_Domain')
def build_idxs_vals(self, node, wanted_idxs):
"""
This recursive procedure should be called on an output-node.
"""
checkpoint_asserts = False
def checkpoint():
if checkpoint_asserts:
self.assert_integrity_idxs_take()
if node in self.idxs_memo:
toposort(self.idxs_memo[node])
if node in self.take_memo:
for take in self.take_memo[node]:
toposort(take)
checkpoint()
# wanted_idxs are fixed, whereas idxs_memo
# is full of unions, that can grow in subsequent recursive
# calls to build_idxs_vals with node as argument.
assert wanted_idxs != self.idxs_memo.get(node)
# -- easy exit case
if node.name == 'hyperopt_param':
# -- ignore, not vectorizing
return self.build_idxs_vals(node.arg['obj'], wanted_idxs)
# -- easy exit case
elif node.name == 'hyperopt_result':
# -- ignore, not vectorizing
return self.build_idxs_vals(node.arg['obj'], wanted_idxs)
# -- literal case: always take from universal set
elif node.name == 'literal':
if node in self.idxs_memo:
all_idxs, all_vals = self.take_memo[node][0].pos_args[:2]
wanted_vals = scope.idxs_take(all_idxs, all_vals, wanted_idxs)
self.take_memo[node].append(wanted_vals)
checkpoint()
else:
# -- initialize idxs_memo to full set
all_idxs = self.expr_idxs
n_times = scope.len(all_idxs)
# -- put array_union into graph for consistency, though it is
# not necessary
all_idxs = scope.array_union(all_idxs)
self.idxs_memo[node] = all_idxs
all_vals = scope.asarray(scope.repeat(n_times, node))
wanted_vals = scope.idxs_take(all_idxs, all_vals, wanted_idxs)
assert node not in self.take_memo
self.take_memo[node] = [wanted_vals]
checkpoint()
return wanted_vals
# -- switch case: complicated
elif node.name == 'switch':
if (node in self.idxs_memo
and wanted_idxs in self.idxs_memo[node].pos_args):
# -- phew, easy case
all_idxs, all_vals = self.take_memo[node][0].pos_args[:2]
wanted_vals = scope.idxs_take(all_idxs, all_vals, wanted_idxs)
self.take_memo[node].append(wanted_vals)
checkpoint()
else:
# -- we need to add some indexes
if node in self.idxs_memo:
all_idxs = self.idxs_memo[node]
assert all_idxs.name == 'array_union'
all_idxs.pos_args.append(wanted_idxs)
else:
all_idxs = scope.array_union(wanted_idxs)
choice = node.pos_args[0]
all_choices = self.build_idxs_vals(choice, all_idxs)
options = node.pos_args[1:]
args_idxs = scope.vchoice_split(all_idxs, all_choices,
len(options))
all_vals = scope.vchoice_merge(all_idxs, all_choices)
for opt_ii, idxs_ii in zip(options, args_idxs):
all_vals.pos_args.append(
as_apply([
idxs_ii,
self.build_idxs_vals(opt_ii, idxs_ii),
]))
wanted_vals = scope.idxs_take(
all_idxs, # -- may grow in future
all_vals, # -- may be replaced in future
wanted_idxs) # -- fixed.
if node in self.idxs_memo:
assert self.idxs_memo[node].name == 'array_union'
self.idxs_memo[node].pos_args.append(wanted_idxs)
for take in self.take_memo[node]:
assert take.name == 'idxs_take'
take.pos_args[1] = all_vals
self.take_memo[node].append(wanted_vals)
else:
self.idxs_memo[node] = all_idxs
self.take_memo[node] = [wanted_vals]
checkpoint()
# -- general case
else:
# -- this is a general node.
# It is generally handled with idxs_memo,
# but vectorize_stochastic may immediately transform it into
# a more compact form.
if (node in self.idxs_memo
and wanted_idxs in self.idxs_memo[node].pos_args):
# -- phew, easy case
for take in self.take_memo[node]:
if take.pos_args[2] == wanted_idxs:
return take
raise NotImplementedError('how did this happen?')
#all_idxs, all_vals = self.take_memo[node][0].pos_args[:2]
#wanted_vals = scope.idxs_take(all_idxs, all_vals, wanted_idxs)
#self.take_memo[node].append(wanted_vals)
#checkpoint()
else:
# XXX
# -- determine if wanted_idxs is actually a subset of the idxs
# that we are already computing. This is not only an
# optimization, but prevents the creation of cycles, which
# would otherwise occur if we have a graph of the form
# switch(f(a), g(a), 0). If there are other switches inside f
# and g, does this get trickier?
# -- assume we need to add some indexes
checkpoint()
if node in self.idxs_memo:
all_idxs = self.idxs_memo[node]
else:
all_idxs = scope.array_union(wanted_idxs)
checkpoint()
all_vals = scope.idxs_map(all_idxs, node.name)
for ii, aa in enumerate(node.pos_args):
all_vals.pos_args.append(as_apply([
all_idxs, self.build_idxs_vals(aa, all_idxs)]))
checkpoint()
for ii, (nn, aa) in enumerate(node.named_args):
all_vals.named_args.append([nn, as_apply([
all_idxs, self.build_idxs_vals(aa, all_idxs)])])
checkpoint()
all_vals = vectorize_stochastic(all_vals)
checkpoint()
wanted_vals = scope.idxs_take(
all_idxs, # -- may grow in future
all_vals, # -- may be replaced in future
wanted_idxs) # -- fixed.
if node in self.idxs_memo:
assert self.idxs_memo[node].name == 'array_union'
self.idxs_memo[node].pos_args.append(wanted_idxs)
toposort(self.idxs_memo[node])
# -- this catches the cycle bug mentioned above
for take in self.take_memo[node]:
assert take.name == 'idxs_take'
take.pos_args[1] = all_vals
self.take_memo[node].append(wanted_vals)
else:
self.idxs_memo[node] = all_idxs
self.take_memo[node] = [wanted_vals]
checkpoint()
return wanted_vals
def build_idxs_vals(self, node, wanted_idxs):
"""
This recursive procedure should be called on an output-node.
"""
checkpoint_asserts = False
def checkpoint():
if checkpoint_asserts:
self.assert_integrity_idxs_take()
if node in self.idxs_memo:
toposort(self.idxs_memo[node])
if node in self.take_memo:
for take in self.take_memo[node]:
toposort(take)
checkpoint()
# wanted_idxs are fixed, whereas idxs_memo
# is full of unions, that can grow in subsequent recursive
# calls to build_idxs_vals with node as argument.
assert wanted_idxs != self.idxs_memo.get(node)
# -- easy exit case
if node.name == 'hyperopt_param':
# -- ignore, not vectorizing
return self.build_idxs_vals(node.arg['obj'], wanted_idxs)
# -- easy exit case
elif node.name == 'hyperopt_result':
# -- ignore, not vectorizing
return self.build_idxs_vals(node.arg['obj'], wanted_idxs)
# -- literal case: always take from universal set
elif node.name == 'literal':
if node in self.idxs_memo:
all_idxs, all_vals = self.take_memo[node][0].pos_args[:2]
wanted_vals = scope.idxs_take(all_idxs, all_vals, wanted_idxs)
self.take_memo[node].append(wanted_vals)
checkpoint()
else:
# -- initialize idxs_memo to full set
all_idxs = self.expr_idxs
n_times = scope.len(all_idxs)
# -- put array_union into graph for consistency, though it is
# not necessary
all_idxs = scope.array_union(all_idxs)
self.idxs_memo[node] = all_idxs
all_vals = scope.asarray(scope.repeat(n_times, node))
wanted_vals = scope.idxs_take(all_idxs, all_vals, wanted_idxs)
assert node not in self.take_memo
self.take_memo[node] = [wanted_vals]
checkpoint()
return wanted_vals
# -- switch case: complicated
elif node.name == 'switch':
if (node in self.idxs_memo
and wanted_idxs in self.idxs_memo[node].pos_args):
# -- phew, easy case
all_idxs, all_vals = self.take_memo[node][0].pos_args[:2]
wanted_vals = scope.idxs_take(all_idxs, all_vals, wanted_idxs)
self.take_memo[node].append(wanted_vals)
checkpoint()
else:
# -- we need to add some indexes
if node in self.idxs_memo:
all_idxs = self.idxs_memo[node]
assert all_idxs.name == 'array_union'
all_idxs.pos_args.append(wanted_idxs)
else:
all_idxs = scope.array_union(wanted_idxs)
choice = node.pos_args[0]
all_choices = self.build_idxs_vals(choice, all_idxs)
options = node.pos_args[1:]
args_idxs = scope.vchoice_split(all_idxs, all_choices,
len(options))
all_vals = scope.vchoice_merge(all_idxs, all_choices)
for opt_ii, idxs_ii in zip(options, args_idxs):
all_vals.pos_args.append(
as_apply([
idxs_ii,
self.build_idxs_vals(opt_ii, idxs_ii),
]))
wanted_vals = scope.idxs_take(
all_idxs, # -- may grow in future
all_vals, # -- may be replaced in future
wanted_idxs) # -- fixed.
if node in self.idxs_memo:
assert self.idxs_memo[node].name == 'array_union'
self.idxs_memo[node].pos_args.append(wanted_idxs)
for take in self.take_memo[node]:
assert take.name == 'idxs_take'
take.pos_args[1] = all_vals
self.take_memo[node].append(wanted_vals)
else:
self.idxs_memo[node] = all_idxs
self.take_memo[node] = [wanted_vals]
checkpoint()
# -- general case
else:
# -- this is a general node.
# It is generally handled with idxs_memo,
# but vectorize_stochastic may immediately transform it into
# a more compact form.
if (node in self.idxs_memo
and wanted_idxs in self.idxs_memo[node].pos_args):
# -- phew, easy case
for take in self.take_memo[node]:
if take.pos_args[2] == wanted_idxs:
return take
raise NotImplementedError('how did this happen?')
#all_idxs, all_vals = self.take_memo[node][0].pos_args[:2]
#wanted_vals = scope.idxs_take(all_idxs, all_vals, wanted_idxs)
#self.take_memo[node].append(wanted_vals)
#checkpoint()
else:
# XXX
# -- determine if wanted_idxs is actually a subset of the idxs
# that we are already computing. This is not only an
# optimization, but prevents the creation of cycles, which
# would otherwise occur if we have a graph of the form
# switch(f(a), g(a), 0). If there are other switches inside f
# and g, does this get trickier?
# -- assume we need to add some indexes
checkpoint()
if node in self.idxs_memo:
all_idxs = self.idxs_memo[node]
else:
all_idxs = scope.array_union(wanted_idxs)
checkpoint()
all_vals = scope.idxs_map(all_idxs, node.name)
for ii, aa in enumerate(node.pos_args):
all_vals.pos_args.append(
as_apply(
[all_idxs,
self.build_idxs_vals(aa, all_idxs)]))
checkpoint()
for ii, (nn, aa) in enumerate(node.named_args):
all_vals.named_args.append([
nn,
as_apply(
[all_idxs,
self.build_idxs_vals(aa, all_idxs)])
])
checkpoint()
all_vals = vectorize_stochastic(all_vals)
checkpoint()
wanted_vals = scope.idxs_take(
all_idxs, # -- may grow in future
all_vals, # -- may be replaced in future
wanted_idxs) # -- fixed.
if node in self.idxs_memo:
assert self.idxs_memo[node].name == 'array_union'
self.idxs_memo[node].pos_args.append(wanted_idxs)
toposort(self.idxs_memo[node])
# -- this catches the cycle bug mentioned above
for take in self.take_memo[node]:
assert take.name == 'idxs_take'
take.pos_args[1] = all_vals
self.take_memo[node].append(wanted_vals)
else:
self.idxs_memo[node] = all_idxs
self.take_memo[node] = [wanted_vals]
checkpoint()
return wanted_vals
