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enumeration.py
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enumeration.py
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from dreamcoder.likelihoodModel import AllOrNothingLikelihoodModel
from dreamcoder.grammar import *
from dreamcoder.utilities import get_root_dir, limit_virtual_memory_fn
import os
import traceback
import subprocess
def multicoreEnumeration(g, tasks, _=None,
enumerationTimeout=None,
solver='ocaml',
CPUs=1,
maximumFrontier=None,
verbose=True,
evaluationTimeout=None,
testing=False,
unigramGrammar=None,
max_mem_per_enumeration_thread=1000000):
'''g: Either a Grammar, or a map from task to grammar.
Returns (list-of-frontiers, map-from-task-to-search-time)'''
# We don't use actual threads but instead use the multiprocessing
# library. This is because we need to be able to kill workers.
#from multiprocess import Process, Queue
print(f"Beginning enumeration with a total of {CPUs} CPUs.")
from multiprocessing import Queue
# everything that gets sent between processes will be dilled
import dill
solvers = {"ocaml": solveForTask_ocaml,
"pypy": solveForTask_pypy,
"python": solveForTask_python}
assert solver in solvers, "You must specify a valid solver. options are ocaml, pypy, or python."
likelihoodModel = None
if solver == 'pypy' or solver == 'python':
# Use an all or nothing likelihood model.
likelihoodModel = AllOrNothingLikelihoodModel(timeout=evaluationTimeout)
solver = solvers[solver]
if not isinstance(g, dict):
g = {t: g for t in tasks}
task2grammar = g
# If we are not evaluating on held out testing tasks:
# Bin the tasks by request type and grammar
# If these are the same then we can enumerate for multiple tasks simultaneously
# If we are evaluating testing tasks:
# Make sure that each job corresponds to exactly one task
jobs = {}
for i, t in enumerate(tasks):
if testing:
k = (task2grammar[t], t.request, i)
else:
k = (task2grammar[t], t.request)
jobs[k] = jobs.get(k, []) + [t]
disableParallelism = len(jobs) == 1
parallelCallback = launchParallelProcess if not disableParallelism else lambda f, * \
a, **k: f(*a, **k)
if disableParallelism:
eprint("Disabling parallelism on the Python side because we only have one job.")
eprint("If you are using ocaml, there could still be parallelism.")
# Map from task to the shortest time to find a program solving it
bestSearchTime = {t: None for t in task2grammar}
lowerBounds = {k: 0. for k in jobs}
frontiers = {t: Frontier([], task=t) for t in task2grammar}
# For each job we keep track of how long we have been working on it
stopwatches = {t: Stopwatch() for t in jobs}
# Map from task to how many programs we enumerated for that task
taskToNumberOfPrograms = {t: 0 for t in tasks }
def numberOfHits(f):
return sum(e.logLikelihood > -0.01 for e in f)
def budgetIncrement(lb):
if True:
return 1.5
# Very heuristic - not sure what to do here
if lb < 24.:
return 1.
elif lb < 27.:
return 0.5
else:
return 0.25
def maximumFrontiers(j):
tasks = jobs[j]
return {t: maximumFrontier - numberOfHits(frontiers[t]) for t in tasks}
def allocateCPUs(n, tasks):
allocation = {t: 0 for t in tasks}
while n > 0:
for t in tasks:
# During testing we use exactly one CPU per task
if testing and allocation[t] > 0:
return allocation
allocation[t] += 1
n -= 1
if n == 0:
break
return allocation
def refreshJobs():
for k in list(jobs.keys()):
v = [t for t in jobs[k]
if numberOfHits(frontiers[t]) < maximumFrontier
and stopwatches[k].elapsed <= enumerationTimeout]
if v:
jobs[k] = v
else:
del jobs[k]
# Workers put their messages in here
q = Queue()
# How many CPUs are we using?
activeCPUs = 0
# How many CPUs was each job allocated?
id2CPUs = {}
# What job was each ID working on?
id2job = {}
nextID = 0
while True:
refreshJobs()
# Don't launch a job that we are already working on
# We run the stopwatch whenever the job is being worked on
# freeJobs are things that we are not working on but could be
freeJobs = [j for j in jobs if not stopwatches[j].running
and stopwatches[j].elapsed < enumerationTimeout - 0.5]
if freeJobs and activeCPUs < CPUs:
# Allocate a CPU to each of the jobs that we have made the least
# progress on
freeJobs.sort(key=lambda j: lowerBounds[j])
# Launch some more jobs until all of the CPUs are being used
availableCPUs = CPUs - activeCPUs
allocation = allocateCPUs(availableCPUs, freeJobs)
for j in freeJobs:
if allocation[j] == 0:
continue
g, request = j[:2]
bi = budgetIncrement(lowerBounds[j])
thisTimeout = enumerationTimeout - stopwatches[j].elapsed
eprint("(python) Launching %s (%d tasks) w/ %d CPUs. %f <= MDL < %f. Timeout %f." %
(request, len(jobs[j]), allocation[j], lowerBounds[j], lowerBounds[j] + bi, thisTimeout))
stopwatches[j].start()
parallelCallback(wrapInThread(solver),
q=q, g=g, ID=nextID,
elapsedTime=stopwatches[j].elapsed,
CPUs=allocation[j],
tasks=jobs[j],
lowerBound=lowerBounds[j],
upperBound=lowerBounds[j] + bi,
budgetIncrement=bi,
timeout=thisTimeout,
evaluationTimeout=evaluationTimeout,
maximumFrontiers=maximumFrontiers(j),
testing=testing,
likelihoodModel=likelihoodModel,
unigramGrammar=unigramGrammar,
max_mem_per_enumeration_thread=max_mem_per_enumeration_thread)
id2CPUs[nextID] = allocation[j]
id2job[nextID] = j
nextID += 1
activeCPUs += allocation[j]
lowerBounds[j] += bi
# If nothing is running, and we just tried to launch jobs,
# then that means we are finished
if all(not s.running for s in stopwatches.values()):
break
# Wait to get a response
message = Bunch(dill.loads(q.get()))
if message.result == "failure":
eprint("PANIC! Exception in child worker:", message.exception)
eprint(message.stacktrace)
assert False
elif message.result == "success":
# Mark the CPUs is no longer being used and pause the stopwatch
activeCPUs -= id2CPUs[message.ID]
stopwatches[id2job[message.ID]].stop()
newFrontiers, searchTimes, pc = message.value
for t, f in newFrontiers.items():
oldBest = None if len(
frontiers[t]) == 0 else frontiers[t].bestPosterior
frontiers[t] = frontiers[t].combine(f)
newBest = None if len(
frontiers[t]) == 0 else frontiers[t].bestPosterior
taskToNumberOfPrograms[t] += pc
dt = searchTimes[t]
if dt is not None:
if bestSearchTime[t] is None:
bestSearchTime[t] = dt
else:
# newBest & oldBest should both be defined
assert oldBest is not None
assert newBest is not None
newScore = newBest.logPrior + newBest.logLikelihood
oldScore = oldBest.logPrior + oldBest.logLikelihood
if newScore > oldScore:
bestSearchTime[t] = dt
elif newScore == oldScore:
bestSearchTime[t] = min(bestSearchTime[t], dt)
else:
eprint("Unknown message result:", message.result)
assert False
eprint("We enumerated this many programs, for each task:\n\t",
list(taskToNumberOfPrograms.values()))
return [frontiers[t] for t in tasks], bestSearchTime
def wrapInThread(f):
"""
Returns a function that is designed to be run in a thread/threadlike process.
Result will be either put into the q
"""
import dill
def _f(*a, **k):
q = k.pop("q")
ID = k.pop("ID")
try:
r = f(*a, **k)
q.put(dill.dumps({"result": "success",
"ID": ID,
"value": r}))
except Exception as e:
q.put(dill.dumps({"result": "failure",
"exception": e,
"stacktrace": traceback.format_exc(),
"ID": ID}))
return
return _f
OCAML_TEST_FLAG = "is_ocaml_test" # Indicates a JSON response intended for testing.
def solveForTask_ocaml(_=None,
elapsedTime=0.,
CPUs=1,
g=None, tasks=None,
lowerBound=None, upperBound=None, budgetIncrement=None,
timeout=None,
testing=None, # FIXME: unused
likelihoodModel=None,
evaluationTimeout=None, maximumFrontiers=None,
unigramGrammar=None,
verbose=False,
max_mem_per_enumeration_thread=1000000):
import json
def taskMessage(t):
serialized_examples = []
for xs, y in t.examples:
if hasattr(t, "serializeSpecialInput"):
xs = t.serializeSpecialInput(xs)
if hasattr(t, "serializeSpecialOutput"):
y = t.serializeSpecialOutput(y, is_output=True)
serialized_examples.append({"inputs": list(xs), "output": y})
m = {
"examples": serialized_examples,
"name": t.name,
"request": t.request.json(),
"maximumFrontier": maximumFrontiers[t]}
if hasattr(t, "specialTask"):
special, extra = t.specialTask
m["specialTask"] = special
m["extras"] = extra
if hasattr(t, "raw_programs_to_test"):
m["raw_programs_to_test"] = t.raw_programs_to_test
return m
message = {"DSL": g.json(),
"tasks": [taskMessage(t)
for t in tasks],
"programTimeout": evaluationTimeout,
"nc": CPUs,
"timeout": timeout,
"lowerBound": lowerBound,
"upperBound": upperBound,
"budgetIncrement": budgetIncrement,
"verbose": verbose,
"shatter": 5 if len(tasks) == 1 and "turtle" in str(tasks[0].request) else 10}
if hasattr(tasks[0], 'maxParameters') and tasks[0].maxParameters is not None:
message["maxParameters"] = tasks[0].maxParameters
message = json.dumps(message)
# uncomment this if you want to save the messages being sent to the solver
solver_file = 'solver'
if hasattr(tasks[0], 'specialSolver'):
solver_file = tasks[0].specialSolver
try:
solver_file = os.path.join(get_root_dir(), solver_file)
process = subprocess.Popen(solver_file,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
limit_virtual_memory_with_psutil_if_possible(process, max_mem_per_enumeration_thread)
response, error = process.communicate(bytes(message, encoding="utf-8"))
response = json.loads(response.decode("utf-8"))
except OSError as exc:
raise exc
except:
print("response:", response)
print("error:", error)
with open("message", "w") as f:
f.write(message)
# Don't fail on errors
# assert False, "MAX RAISE"
print("ERROR in enumeration, returning empty frontiers for this batch of tasks.")
response = {t.name : [] for t in tasks} # Empty response
def escape_tokens(tokens):
if unigramGrammar is not None:
return unigramGrammar.escape_tokens_string(tokens)
return g.escape_tokens_string(tokens)
if OCAML_TEST_FLAG in response:
return response
pc = response.get("number_enumerated",0) # TODO
frontiers = {}
searchTimes = {}
for t in tasks:
solutions = response[t.name]
frontier = Frontier([FrontierEntry(program=p,
logLikelihood=e["logLikelihood"],
tokens=escape_tokens(e["tokens"]).split(),
logPrior=g.logLikelihood(t.request, p))
for e in solutions
for p in [Program.parse(e["program"])]],
task=t)
frontiers[t] = frontier
if frontier.empty:
searchTimes[t] = None
# This is subtle:
# The search time we report is actually not be minimum time to find any solution
# Rather it is the time to find the MAP solution
# This is important for regression problems,
# where we might find something with a good prior but bad likelihood early on,
# and only later discovered the good high likelihood program
else:
searchTimes[t] = min(
(e["logLikelihood"] + e["logPrior"],
e["time"]) for e in solutions)[1] + elapsedTime
return frontiers, searchTimes, pc
def solveForTask_pypy(_=None,
elapsedTime=0.,
g=None, task=None,
lowerBound=None, upperBound=None, budgetIncrement=None,
timeout=None,
likelihoodModel=None,
evaluationTimeout=None, maximumFrontier=None, testing=False,unigramGrammar=None):
return callCompiled(enumerateForTasks,
g, tasks, likelihoodModel,
timeout=timeout,
testing=testing,
elapsedTime=elapsedTime,
evaluationTimeout=evaluationTimeout,
maximumFrontiers=maximumFrontiers,
budgetIncrement=budgetIncrement,
lowerBound=lowerBound, upperBound=upperBound,unigramGrammar=None)
def solveForTask_python(_=None,
elapsedTime=0.,
g=None, tasks=None,
lowerBound=None, upperBound=None, budgetIncrement=None,
timeout=None,
CPUs=1,
likelihoodModel=None,
evaluationTimeout=None, maximumFrontiers=None, testing=False,unigramGrammar=None):
return enumerateForTasks(g, tasks, likelihoodModel,
timeout=timeout,
testing=testing,
elapsedTime=elapsedTime,
evaluationTimeout=evaluationTimeout,
maximumFrontiers=maximumFrontiers,
budgetIncrement=budgetIncrement,
lowerBound=lowerBound, upperBound=upperBound,unigramGrammar=None)
class EnumerationTimeout(Exception):
pass
def enumerateForTasks(g, tasks, likelihoodModel, _=None,
verbose=False,
timeout=None,
elapsedTime=0.,
CPUs=1,
testing=False, #unused
evaluationTimeout=None,
lowerBound=0.,
upperBound=100.,
budgetIncrement=1.0, maximumFrontiers=None,unigramGrammar=None):
assert timeout is not None, \
"enumerateForTasks: You must provide a timeout."
from time import time
request = tasks[0].request
assert all(t.request == request for t in tasks), \
"enumerateForTasks: Expected tasks to all have the same type"
maximumFrontiers = [maximumFrontiers[t] for t in tasks]
# store all of the hits in a priority queue
# we will never maintain maximumFrontier best solutions
hits = [PQ() for _ in tasks]
starting = time()
previousBudget = lowerBound
budget = lowerBound + budgetIncrement
try:
totalNumberOfPrograms = 0
while time() < starting + timeout and \
any(len(h) < mf for h, mf in zip(hits, maximumFrontiers)) and \
budget <= upperBound:
numberOfPrograms = 0
for prior, _, p in g.enumeration(Context.EMPTY, [], request,
maximumDepth=99,
upperBound=budget,
lowerBound=previousBudget):
descriptionLength = -prior
# Shouldn't see it on this iteration
assert descriptionLength <= budget
# Should already have seen it
assert descriptionLength > previousBudget
numberOfPrograms += 1
totalNumberOfPrograms += 1
for n in range(len(tasks)):
task = tasks[n]
#Warning:changed to max's new likelihood model situation
#likelihood = task.logLikelihood(p, evaluationTimeout)
#if invalid(likelihood):
#continue
success, likelihood = likelihoodModel.score(p, task)
if not success:
continue
dt = time() - starting + elapsedTime
priority = -(likelihood + prior)
hits[n].push(priority,
(dt, FrontierEntry(program=p,
logLikelihood=likelihood,
logPrior=prior)))
if len(hits[n]) > maximumFrontiers[n]:
hits[n].popMaximum()
if timeout is not None and time() - starting > timeout:
raise EnumerationTimeout
previousBudget = budget
budget += budgetIncrement
if budget > upperBound:
break
except EnumerationTimeout:
pass
frontiers = {tasks[n]: Frontier([e for _, e in hits[n]],
task=tasks[n])
for n in range(len(tasks))}
searchTimes = {
tasks[n]: None if len(hits[n]) == 0 else \
min(t for t,_ in hits[n]) for n in range(len(tasks))}
return frontiers, searchTimes, totalNumberOfPrograms