mirror of
https://github.com/wassname/ray.git
synced 2026-06-27 22:38:16 +08:00
Edward Oakes
312 lines
11 KiB
Python
312 lines
11 KiB
Python
from collections import defaultdict
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import json
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import multiprocessing
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import numpy as np
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import pytest
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import time
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import warnings
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import ray
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from ray.cluster_utils import Cluster
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from ray.test_utils import wait_for_condition
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if (multiprocessing.cpu_count() < 40
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or ray.utils.get_system_memory() < 50 * 10**9):
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warnings.warn("This test must be run on large machines.")
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def create_cluster(num_nodes):
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cluster = Cluster()
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for i in range(num_nodes):
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cluster.add_node(resources={str(i): 100}, object_store_memory=10**9)
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ray.init(address=cluster.address)
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return cluster
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@pytest.fixture()
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def ray_start_cluster_with_resource():
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num_nodes = 5
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cluster = create_cluster(num_nodes)
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yield cluster, num_nodes
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# The code after the yield will run as teardown code.
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ray.shutdown()
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cluster.shutdown()
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# This test is here to make sure that when we broadcast an object to a bunch of
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# machines, we don't have too many excess object transfers.
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@pytest.mark.skip(reason="TODO(ekl)")
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def test_object_broadcast(ray_start_cluster_with_resource):
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cluster, num_nodes = ray_start_cluster_with_resource
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@ray.remote
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def f(x):
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return
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x = np.zeros(1024 * 1024, dtype=np.uint8)
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@ray.remote
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def create_object():
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return np.zeros(1024 * 1024, dtype=np.uint8)
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object_refs = []
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for _ in range(3):
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# Broadcast an object to all machines.
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x_id = ray.put(x)
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object_refs.append(x_id)
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ray.get([
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f._remote(args=[x_id], resources={str(i % num_nodes): 1})
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for i in range(10 * num_nodes)
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])
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for _ in range(3):
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# Broadcast an object to all machines.
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x_id = create_object.remote()
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object_refs.append(x_id)
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ray.get([
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f._remote(args=[x_id], resources={str(i % num_nodes): 1})
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for i in range(10 * num_nodes)
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])
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# Wait for profiling information to be pushed to the profile table.
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time.sleep(1)
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transfer_events = ray.object_transfer_timeline()
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# Make sure that each object was transferred a reasonable number of times.
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for x_id in object_refs:
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relevant_events = [
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event for event in transfer_events
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if event["cat"] == "transfer_send"
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and event["args"][0] == x_id.hex() and event["args"][2] == 1
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]
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# NOTE: Each event currently appears twice because we duplicate the
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# send and receive boxes to underline them with a box (black if it is a
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# send and gray if it is a receive). So we need to remove these extra
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# boxes here.
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deduplicated_relevant_events = [
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event for event in relevant_events if event["cname"] != "black"
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]
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assert len(deduplicated_relevant_events) * 2 == len(relevant_events)
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relevant_events = deduplicated_relevant_events
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# Each object must have been broadcast to each remote machine.
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assert len(relevant_events) >= num_nodes - 1
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# If more object transfers than necessary have been done, print a
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# warning.
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if len(relevant_events) > num_nodes - 1:
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warnings.warn("This object was transferred {} times, when only {} "
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"transfers were required.".format(
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len(relevant_events), num_nodes - 1))
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# Each object should not have been broadcast more than once from every
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# machine to every other machine. Also, a pair of machines should not
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# both have sent the object to each other.
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assert len(relevant_events) <= (num_nodes - 1) * num_nodes / 2
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# Make sure that no object was sent multiple times between the same
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# pair of object managers.
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send_counts = defaultdict(int)
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for event in relevant_events:
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# The pid identifies the sender and the tid identifies the
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# receiver.
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send_counts[(event["pid"], event["tid"])] += 1
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assert all(value == 1 for value in send_counts.values())
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# When submitting an actor method, we try to pre-emptively push its arguments
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# to the actor's object manager. However, in the past we did not deduplicate
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# the pushes and so the same object could get shipped to the same object
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# manager many times. This test checks that that isn't happening.
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def test_actor_broadcast(ray_start_cluster_with_resource):
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cluster, num_nodes = ray_start_cluster_with_resource
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@ray.remote
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class Actor:
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def ready(self):
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pass
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def set_weights(self, x):
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pass
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actors = [
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Actor._remote(
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args=[],
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kwargs={},
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num_cpus=0.01,
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resources={str(i % num_nodes): 1}) for i in range(30)
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]
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# Wait for the actors to start up.
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ray.get([a.ready.remote() for a in actors])
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object_refs = []
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# Broadcast a large object to all actors.
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for _ in range(5):
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x_id = ray.put(np.zeros(1024 * 1024, dtype=np.uint8))
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object_refs.append(x_id)
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# Pass the object into a method for every actor.
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ray.get([a.set_weights.remote(x_id) for a in actors])
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# Wait for profiling information to be pushed to the profile table.
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time.sleep(1)
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transfer_events = ray.object_transfer_timeline()
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# Make sure that each object was transferred a reasonable number of times.
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for x_id in object_refs:
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relevant_events = [
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event for event in transfer_events
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if event["cat"] == "transfer_send"
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and event["args"][0] == x_id.hex() and event["args"][2] == 1
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]
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# NOTE: Each event currently appears twice because we duplicate the
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# send and receive boxes to underline them with a box (black if it is a
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# send and gray if it is a receive). So we need to remove these extra
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# boxes here.
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deduplicated_relevant_events = [
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event for event in relevant_events if event["cname"] != "black"
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]
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assert len(deduplicated_relevant_events) * 2 == len(relevant_events)
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relevant_events = deduplicated_relevant_events
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# Each object must have been broadcast to each remote machine.
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assert len(relevant_events) >= num_nodes - 1
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# If more object transfers than necessary have been done, print a
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# warning.
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if len(relevant_events) > num_nodes - 1:
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warnings.warn("This object was transferred {} times, when only {} "
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"transfers were required.".format(
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len(relevant_events), num_nodes - 1))
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# Each object should not have been broadcast more than once from every
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# machine to every other machine. Also, a pair of machines should not
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# both have sent the object to each other.
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assert len(relevant_events) <= (num_nodes - 1) * num_nodes / 2
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# Make sure that no object was sent multiple times between the same
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# pair of object managers.
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send_counts = defaultdict(int)
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for event in relevant_events:
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# The pid identifies the sender and the tid identifies the
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# receiver.
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send_counts[(event["pid"], event["tid"])] += 1
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assert all(value == 1 for value in send_counts.values())
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# The purpose of this test is to make sure that an object that was already been
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# transferred to a node can be transferred again.
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def test_object_transfer_retry(ray_start_cluster):
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cluster = ray_start_cluster
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repeated_push_delay = 1
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# Force the sending object manager to allow duplicate pushes again sooner.
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# Also, force the receiving object manager to retry the pull sooner. We
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# make the chunk size smaller in order to make it easier to test objects
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# with multiple chunks.
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config = json.dumps({
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"object_manager_repeated_push_delay_ms": repeated_push_delay * 1000,
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"object_manager_pull_timeout_ms": repeated_push_delay * 1000 / 4,
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"object_manager_default_chunk_size": 1000
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})
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object_store_memory = 150 * 1024 * 1024
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cluster.add_node(
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object_store_memory=object_store_memory, _internal_config=config)
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cluster.add_node(num_gpus=1, object_store_memory=object_store_memory)
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ray.init(address=cluster.address)
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@ray.remote(num_gpus=1)
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def f(size):
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return np.zeros(size, dtype=np.uint8)
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# Transfer an object to warm up the object manager.
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ray.get(f.remote(10**6))
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x_id = f.remote(10**6)
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assert not ray.worker.global_worker.core_worker.object_exists(x_id)
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# Get the objects locally to cause them to be transferred. This is the
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# first time the objects are getting transferred, so it should happen
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# quickly.
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start_time = time.time()
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ray.get(x_id)
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end_time = time.time()
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if end_time - start_time > repeated_push_delay:
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warnings.warn("The initial transfer took longer than the repeated "
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"push delay, so this test may not be testing the thing "
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"it's supposed to test.")
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def not_exists():
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return not ray.worker.global_worker.core_worker.object_exists(x_id)
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def force_eviction():
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for _ in range(20):
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ray.put(np.zeros(object_store_memory // 10, dtype=np.uint8))
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wait_for_condition(not_exists)
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# Force the object to be evicted from the local node.
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force_eviction()
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# Get the object again and make sure it gets transferred.
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ray.get(x_id)
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end_transfer_time = time.time()
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# We should have had to wait for the repeated push delay.
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assert end_transfer_time - start_time >= repeated_push_delay
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# Force the object to be evicted again and wait longer than the repeated
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# push delay and make sure that the object is transferred again.
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force_eviction()
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time.sleep(repeated_push_delay)
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# Fetch the object again. This should not wait for the delay.
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start_time = time.time()
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ray.get(x_id)
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end_time = time.time()
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assert end_time - start_time < repeated_push_delay
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# The purpose of this test is to make sure we can transfer many objects. In the
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# past, this has caused failures in which object managers create too many open
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# files and run out of resources.
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def test_many_small_transfers(ray_start_cluster_with_resource):
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cluster, num_nodes = ray_start_cluster_with_resource
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@ray.remote
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def f(*args):
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pass
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# This function creates 1000 objects on each machine and then transfers
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# each object to every other machine.
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def do_transfers():
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id_lists = []
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for i in range(num_nodes):
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id_lists.append([
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f._remote(args=[], kwargs={}, resources={str(i): 1})
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for _ in range(1000)
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])
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ids = []
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for i in range(num_nodes):
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for j in range(num_nodes):
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if i == j:
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continue
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ids.append(
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f._remote(
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args=id_lists[j], kwargs={}, resources={str(i): 1}))
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# Wait for all of the transfers to finish.
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ray.get(ids)
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do_transfers()
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do_transfers()
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do_transfers()
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do_transfers()
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if __name__ == "__main__":
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import pytest
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import sys
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sys.exit(pytest.main(["-v", __file__]))
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