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1faccd4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import ray
import torch
from verl import DataProto
from verl.single_controller.base import Worker
from verl.single_controller.base.decorator import Dispatch, register
from verl.single_controller.ray.base import (
RayClassWithInitArgs,
RayResourcePool,
RayWorkerGroup,
split_resource_pool,
)
from verl.utils.device import get_device_name, get_nccl_backend
@ray.remote
class Actor(Worker):
def __init__(self, worker_id) -> None:
super().__init__()
self.worker_id = worker_id
self.temp_tensor = torch.rand(4096, 4096).to(get_device_name())
if not torch.distributed.is_initialized():
rank = int(os.environ.get("RANK", 0))
world_size = int(os.environ.get("WORLD_SIZE", 1))
torch.distributed.init_process_group(backend=get_nccl_backend(), world_size=world_size, rank=rank)
@register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO)
def add(self, data: DataProto):
data.batch["a"] += self.rank + self.worker_id
return data
def test_split_resource_pool_with_split_size():
ray.init()
# assume we have 2 nodes, with 4 GPUs each
global_resource_pool = RayResourcePool(process_on_nodes=[4, 4])
global_resource_pool.get_placement_groups(device_name=get_device_name())
# first 4 gpus for actor_1, last 4 gpus for actor_2
actor_1_resource_pool, actor_2_resource_pool = split_resource_pool(resource_pool=global_resource_pool, split_size=4)
actor_cls_1 = RayClassWithInitArgs(cls=Actor, worker_id=0)
actor_cls_2 = RayClassWithInitArgs(cls=Actor, worker_id=100)
actor_worker_1 = RayWorkerGroup(
resource_pool=actor_1_resource_pool, ray_cls_with_init=actor_cls_1, device_name=get_device_name()
)
actor_worker_2 = RayWorkerGroup(
resource_pool=actor_2_resource_pool, ray_cls_with_init=actor_cls_2, device_name=get_device_name()
)
assert actor_worker_1.world_size == 4
assert actor_worker_2.world_size == 4
data = DataProto.from_dict({"a": torch.zeros(8)})
actor_output_1 = actor_worker_1.add(data)
actor_output_2 = actor_worker_2.add(data)
assert actor_output_1.batch["a"].tolist() == [0, 0, 1, 1, 2, 2, 3, 3]
assert actor_output_2.batch["a"].tolist() == [100, 100, 101, 101, 102, 102, 103, 103]
ray.shutdown()
def test_split_resource_pool_with_split_size_list():
ray.init()
# assume we have 4 nodes, with 2 GPUs each
global_resource_pool = RayResourcePool(process_on_nodes=[2, 2, 2, 2])
global_resource_pool.get_placement_groups(device_name=get_device_name())
# first 2 gpus for actor_1, last 6 gpus for actor_2
actor_1_resource_pool, actor_2_resource_pool = split_resource_pool(
resource_pool=global_resource_pool,
split_size=[2, 6],
)
actor_cls_1 = RayClassWithInitArgs(cls=Actor, worker_id=0)
actor_cls_2 = RayClassWithInitArgs(cls=Actor, worker_id=100)
actor_worker_1 = RayWorkerGroup(
resource_pool=actor_1_resource_pool, ray_cls_with_init=actor_cls_1, device_name=get_device_name()
)
actor_worker_2 = RayWorkerGroup(
resource_pool=actor_2_resource_pool, ray_cls_with_init=actor_cls_2, device_name=get_device_name()
)
assert actor_worker_1.world_size == 2
assert actor_worker_2.world_size == 6
data_1 = DataProto.from_dict({"a": torch.zeros(4)})
data_2 = DataProto.from_dict({"a": torch.zeros(6)})
actor_output_1 = actor_worker_1.add(data_1)
actor_output_2 = actor_worker_2.add(data_2)
print(actor_output_1.batch["a"].tolist())
print(actor_output_2.batch["a"].tolist())
assert actor_output_1.batch["a"].tolist() == [0, 0, 1, 1]
assert actor_output_2.batch["a"].tolist() == [100, 101, 102, 103, 104, 105]
ray.shutdown()
def test_split_resource_pool_with_split_size_list_cross_nodes():
ray.init()
# assume we have 4 nodes, with 2 GPUs each
global_resource_pool = RayResourcePool(process_on_nodes=[4, 4])
global_resource_pool.get_placement_groups(device_name=get_device_name())
# first 2 gpus for actor_1, last 6 gpus for actor_2
actor_1_resource_pool, actor_2_resource_pool = split_resource_pool(
resource_pool=global_resource_pool,
split_size=[2, 6],
)
actor_cls_1 = RayClassWithInitArgs(cls=Actor, worker_id=0)
actor_cls_2 = RayClassWithInitArgs(cls=Actor, worker_id=100)
actor_worker_1 = RayWorkerGroup(
resource_pool=actor_1_resource_pool, ray_cls_with_init=actor_cls_1, device_name=get_device_name()
)
actor_worker_2 = RayWorkerGroup(
resource_pool=actor_2_resource_pool, ray_cls_with_init=actor_cls_2, device_name=get_device_name()
)
assert actor_worker_1.world_size == 2
assert actor_worker_2.world_size == 6
data_1 = DataProto.from_dict({"a": torch.zeros(4)})
data_2 = DataProto.from_dict({"a": torch.zeros(6)})
actor_output_1 = actor_worker_1.add(data_1)
actor_output_2 = actor_worker_2.add(data_2)
print(actor_output_1.batch["a"].tolist())
print(actor_output_2.batch["a"].tolist())
assert actor_output_1.batch["a"].tolist() == [0, 0, 1, 1]
assert actor_output_2.batch["a"].tolist() == [100, 101, 102, 103, 104, 105]
ray.shutdown()
def test_split_resource_pool_with_split_twice():
ray.init()
# assume we have 4 nodes, with 2 GPUs each
global_resource_pool = RayResourcePool(process_on_nodes=[2, 2, 2, 2])
global_resource_pool.get_placement_groups(device_name=get_device_name())
# actors with [2, 1, 1, 1, 1, 2] (split twice)
rp_1, rp_2, rp_3 = split_resource_pool(
resource_pool=global_resource_pool,
split_size=[2, 4, 2],
)
rp_2_1, rp_2_2, rp_2_3, rp_2_4 = split_resource_pool(
resource_pool=rp_2,
split_size=1,
)
fp_list = [rp_1, rp_2_1, rp_2_2, rp_2_3, rp_2_4, rp_3]
correct_world_size = [2, 1, 1, 1, 1, 2]
correct_output = [
[0.0, 0.0, 1.0, 1.0], # 2 worker
[100.0, 100.0, 100.0, 100.0], # 1 worker
[200.0, 200.0, 200.0, 200.0], # 1 worker
[300.0, 300.0, 300.0, 300.0], # 1 worker
[400.0, 400.0, 400.0, 400.0], # 1 worker
[500.0, 500.0, 501.0, 501.0], # 2 worker
]
for idx, rp in enumerate(fp_list):
actor_cls = RayClassWithInitArgs(cls=Actor, worker_id=idx * 100)
actor_worker = RayWorkerGroup(resource_pool=rp, ray_cls_with_init=actor_cls, device_name=get_device_name())
data = DataProto.from_dict({"a": torch.zeros(4)})
actor_output = actor_worker.add(data)
assert actor_worker.world_size == correct_world_size[idx]
assert actor_output.batch["a"].tolist() == correct_output[idx]
ray.shutdown()
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