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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | # 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 pytest
import ray
from tests.checkpoint_engine.test_utils import create_rollout_worker_group, create_trainer_worker_group
from verl.checkpoint_engine import CheckpointEngineManager
from verl.single_controller.ray.base import (
RayResourcePool,
split_resource_pool,
)
from verl.utils.device import get_device_name
from verl.utils.ray_utils import auto_await
from verl.workers.config import CheckpointEngineConfig, HFModelConfig, RolloutConfig
@pytest.mark.asyncio
@pytest.mark.parametrize("rebuild_group", [False, True])
@pytest.mark.parametrize("num_trainer, num_rollout", [(2, 6)])
@auto_await
async def test_nccl_checkpoint_engine(
rebuild_group,
num_trainer,
num_rollout,
num_nodes=1,
num_gpus_per_node=8,
check_allclose=True,
model_path="~/models/Qwen/Qwen3-8B-Base",
):
model_path = os.path.expanduser(model_path)
ray.init(
runtime_env={
"env_vars": {
"UCX_TLS": "rc,tcp,cuda",
"UCX_MAX_RNDV_RAILS": "4",
"UCX_LOG_LEVEL": "INFO",
"VERL_LOGGING_LEVEL": "DEBUG",
}
}
)
# initialize config
checkpoint_engine_config = CheckpointEngineConfig(
backend="nccl", engine_kwargs={"nccl": {"rebuild_group": rebuild_group}}
)
model_config = HFModelConfig(path=model_path, use_remove_padding=True)
rollout_config = RolloutConfig(name="vllm", checkpoint_engine=checkpoint_engine_config)
# create trainer and rollout worker group
resource_pool = RayResourcePool(process_on_nodes=[num_gpus_per_node] * num_nodes, max_colocate_count=3)
trainer_pool, rollout_pool = split_resource_pool(resource_pool, [num_trainer, num_rollout])
trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config)
trainer.reset()
rollout, replicas = await create_rollout_worker_group(rollout_pool, model_config, rollout_config, check_allclose)
# create checkpoint engine manager
checkpoint_manager = CheckpointEngineManager(config=checkpoint_engine_config, trainer=trainer, replicas=replicas)
for _ in range(3):
await checkpoint_manager.update_weights()
rollout.check_weights()
ray.shutdown()
@pytest.mark.skip(reason="temporary skip since our ci environment is not ready")
@pytest.mark.asyncio
@pytest.mark.parametrize("device", ["cuda", "cpu"])
@pytest.mark.parametrize("num_trainer, num_rollout", [(2, 6)])
@auto_await
async def test_nixl_checkpoint_engine(
num_trainer,
num_rollout,
device,
num_nodes=1,
num_gpus_per_node=8,
check_allclose=True,
model_path="~/models/Qwen/Qwen3-8B-Base",
):
model_path = os.path.expanduser(model_path)
ray.init(
runtime_env={
"env_vars": {
# TODO: it's pretty hard to set these environment variables right, please consult
# with your network admin. Maybe auto adjust UCX_* according to NCCL_IB_*?
"UCX_TLS": "rc,ud,cuda",
# "UCX_IB_GID_INDEX": "3", # NCCL_IB_GID_INDEX
# "UCX_IB_DEVICES": "mlx5_1:1,mlx5_2:1,mlx5_3:1", # NCCL_IB_HCA
"UCX_RC_TIMEOUT": "30s", # NCCL_IB_TIMEOUT
"UCX_RC_RETRY_COUNT": "7", # NCCL_IB_RETRY_COUNT
"UCX_KEEPALIVE_INTERVAL": "1s",
"UCX_KEEPALIVE_NUM_EPS": "10",
"UCX_MAX_RNDV_RAILS": "4",
"UCX_IB_ROCE_REACHABILITY_MODE": "all",
"UCX_LOG_LEVEL": "INFO",
"VERL_LOGGING_LEVEL": "DEBUG",
}
}
)
# initialize config
checkpoint_engine_config = CheckpointEngineConfig(backend="nixl", engine_kwargs={"nixl": {"device": device}})
model_config = HFModelConfig(path=model_path, use_remove_padding=True)
rollout_config = RolloutConfig(name="vllm", checkpoint_engine=checkpoint_engine_config)
# create trainer and rollout worker group
resource_pool = RayResourcePool(process_on_nodes=[num_gpus_per_node] * num_nodes, max_colocate_count=3)
trainer_pool, rollout_pool = split_resource_pool(resource_pool, [num_trainer, num_rollout])
trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config)
trainer.reset()
rollout, replicas = await create_rollout_worker_group(rollout_pool, model_config, rollout_config, check_allclose)
# create checkpoint engine manager
checkpoint_manager = CheckpointEngineManager(config=checkpoint_engine_config, trainer=trainer, replicas=replicas)
for _ in range(3):
await checkpoint_manager.update_weights()
rollout.check_weights()
ray.shutdown()
@pytest.mark.skip(reason="temporary skip since our ci environment is not ready")
@pytest.mark.asyncio
@pytest.mark.parametrize("rebuild_group", [False])
@pytest.mark.parametrize("num_trainer, num_rollout", [(2, 6)])
@auto_await
async def test_kimi_checkpoint_engine(
rebuild_group,
num_trainer,
num_rollout,
num_nodes=1,
num_gpus_per_node=8,
check_allclose=True,
model_path="~/models/Qwen/Qwen3-8B-Base",
):
model_path = os.path.expanduser(model_path)
ray.init(
runtime_env={
"env_vars": {
"NCCL_IB_HCA": "mlx5",
"VERL_LOGGING_LEVEL": "DEBUG",
}
}
)
# initialize config
checkpoint_engine_config = CheckpointEngineConfig(
backend="kimi_ckpt_engine", engine_kwargs={"kimi_ckpt_engine": {"rebuild_group": rebuild_group}}
)
model_config = HFModelConfig(path=model_path, use_remove_padding=True)
rollout_config = RolloutConfig(name="vllm", checkpoint_engine=checkpoint_engine_config)
# create trainer and rollout worker group
resource_pool = RayResourcePool(process_on_nodes=[num_gpus_per_node] * num_nodes, max_colocate_count=3)
resource_pool.get_placement_groups(device_name=get_device_name())
trainer_pool, rollout_pool = split_resource_pool(resource_pool, [num_trainer, num_rollout])
trainer = create_trainer_worker_group(trainer_pool, model_config, checkpoint_engine_config)
trainer.reset()
rollout, replicas = await create_rollout_worker_group(rollout_pool, model_config, rollout_config, check_allclose)
# create checkpoint engine manager
checkpoint_manager = CheckpointEngineManager(config=checkpoint_engine_config, trainer=trainer, replicas=replicas)
for _ in range(3):
await checkpoint_manager.update_weights()
rollout.check_weights()
ray.shutdown()
if __name__ == "__main__":
test_nccl_checkpoint_engine(
rebuild_group=False,
num_trainer=2,
num_rollout=30,
num_nodes=4,
num_gpus_per_node=8,
check_allclose=False,
model_path=os.environ["HDFS_ROOT"] + "/model/Qwen3-30B-A3B-Base",
)
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