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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 | # 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
os.environ["RAY_DEDUP_LOGS"] = "0"
os.environ["NCCL_DEBUG"] = "WARN"
import ray
import torch
import torch.distributed
from verl.single_controller.base.worker import Worker
from verl.single_controller.ray.base import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup
from verl.utils.device import get_device_name
@ray.remote
class TestAllGatherActor(Worker):
def __init__(self, size) -> None:
super().__init__()
self.size = size
def init(self):
torch.distributed.init_process_group()
self.tensor = torch.zeros(size=(self.size,), dtype=torch.int64, device=get_device_name())
self.tensor += self.rank
def all_gather(self):
world_size = self._world_size
output = torch.zeros(
size=(self.tensor.shape[0] * world_size,), dtype=self.tensor.dtype, device=self.tensor.device
)
torch.distributed.all_gather_into_tensor(output, self.tensor, async_op=False)
return output
@ray.remote
class TestAllGatherActorV2(Worker):
def __init__(self, size) -> None:
super().__init__()
self.size = size
torch.distributed.init_process_group()
self.tensor = torch.zeros(size=(self.size,), dtype=torch.int64, device=get_device_name())
self.tensor += self.rank
def all_gather(self):
world_size = self._world_size
output = torch.zeros(
size=(self.tensor.shape[0] * world_size,), dtype=self.tensor.dtype, device=self.tensor.device
)
torch.distributed.all_gather_into_tensor(output, self.tensor, async_op=False)
return output
def test_all_gather_torch():
"""
In this test, we instantiate 4 GPUs in a group and test the all_gather
"""
ray.init()
# create 4 workers, each hold a GPU
resource_pool = RayResourcePool([4], use_gpu=True)
class_with_args = RayClassWithInitArgs(cls=TestAllGatherActor, size=2)
worker_group = RayWorkerGroup(
resource_pool, class_with_args, name_prefix="worker_group_torch", device_name=get_device_name()
)
worker_group.execute_all_sync("init")
output = worker_group.execute_all_sync("all_gather")
for i in range(1, len(output)):
assert torch.all(output[i] == output[0])
output = output[0].cpu()
print(output)
assert torch.all(output == torch.tensor([0, 0, 1, 1, 2, 2, 3, 3], dtype=torch.int64))
ray.shutdown()
def test_all_gather_torch_v2():
"""
In this test, we instantiate 4 GPUs in a group and test the all_gather
"""
ray.init()
# create 4 workers, each hold a GPU
resource_pool = RayResourcePool([4], use_gpu=True)
class_with_args = RayClassWithInitArgs(cls=TestAllGatherActorV2, size=2)
worker_group = RayWorkerGroup(
resource_pool, class_with_args, name_prefix="worker_group_torch", device_name=get_device_name()
)
output = worker_group.execute_all_sync("all_gather")
for i in range(1, len(output)):
assert torch.all(output[i] == output[0])
output = output[0].cpu()
print(output)
assert torch.all(output == torch.tensor([0, 0, 1, 1, 2, 2, 3, 3], dtype=torch.int64))
ray.shutdown()
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