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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 | # Copyright 2025 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 numpy as np
import ray
import torch
from tensordict import TensorDict
import verl.utils.tensordict_utils as tu
from verl import DataProto
from verl.single_controller.base import Worker
from verl.single_controller.base.decorator import make_nd_compute_dataproto_dispatch_fn, register
from verl.utils.device import get_device_name, get_nccl_backend
@ray.remote
class TestActor(Worker):
def __init__(self):
super().__init__()
import torch.distributed
torch.distributed.init_process_group(backend=get_nccl_backend())
self.infer_device_mesh = torch.distributed.device_mesh.init_device_mesh(
device_type=get_device_name(), mesh_shape=[2, 4], mesh_dim_names=["dp", "tp"]
)
self.train_device_mesh = torch.distributed.device_mesh.init_device_mesh(
device_type=get_device_name(), mesh_shape=[2, 2, 2], mesh_dim_names=["pp", "dp", "tp"]
)
self._register_dispatch_collect_info(
"infer",
dp_rank=self.infer_device_mesh["dp"].get_local_rank(),
is_collect=self.infer_device_mesh["tp"].get_local_rank() == 0,
)
self._register_dispatch_collect_info(
"train",
dp_rank=self.train_device_mesh["dp"].get_local_rank(),
is_collect=self.train_device_mesh["tp"].get_local_rank() == 0
and self.train_device_mesh["pp"].get_local_rank() == 1,
)
@register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="infer"))
def generate_data_proto(self, data: DataProto):
tp_rank = self.infer_device_mesh["tp"].get_local_rank()
dp_rank = self.infer_device_mesh["dp"].get_local_rank()
data.batch["a"] += (tp_rank + 1) * dp_rank
return data
@register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="infer"))
def generate_tensordict(self, data: TensorDict):
tp_rank = self.infer_device_mesh["tp"].get_local_rank()
dp_rank = self.infer_device_mesh["dp"].get_local_rank()
data["a"] += (tp_rank + 1) * dp_rank
return data
@register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="train"))
def train_data_proto(self, data: DataProto):
tp_rank = self.train_device_mesh["tp"].get_local_rank()
dp_rank = self.train_device_mesh["dp"].get_local_rank()
pp_rank = self.train_device_mesh["pp"].get_local_rank()
data.batch["a"] += (tp_rank + 1) * (dp_rank + 2) * (pp_rank + 3)
# tp rank 0, pp rank 1, dp rank 0, output data added: 8 + 3 = 11
# tp rank 0, pp rank 1, dp rank 1, output data added: 12 + 4 = 16
return data
@register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="train"))
def train_tensordict(self, data: TensorDict):
tp_rank = self.train_device_mesh["tp"].get_local_rank()
dp_rank = self.train_device_mesh["dp"].get_local_rank()
pp_rank = self.train_device_mesh["pp"].get_local_rank()
data["a"] += (tp_rank + 1) * (dp_rank + 2) * (pp_rank + 3)
# tp rank 0, pp rank 1, dp rank 0, output data added: 8 + 3 = 11
# tp rank 0, pp rank 1, dp rank 1, output data added: 12 + 4 = 16
return data
@register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="infer"))
def generate_nested_tensor(self, data: TensorDict):
tp_rank = self.infer_device_mesh["tp"].get_local_rank()
dp_rank = self.infer_device_mesh["dp"].get_local_rank()
assert data.shape[0] == 8
data["input_ids"] += tp_rank + dp_rank
print(data)
return data
def test_dist_global_info_wg():
# create a worker group with size 8
# register a infer dist info with tp=4, dp=2
# register a train dist info with tp=2, dp=2, pp=2
# test the correctness of data dispatch and computation
from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup
ray.init()
ray_cls = RayClassWithInitArgs(TestActor)
resource_pool = RayResourcePool(process_on_nodes=[8])
wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls, device_name=get_device_name())
infer_input_data_proto = DataProto.from_single_dict(data={"a": torch.tensor([1, 2])})
infer_output_data_proto = wg.generate_data_proto(infer_input_data_proto)
assert wg._dispatch_info["infer"] == [0, 0, 0, 0, 1, 1, 1, 1]
assert torch.all(torch.eq(infer_output_data_proto.batch["a"], torch.tensor([1, 3])))
infer_input_tensordict = infer_input_data_proto.to_tensordict()
infer_output_tensordict = wg.generate_tensordict(infer_input_tensordict)
assert torch.all(torch.eq(infer_output_tensordict["a"], torch.tensor([1, 3])))
train_input_data_proto = DataProto.from_single_dict(data={"a": torch.tensor([3, 4])})
train_output_data_proto = wg.train_data_proto(train_input_data_proto)
assert wg._dispatch_info["train"] == [0, 0, 1, 1, 0, 0, 1, 1]
assert torch.all(torch.eq(train_output_data_proto.batch["a"], torch.tensor([11, 16])))
train_input_tensordict = train_input_data_proto.to_tensordict()
train_output_tensordict = wg.train_tensordict(train_input_tensordict)
assert torch.all(torch.eq(train_output_tensordict["a"], torch.tensor([11, 16])))
# create a batch size of input_ids
input_ids = [
torch.randint(low=0, high=128, size=(np.random.randint(low=1, high=10, dtype=np.int64),)) for _ in range(16)
]
input_ids = torch.nested.as_nested_tensor(input_ids, layout=torch.jagged)
data = tu.get_tensordict(tensor_dict={"input_ids": input_ids})
output = wg.generate_nested_tensor(data)
input_ids_chunked = list(input_ids.chunk(2))
print(input_ids_chunked)
input_ids_chunked[0] += 0
input_ids_chunked[1] += 1
expected = tu.concat_nested_tensors(input_ids_chunked)
assert torch.all(torch.eq(output["input_ids"].values(), expected.values()))
ray.shutdown()
if __name__ == "__main__":
test_dist_global_info_wg()
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