tests/single_controller/detached_worker/client.py

# 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
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"""
In client, we can get the server handler and send RPC request
"""

import ray
import torch
from server import Trainer
from tensordict import TensorDict

from verl import DataProto
from verl.single_controller.ray import RayClassWithInitArgs, RayWorkerGroup


def compute_position_id_with_mask(mask):
    return torch.clip(torch.cumsum(mask, dim=-1) - 1, min=0, max=None)


if __name__ == "__main__":
    ray.init(address="auto", namespace="verl")
    # get the worker group using names
    worker_names = ["trainerTrainer_0:0", "trainerTrainer_0:1"]
    cls_with_init_args = RayClassWithInitArgs(cls=Trainer)
    worker_group = RayWorkerGroup.from_detached(worker_names=worker_names, ray_cls_with_init=cls_with_init_args)

    batch_size = 16
    sequence_length = 1024

    # give Trainer some data to train
    input_ids = torch.randint(low=0, high=256, size=(batch_size, sequence_length), dtype=torch.int64, device="cuda")
    attention_mask = torch.ones_like(input_ids)
    position_ids = compute_position_id_with_mask(attention_mask)

    data = DataProto(
        batch=TensorDict(
            {"input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids},
            batch_size=batch_size,
        ),
        meta_info={},
    )

    output = worker_group.train_model(data)

    print(output)