# 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 importlib

import logging

import os

from packaging.version import parse as parse_version

from .protocol import DataProto

from .utils.device import is_npu_available

from .utils.import_utils import import_external_libs

from .utils.logging_utils import set_basic_config

version_folder = os.path.dirname(os.path.join(os.path.abspath(__file__)))

with open(os.path.join(version_folder, "version/version")) as f:

__version__ = f.read().strip()

set_basic_config(level=logging.WARNING)

__all__ = ["DataProto", "__version__"]

modules = os.getenv("VERL_USE_EXTERNAL_MODULES", "")

if modules:

modules = modules.split(",")

import_external_libs(modules)

if os.getenv("VERL_USE_MODELSCOPE", "False").lower() == "true":

if importlib.util.find_spec("modelscope") is None:

raise ImportError("You are using the modelscope hub, please install modelscope by `pip install modelscope -U`")

# Patch hub to download models from modelscope to speed up.

from modelscope.utils.hf_util import patch_hub

patch_hub()

if is_npu_available:

# Workaround for torch-npu's lack of support for creating nested tensors from NPU tensors.

#

# ```

# >>> a, b = torch.arange(3).npu(), torch.arange(5).npu() + 3

# >>> nt = torch.nested.nested_tensor([a, b], layout=torch.jagged)

# ```

# throws "not supported in npu" on Ascend NPU.

# See https://github.com/Ascend/pytorch/blob/294cdf5335439b359991cecc042957458a8d38ae/torch_npu/utils/npu_intercept.py#L109

# for details.

import torch

try:

if hasattr(torch.nested.nested_tensor, "__wrapped__"):

torch.nested.nested_tensor = torch.nested.nested_tensor.__wrapped__

if hasattr(torch.nested.as_nested_tensor, "__wrapped__"):

torch.nested.as_nested_tensor = torch.nested.as_nested_tensor.__wrapped__

except AttributeError:

pass

# In verl, the driver process aggregates the computation results of workers via Ray.

# Therefore, after a worker completes its computation job, it will package the output

# using tensordict and transfer it to the CPU. Since the `to` operation of tensordict

# is non-blocking, when transferring data from a device to the CPU, it is necessary to

# ensure that a batch of data has been completely transferred before being used on the

# host; otherwise, unexpected precision issues may arise. Tensordict has already noticed

# this problem and fixed it. Ref: https://github.com/pytorch/tensordict/issues/725

# However, the relevant modifications only cover CUDA and MPS devices and do not take effect

# for third-party devices such as NPUs. This patch fixes this issue, and the relevant

# modifications can be removed once the fix is merged into tensordict.

import tensordict

if parse_version(tensordict.__version__) < parse_version("0.10.0"):

from tensordict.base import TensorDictBase

def _sync_all_patch(self):

from torch._utils import _get_available_device_type, _get_device_module

device_type = _get_available_device_type()

if device_type is None:

return

device_module = _get_device_module(device_type)

device_module.synchronize()

TensorDictBase._sync_all = _sync_all_patch