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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 | # 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
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