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Source code for lightning.pytorch.accelerators.ipu

# Copyright The Lightning AI team.
#
# 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.
from typing import Any, Dict, List

import torch
from lightning_utilities.core.imports import package_available

from lightning.fabric.utilities.types import _DEVICE
from lightning.pytorch.accelerators.accelerator import Accelerator

_POPTORCH_AVAILABLE = package_available("poptorch")

if _POPTORCH_AVAILABLE:
    import poptorch

    _IPU_AVAILABLE = poptorch.ipuHardwareIsAvailable()
else:
    poptorch = None
    _IPU_AVAILABLE = False


[docs]class IPUAccelerator(Accelerator): """Accelerator for IPUs. .. warning:: Use of this accelerator beyond import and instantiation is experimental. """
[docs] def setup_device(self, device: torch.device) -> None: pass
[docs] def get_device_stats(self, device: _DEVICE) -> Dict[str, Any]: """IPU device stats aren't supported yet.""" return {}
[docs] def teardown(self) -> None: pass
[docs] @staticmethod def parse_devices(devices: int) -> int: """Accelerator device parsing logic.""" return devices
[docs] @staticmethod def get_parallel_devices(devices: int) -> List[int]: """Gets parallel devices for the Accelerator.""" return list(range(devices))
[docs] @staticmethod def auto_device_count() -> int: """Get the devices when set to auto.""" # TODO (@kaushikb11): 4 is the minimal unit they are shipped in. # Update this when api is exposed by the Graphcore team. return 4
[docs] @staticmethod def is_available() -> bool: return _IPU_AVAILABLE
@classmethod def register_accelerators(cls, accelerator_registry: Dict) -> None: accelerator_registry.register( "ipu", cls, description=f"{cls.__class__.__name__}", )

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