Source code for pytorch_lightning.plugins.precision.hpu
# 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
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from typing import cast, Optional, Union
from typing_extensions import get_args, Literal
from pytorch_lightning.accelerators.hpu import _HPU_AVAILABLE
from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
from pytorch_lightning.utilities.exceptions import MisconfigurationException
if _HPU_AVAILABLE:
from habana_frameworks.torch.hpex import hmp
_PRECISION_INPUT_INT = Literal[32, 16]
_PRECISION_INPUT_STR = Literal["32", "16", "bf16"]
_PRECISION_INPUT = Union[_PRECISION_INPUT_INT, _PRECISION_INPUT_STR]
[docs]class HPUPrecisionPlugin(PrecisionPlugin):
"""Plugin that enables bfloat/half support on HPUs.
Args:
precision: The precision to use.
opt_level: Choose optimization level for hmp.
bf16_file_path: Path to bf16 ops list in hmp O1 mode.
fp32_file_path: Path to fp32 ops list in hmp O1 mode.
verbose: Enable verbose mode for hmp.
"""
def __init__(
self,
precision: _PRECISION_INPUT,
opt_level: str = "O2",
bf16_file_path: Optional[str] = None,
fp32_file_path: Optional[str] = None,
verbose: bool = False,
) -> None:
if not _HPU_AVAILABLE:
raise MisconfigurationException("HPU precision plugin requires HPU devices.")
supported_precision = get_args(_PRECISION_INPUT_STR) + get_args(_PRECISION_INPUT_INT)
if precision not in supported_precision:
raise ValueError(
f"`Trainer(accelerator='hpu', precision={precision!r})` is not supported."
f" `precision` must be one of: {supported_precision}."
)
self.precision = cast(_PRECISION_INPUT_STR, str(precision))
if self.precision in ("16", "bf16"):
hmp.convert(
opt_level=opt_level, bf16_file_path=bf16_file_path, fp32_file_path=fp32_file_path, isVerbose=verbose
)