Source code for lightning.fabric.plugins.precision.deepspeed

# 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 contextlib import AbstractContextManager, nullcontext
from typing import TYPE_CHECKING, Any, Literal

import torch
from lightning_utilities.core.apply_func import apply_to_collection
from torch import Tensor
from torch.nn import Module
from typing_extensions import get_args, override

from lightning.fabric.plugins.precision.precision import Precision
from lightning.fabric.plugins.precision.utils import _convert_fp_tensor, _DtypeContextManager
from lightning.fabric.utilities.types import Steppable

if TYPE_CHECKING:
    from deepspeed import DeepSpeedEngine

_PRECISION_INPUT = Literal["32-true", "16-true", "bf16-true", "16-mixed", "bf16-mixed"]


[docs]class DeepSpeedPrecision(Precision): """Precision plugin for DeepSpeed integration. Args: precision: Full precision (32-true), half precision (16-true, bf16-true) or mixed precision (16-mixed, bf16-mixed). Raises: ValueError: If unsupported ``precision`` is provided. """ def __init__(self, precision: _PRECISION_INPUT) -> None: supported_precision = get_args(_PRECISION_INPUT) if precision not in supported_precision: raise ValueError( f"`precision={precision!r})` is not supported in DeepSpeed." f" `precision` must be one of: {supported_precision}." ) self.precision = precision precision_to_type = { "bf16-mixed": torch.bfloat16, "16-mixed": torch.float16, "bf16-true": torch.bfloat16, "16-true": torch.float16, "32-true": torch.float32, } self._desired_dtype = precision_to_type[self.precision]
[docs] @override def convert_module(self, module: Module) -> Module: if "true" in self.precision: return module.to(dtype=self._desired_dtype) return module
[docs] @override def tensor_init_context(self) -> AbstractContextManager: if "true" not in self.precision: return nullcontext() return _DtypeContextManager(self._desired_dtype)
[docs] @override def module_init_context(self) -> AbstractContextManager: return self.tensor_init_context()
[docs] @override def convert_input(self, data: Any) -> Any: return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=self._desired_dtype)
[docs] @override def convert_output(self, data: Any) -> Any: return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=torch.get_default_dtype())
[docs] @override def backward(self, tensor: Tensor, model: "DeepSpeedEngine", *args: Any, **kwargs: Any) -> None: """Performs back-propagation using DeepSpeed's engine.""" model.backward(tensor, *args, **kwargs)
[docs] @override def optimizer_step( self, optimizer: Steppable, **kwargs: Any, ) -> Any: # DeepSpeed handles the optimizer step internally return optimizer.step(**kwargs)