Source code for lightning.fabric.plugins.precision.precision
# 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 Any, Literal
from torch import Tensor
from torch.nn import Module
from torch.optim import Optimizer
from lightning.fabric.utilities.types import _PARAMETERS, Optimizable
_PRECISION_INPUT_INT = Literal[64, 32, 16]
_PRECISION_INPUT_STR_ALIAS_CONVERSION = {"64": "64-true", "32": "32-true", "16": "16-mixed", "bf16": "bf16-mixed"}
_PRECISION_INPUT_STR_ALIAS = Literal["64", "32", "16", "bf16"]
_PRECISION_INPUT_STR = Literal[
"transformer-engine",
"transformer-engine-float16",
"16-true",
"16-mixed",
"bf16-true",
"bf16-mixed",
"32-true",
"64-true",
]
_PRECISION_INPUT = _PRECISION_INPUT_INT | _PRECISION_INPUT_STR | _PRECISION_INPUT_STR_ALIAS
[docs]class Precision:
"""Base class for all plugins handling the precision-specific parts of the training.
The class attribute precision must be overwritten in child classes. The default value reflects fp32 training.
"""
precision: _PRECISION_INPUT_STR = "32-true"
[docs] def convert_module(self, module: Module) -> Module:
"""Convert the module parameters to the precision type this plugin handles.
This is optional and depends on the precision limitations during optimization.
"""
return module
[docs] def tensor_init_context(self) -> AbstractContextManager:
"""Controls how tensors get created (device, dtype)."""
return nullcontext()
[docs] def module_init_context(self) -> AbstractContextManager:
"""Instantiate module parameters or tensors in the precision type this plugin handles.
This is optional and depends on the precision limitations during optimization.
"""
return nullcontext()
[docs] def forward_context(self) -> AbstractContextManager:
"""A contextmanager for managing model forward/training_step/evaluation_step/predict_step."""
return nullcontext()
[docs] def convert_output(self, data: Any) -> Any:
"""Convert outputs to the floating point precision type expected after model's forward.
This is a no-op in the base precision plugin, since we assume the data already has the desired type (default is
torch.float32).
"""
return data
[docs] def pre_backward(self, tensor: Tensor, module: Module | None) -> Any:
"""Runs before precision plugin executes backward.
Args:
tensor: The tensor that will be used for backpropagation
module: The module that was involved in producing the tensor and whose parameters need the gradients
"""
[docs] def backward(self, tensor: Tensor, model: Module | None, *args: Any, **kwargs: Any) -> None:
"""Performs the actual backpropagation.
Args:
tensor: The tensor that will be used for backpropagation
model: The module that was involved in producing the tensor and whose parameters need the gradients
"""
tensor.backward(*args, **kwargs)
[docs] def post_backward(self, tensor: Tensor, module: Module | None) -> Any:
"""Runs after precision plugin executes backward.
Args:
tensor: The tensor that will be used for backpropagation
module: The module that was involved in producing the tensor and whose parameters need the gradients
"""
[docs] def optimizer_step(
self,
optimizer: Optimizable,
**kwargs: Any,
) -> Any:
"""Hook to run the optimizer step."""
return optimizer.step(**kwargs)
[docs] def main_params(self, optimizer: Optimizer) -> _PARAMETERS:
"""The main params of the model.
Returns the plain model params here. Maybe different in other precision plugins.
"""
for group in optimizer.param_groups:
yield from group["params"]
def unscale_gradients(self, optimizer: Optimizer) -> None:
return
[docs] def state_dict(self) -> dict[str, Any]:
"""Called when saving a checkpoint, implement to generate precision plugin state_dict.
Returns:
A dictionary containing precision plugin state.
"""
return {}
[docs] def load_state_dict(self, state_dict: dict[str, Any]) -> None:
"""Called when loading a checkpoint, implement to reload precision plugin state given precision plugin
state_dict.
Args:
state_dict: the precision plugin state returned by ``state_dict``.
"""
pass
[docs] def teardown(self) -> None:
"""This method is called to teardown the training process.
It is the right place to release memory and free other resources.
"""
