Source code for lightning.pytorch.plugins.precision.fsdp
# 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
from typing import TYPE_CHECKING, Any, Callable, Optional
import torch
from lightning_utilities import apply_to_collection
from torch import Tensor
from torch.nn import Module
from typing_extensions import get_args, override
import lightning.pytorch as pl
from lightning.fabric.plugins.precision.amp import _optimizer_handles_unscaling
from lightning.fabric.plugins.precision.fsdp import _PRECISION_INPUT
from lightning.fabric.plugins.precision.utils import _convert_fp_tensor, _DtypeContextManager
from lightning.fabric.utilities.types import Optimizable
from lightning.pytorch.plugins.precision.precision import Precision
from lightning.pytorch.utilities.exceptions import MisconfigurationException
if TYPE_CHECKING:
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision as TorchMixedPrecision
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
[docs]class FSDPPrecision(Precision):
"""Precision plugin for training with Fully Sharded Data Parallel (FSDP).
.. warning:: This is an :ref:`experimental <versioning:Experimental API>` feature.
Args:
precision: Full precision (32-true), half precision (16-true, bf16-true) or
mixed precision (16-mixed, bf16-mixed).
scaler: An optional :class:`torch.distributed.fsdp.sharded_grad_scaler.ShardedGradScaler` to use.
Raises:
ValueError:
If unsupported ``precision`` is provided.
"""
def __init__(self, precision: _PRECISION_INPUT, scaler: Optional["ShardedGradScaler"] = None) -> None:
supported_precision = get_args(_PRECISION_INPUT)
if precision not in supported_precision:
raise ValueError(
f"`precision={precision!r})` is not supported in FSDP."
f" `precision` must be one of: {supported_precision}."
)
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
if scaler is not None and self.precision != "16-mixed":
raise ValueError(f"`precision={precision!r}` does not use a scaler, found {scaler}.")
self.scaler = ShardedGradScaler() if scaler is None and precision == "16-mixed" else None
self.precision = precision
precision_to_type = {
"bf16-mixed": torch.float32,
"16-mixed": torch.float32,
"bf16-true": torch.bfloat16,
"16-true": torch.float16,
"32-true": torch.float32,
}
self._desired_input_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_input_dtype)
return module
[docs] @override
def clip_grad_by_norm(self, *_: Any, **__: Any) -> None:
# see https://pytorch.org/docs/stable/fsdp.html#torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_
# section `Gradient Clipping`, using `torch.nn.utils.clip_grad_norm_` is incorrect with FSDP.
# To overcome this we need to call root_sharded_module.clip_grad_norm(clip_val), but we don't have a reference
# to the root module
raise MisconfigurationException(
f"`gradient_clip_algorithm='norm'` is currently not supported for `{self.__class__.__name__}`"
)
@property
def mixed_precision_config(self) -> "TorchMixedPrecision":
from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision as TorchMixedPrecision
if self.precision == "16-mixed":
param_dtype = torch.float32
reduce_dtype = buffer_dtype = torch.float16
elif self.precision == "bf16-mixed":
param_dtype = torch.float32
reduce_dtype = buffer_dtype = torch.bfloat16
elif self.precision == "16-true":
param_dtype = reduce_dtype = buffer_dtype = torch.float16
elif self.precision == "bf16-true":
param_dtype = reduce_dtype = buffer_dtype = torch.bfloat16
elif self.precision == "32-true":
param_dtype = torch.float32
reduce_dtype = buffer_dtype = torch.float32
else:
raise MisconfigurationException(f"Was unable to infer precision type, received {self.precision!r}.")
return TorchMixedPrecision(
param_dtype=param_dtype,
reduce_dtype=reduce_dtype,
buffer_dtype=buffer_dtype,
)
[docs] @override
def tensor_init_context(self) -> AbstractContextManager:
return _DtypeContextManager(self._desired_input_dtype)
[docs] @override
def module_init_context(self) -> AbstractContextManager:
return _DtypeContextManager(self.mixed_precision_config.param_dtype or torch.float32)
[docs] @override
def forward_context(self) -> AbstractContextManager:
if "mixed" in self.precision:
return torch.autocast("cuda", dtype=(torch.bfloat16 if self.precision == "bf16-mixed" else torch.float16))
return _DtypeContextManager(self._desired_input_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 pre_backward(self, tensor: Tensor, module: "pl.LightningModule") -> Tensor: # type: ignore[override]
if self.scaler is not None:
tensor = self.scaler.scale(tensor)
return super().pre_backward(tensor, module)
[docs] @override
def optimizer_step( # type: ignore[override]
self,
optimizer: Optimizable,
model: "pl.LightningModule",
closure: Callable[[], Any],
**kwargs: Any,
) -> Any:
if self.scaler is None:
# skip scaler logic, as bfloat16 does not require scaler
return super().optimizer_step(optimizer, model=model, closure=closure, **kwargs)
closure_result = closure()
if not _optimizer_handles_unscaling(optimizer):
# Unscaling needs to be performed here in case we are going to apply gradient clipping.
# Optimizers that perform unscaling in their `.step()` method are not supported (e.g., fused Adam).
# Note: `unscale` happens after the closure is executed, but before the `on_before_optimizer_step` hook.
self.scaler.unscale_(optimizer) # type: ignore[arg-type]
self._after_closure(model, optimizer)
skipped_backward = closure_result is None
# in manual optimization, the closure does not return a value
if not model.automatic_optimization or not skipped_backward:
# note: the scaler will skip the `optimizer.step` if nonfinite gradients are found
step_output = self.scaler.step(optimizer, **kwargs) # type: ignore[arg-type]
self.scaler.update()
return step_output
return closure_result
[docs] @override
def state_dict(self) -> dict[str, Any]:
if self.scaler is not None:
return self.scaler.state_dict()
return {}
[docs] @override
def load_state_dict(self, state_dict: dict[str, Any]) -> None:
if self.scaler is not None:
self.scaler.load_state_dict(state_dict)
