Source code for pytorch_lightning.strategies.ipu
# Copyright The PyTorch Lightning 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.
import json
import os
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import torch
from lightning_utilities.core.apply_func import apply_to_collection
from torch import Tensor
from torch.utils.data import DataLoader, Sampler
import pytorch_lightning as pl
from lightning_lite.plugins import CheckpointIO, ClusterEnvironment
from lightning_lite.utilities.cloud_io import get_filesystem
from pytorch_lightning.overrides.base import _LightningModuleWrapperBase
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.parallel import ParallelStrategy
from pytorch_lightning.strategies.strategy import TBroadcast
from pytorch_lightning.strategies.utils import _fp_to_half
from pytorch_lightning.trainer.states import RunningStage, TrainerFn
from pytorch_lightning.utilities import _IPU_AVAILABLE, _POPTORCH_AVAILABLE, rank_zero_warn
from pytorch_lightning.utilities.data import _get_dataloader_init_args_and_kwargs, _reinstantiate_wrapped_cls
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.types import STEP_OUTPUT
if _POPTORCH_AVAILABLE:
import poptorch
else:
poptorch = None
[docs]class IPUStrategy(ParallelStrategy):
"""Plugin for training on IPU devices."""
strategy_name = "ipu_strategy"
def __init__(
self,
accelerator: Optional["pl.accelerators.Accelerator"] = None,
device_iterations: int = 1,
autoreport: bool = False,
autoreport_dir: Optional[str] = None,
parallel_devices: Optional[List[torch.device]] = None,
cluster_environment: Optional[ClusterEnvironment] = None,
checkpoint_io: Optional[CheckpointIO] = None,
precision_plugin: Optional[PrecisionPlugin] = None,
training_opts: Optional["poptorch.Options"] = None,
inference_opts: Optional["poptorch.Options"] = None,
) -> None:
"""
Arguments:
device_iterations: Number of iterations to run on device at once before returning to host.
This can be used as an optimization to speed up training.
https://docs.graphcore.ai/projects/poptorch-user-guide/en/0.1.67/batching.html
autoreport: Enable auto-reporting for IPUs using PopVision
https://docs.graphcore.ai/projects/graphcore-popvision-user-guide/en/latest/graph/graph.html
autoreport_dir: Optional directory to store autoReport output.
training_opts: Optional ``poptorch.Options`` to override the default created options for training.
inference_opts: Optional ``poptorch.Options`` to override the default
created options for validation/testing and predicting.
"""
super().__init__(
accelerator=accelerator,
parallel_devices=parallel_devices,
cluster_environment=cluster_environment,
checkpoint_io=checkpoint_io,
precision_plugin=precision_plugin,
)
if not _IPU_AVAILABLE:
raise MisconfigurationException(
"The IPU Accelerator requires IPU devices to run. "
"Learn more or get started with IPUs at https://www.graphcore.ai/getstarted"
)
self.device_iterations = device_iterations
self.autoreport = autoreport
self.autoreport_dir = autoreport_dir
self.poptorch_models: Dict[RunningStage, "poptorch.PoplarExecutor"] = {}
self._training_opts = training_opts
self._inference_opts = inference_opts
if self.autoreport:
options: Dict[str, Any] = {"autoReport.all": self.autoreport}
if self.autoreport_dir:
self._fs = get_filesystem(str(self.autoreport_dir))
self._fs.makedirs(self.autoreport_dir, exist_ok=True)
options["autoReport.directory"] = self.autoreport_dir
os.environ["POPLAR_ENGINE_OPTIONS"] = json.dumps(options)
self._update_dataloader_original: Optional[Callable] = None
self._optimizer_zero_grad_original: Optional[Callable] = None
[docs] def setup(self, trainer: "pl.Trainer") -> None:
# set the `accumulate_grad_batches` property as early as possible
self._handle_gradient_accumulation_steps()
# patch the dataloader creation function with the custom `poptorch.DataLoader`.
# this violates the intended control flow for the plugins, but since this is experimental, we have chosen
# to use the simpler solution before adding abstractions to override the `DataLoader` class
self._update_dataloader_original = pl.trainer.connectors.data_connector._update_dataloader
pl.trainer.connectors.data_connector._update_dataloader = self._convert_to_poptorch_loader
super().setup(trainer)
assert self.lightning_module is not None
# disable the `optimizer_zero_grad` function by setting it to `None`.
# this is because the IPU zeros the gradients internally
self._optimizer_zero_grad_original = self.lightning_module.optimizer_zero_grad
self._disable_zero_grad()
self.model = _LightningModuleWrapperBase(self.lightning_module)
# reset the backup
self.poptorch_models = {}
# Separate models are instantiated for different stages, but they share the same weights on host.
# When validation/test models are run, weights are synced first.
trainer_fn = self.lightning_module.trainer.state.fn
if trainer_fn == TrainerFn.FITTING:
# Create model for training and validation which will run on fit
training_opts = self.training_opts
inference_opts = self.inference_opts
optimizer = self.lightning_module.trainer.optimizers[0]
model = poptorch.trainingModel(model=self.model, options=training_opts, optimizer=optimizer)
self.poptorch_models[RunningStage.TRAINING] = model
if self.lightning_module.trainer.enable_validation:
model = poptorch.inferenceModel(model=self.model, options=inference_opts)
self.poptorch_models[RunningStage.VALIDATING] = model
if self.lightning_module.trainer.num_sanity_val_steps > 0:
self.poptorch_models[RunningStage.SANITY_CHECKING] = model
elif trainer_fn == TrainerFn.VALIDATING:
model = poptorch.inferenceModel(model=self.model, options=self.inference_opts)
self.poptorch_models[RunningStage.VALIDATING] = model
elif trainer_fn == TrainerFn.TESTING:
model = poptorch.inferenceModel(model=self.model, options=self.inference_opts)
self.poptorch_models[RunningStage.TESTING] = model
elif trainer_fn == TrainerFn.PREDICTING:
model = poptorch.inferenceModel(model=self.model, options=self.inference_opts)
self.poptorch_models[RunningStage.PREDICTING] = model
[docs] def setup_optimizers(self, trainer: "pl.Trainer") -> None:
super().setup_optimizers(trainer)
if len(self.optimizers) > 1:
raise MisconfigurationException("IPUs currently only support one optimizer.")
@property
def replication_factor(self) -> int:
if not self.lightning_module or not self.poptorch_models:
# The plugin has been passed in by the user and has not been connected to the Trainer.
# Check if the user has passed in custom poptorch.Options to infer number of IPUs being used.
# In this scenario we prioritize the training options.
if self._training_opts:
return self._training_opts.replication_factor
if self._inference_opts:
return self._inference_opts.replication_factor
assert self.parallel_devices
return len(self.parallel_devices)
stage = self.lightning_module.trainer.state.stage
assert stage is not None
return self.poptorch_models[stage]._options.toDict()["replication_factor"]
def _create_opts(self, training: bool) -> "poptorch.Options":
assert self.lightning_module is not None
opts = poptorch.Options()
opts.deviceIterations(self.device_iterations)
opts.replicationFactor(self.replication_factor)
gradient_accumulation = self.lightning_module.trainer.accumulate_grad_batches if training else 1
opts.Training.gradientAccumulation(gradient_accumulation)
if os.environ.get("PL_GLOBAL_SEED"):
opts.randomSeed(int(os.environ["PL_GLOBAL_SEED"]))
return opts
@property
def training_opts(self) -> "poptorch.Options":
if self._training_opts is None:
self._training_opts = self._create_opts(training=True)
return self._training_opts
@property
def inference_opts(self) -> "poptorch.Options":
if self._inference_opts is None:
self._inference_opts = self._create_opts(training=False)
return self._inference_opts
def _convert_to_poptorch_loader(
self, dataloader: DataLoader, sampler: Union[Sampler, Iterable], mode: Optional[RunningStage] = None
) -> "poptorch.DataLoader":
if isinstance(dataloader, poptorch.DataLoader):
# the user is returning the `poptorch.DataLoader` directly, don't change anything.
return dataloader
dl_args, dl_kwargs = _get_dataloader_init_args_and_kwargs(
dataloader, sampler, mode, self.replication_factor > 1
)
opts = self.training_opts if mode == RunningStage.TRAINING else self.inference_opts
dataloader = _reinstantiate_wrapped_cls(
dataloader, opts, *dl_args, explicit_cls=poptorch.DataLoader, **dl_kwargs
)
return dataloader
def _handle_gradient_accumulation_steps(self) -> None:
"""Override the trainer.accumulation_scheduler to act as ``accumulate_grad_batches=1`` if gradient
accumulation has been set.
``optimizer_step`` will be called on every batch, and the IPU will handle grad accumulation internally.
"""
assert self.lightning_module is not None
accumulation_scheduler = self.lightning_module.trainer.accumulation_scheduler
if accumulation_scheduler.epochs != [0]:
raise MisconfigurationException(
"IPUs currently does not support different `accumulate_grad_batches` at different epochs."
)
# TODO(@tchaton): Add support for accumulate_grad_batches being a dictionary
accumulation_scheduler.scheduling.update({0: 1})
@property
def _n_replicate(self) -> int:
assert self.lightning_module is not None
opts = self.training_opts if self.lightning_module.training else self.inference_opts
accumulate_grad_batches = opts.Training.gradient_accumulation
device_iterations = opts.device_iterations
replication_factor = opts.replication_factor
return replication_factor * device_iterations * accumulate_grad_batches
def _prepare_input(self, args: Any) -> Any:
def to_tuple(x: Any) -> Tuple:
return tuple(x)
def to_tensor(x: Any) -> Tensor:
return torch.tensor(x).unsqueeze(0).repeat(self._n_replicate)
args = apply_to_collection(args, dtype=list, function=to_tuple)
args = apply_to_collection(args, dtype=(int, float), function=to_tensor)
return args
[docs] def batch_to_device(self, batch: Any, device: Optional[torch.device] = None, dataloader_idx: int = 0) -> Any:
# This override is necessary because the cast must occur before the data
# is moved to the device to prevent wasteful host->device copies.
batch = apply_to_collection(batch, Tensor, function=_fp_to_half, precision=self.precision_plugin.precision)
# We don't call `super().batch_to_device` because `data.to(device)` is not
# currently necessary for IPUs. The movement of data from host<->IPU is
# currently handled by PopTorch.
return batch
def _disable_zero_grad(self) -> None:
lightning_module = self.lightning_module
assert lightning_module is not None
if is_overridden("optimizer_zero_grad", lightning_module):
assert lightning_module is not None # `is_overridden` returns False otherwise
rank_zero_warn(
"You have overridden the `LightningModule.optimizer_zero_grad` hook but it will be ignored since"
" IPUs handle the zeroing of gradients internally."
)
lightning_module.optimizer_zero_grad = None # type: ignore[assignment]
def _step(self, stage: RunningStage, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
args = self._prepare_input(args)
poptorch_model = self.poptorch_models[stage]
with pl.core.module._jit_is_scripting():
return poptorch_model(*args, **kwargs)
[docs] def training_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
with self.precision_plugin.train_step_context():
return self._step(RunningStage.TRAINING, *args, **kwargs)
[docs] def validation_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
with self.precision_plugin.val_step_context():
return self._step(RunningStage.VALIDATING, *args, **kwargs)
[docs] def test_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
with self.precision_plugin.test_step_context():
return self._step(RunningStage.TESTING, *args, **kwargs)
[docs] def predict_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
with self.precision_plugin.predict_step_context():
return self._step(RunningStage.PREDICTING, *args, **kwargs)
[docs] def teardown(self) -> None:
if self._update_dataloader_original is not None:
# undo dataloader patching
pl.trainer.connectors.data_connector._update_dataloader = self._update_dataloader_original
assert self.lightning_module is not None
if self._optimizer_zero_grad_original is not None:
# re-enable `optimizer_zero_grad`
self.lightning_module.optimizer_zero_grad = self._optimizer_zero_grad_original # type: ignore[assignment]
for model in self.poptorch_models.values():
model.destroy()
super().teardown()
def _compiled(self, model: Any) -> bool:
# Required to ensure we only attach compiled models, as they are compiled lazily.
return model._executable is not None
def _detach_models(self) -> None:
"""Detaches all stage specific models from IPU devices."""
for k, model in self.poptorch_models.items():
if self._compiled(model) and model.isAttachedToDevice():
model.detachFromDevice()
def _load_model(self, stage: RunningStage) -> None:
"""Loads the stage specific accelerator model onto device if compiled and not attached to IPU devices.
Args:
stage: The stage to load
"""
self._detach_models()
model = self.poptorch_models[stage]
if self._compiled(model) and not model.isAttachedToDevice():
model.attachToDevice()
[docs] def on_train_batch_start(self, batch: Any, batch_idx: int) -> None:
# Updates optimizer stats if LR scheduler modified the optimizer state
optimizer = self.optimizers[0]
self.poptorch_models[RunningStage.TRAINING].setOptimizer(optimizer)
@property
def root_device(self) -> torch.device:
pass
@property
def is_global_zero(self) -> bool:
return True
[docs] def reduce(self, tensor: Union[Tensor, Any], *args: Any, **kwargs: Any) -> Union[Tensor, Any]:
return tensor
[docs] def all_gather(self, tensor: Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> Tensor:
return tensor
@classmethod
def register_strategies(cls, strategy_registry: Dict) -> None:
strategy_registry.register(
cls.strategy_name,
cls,
description=f"{cls.__class__.__name__}",
)
