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 torch import FloatTensor, Tensor
from torch.utils.data import DataLoader, Sampler
import pytorch_lightning as pl
from pytorch_lightning.overrides.base import _LightningModuleWrapperBase, _LightningPrecisionModuleWrapperBase
from pytorch_lightning.plugins.environments.cluster_environment import ClusterEnvironment
from pytorch_lightning.plugins.io.checkpoint_plugin import CheckpointIO
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.apply_func import apply_to_collection
from pytorch_lightning.utilities.cloud_io import get_filesystem
from pytorch_lightning.utilities.data import _get_dataloader_init_args_and_kwargs
from pytorch_lightning.utilities.enums import PrecisionType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation
from pytorch_lightning.utilities.types import STEP_OUTPUT
if _POPTORCH_AVAILABLE:
import poptorch
else:
poptorch = None
class LightningIPUModule(_LightningModuleWrapperBase):
"""
.. deprecated:: v1.7.0
``LightningIPUModule`` has been deprecated in v1.7.0 and will be removed in v1.9.0.
"""
def __init__(
self, pl_module: Union["pl.LightningModule", _LightningPrecisionModuleWrapperBase], precision: Union[str, int]
) -> None:
rank_zero_deprecation("`LightningIPUModule` has been deprecated in v1.7.0 and will be removed in v1.8.0")
super().__init__(pl_module)
self.precision = precision
def forward(self, *inputs: Any, **kwargs: Any) -> Any:
if self.precision == PrecisionType.HALF:
inputs = self._move_float_tensors_to_half(inputs)
return super().forward(*inputs, **kwargs)
@staticmethod
def batch_to(data: Tensor) -> Tensor:
if torch.is_floating_point(data):
return data.half()
return data
def _move_float_tensors_to_half(self, batch: Any) -> Any:
batch = apply_to_collection(batch, (FloatTensor, torch.cuda.FloatTensor), function=self.batch_to)
return batch
[docs]class IPUStrategy(ParallelStrategy):
"""Plugin for training on IPU devices."""
strategy_name = "ipu_strategy"
def __init__(
self,
accelerator: Optional["pl.accelerators.accelerator.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 in (TrainerFn.FITTING, TrainerFn.TUNING):
# 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 # type: ignore[arg-type]
)
opts = self.training_opts if mode == RunningStage.TRAINING else self.inference_opts
dataloader = poptorch.DataLoader(opts, *dl_args, **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)
assert self.lightning_module is not None
poptorch_model = self.poptorch_models[stage]
self.lightning_module._running_torchscript = True
out = poptorch_model(*args, **kwargs)
self.lightning_module._running_torchscript = False
return out
[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__}",
)
