Source code for pytorch_lightning.plugins.training_type.tpu_spawn
# Copyright The PyTorch Lightning team.
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# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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# 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 io
import os
import re
import time
from typing import Any, Dict, List, Optional, Union
import torch
import torch.multiprocessing as mp
from torch.nn import Module
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.core.decorators import parameter_validation
from pytorch_lightning.overrides import LightningDistributedModule
from pytorch_lightning.plugins.training_type.ddp_spawn import DDPSpawnPlugin
from pytorch_lightning.trainer.connectors.data_connector import _PatchDataLoader
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities import _OMEGACONF_AVAILABLE, _TPU_AVAILABLE, rank_zero_warn
from pytorch_lightning.utilities.apply_func import apply_to_collection
from pytorch_lightning.utilities.data import has_len
from pytorch_lightning.utilities.distributed import rank_zero_only, ReduceOp
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.seed import reset_seed
from pytorch_lightning.utilities.types import STEP_OUTPUT
if _TPU_AVAILABLE:
import torch_xla.core.xla_env_vars as xenv
import torch_xla.core.xla_model as xm
import torch_xla.distributed.xla_multiprocessing as xmp
from torch_xla.core.xla_model import rendezvous
from torch_xla.distributed.parallel_loader import MpDeviceLoader
else:
xm, xmp, MpDeviceLoader, rendezvous = [None] * 4
if _OMEGACONF_AVAILABLE:
from omegaconf import DictConfig, ListConfig, OmegaConf
[docs]class TPUSpawnPlugin(DDPSpawnPlugin):
"""Plugin for training multiple TPU devices using the :func:`torch.multiprocessing.spawn` method."""
def __init__(self, parallel_devices: Optional[List[int]] = None, debug: bool = False, **_: Any) -> None:
super().__init__(parallel_devices)
self.debug = debug
self.tpu_local_core_rank = 0
self.tpu_global_core_rank = 0
self.start_method = None
@property
def global_rank(self) -> int:
return self.tpu_global_core_rank
@property
def local_rank(self) -> int:
return self.tpu_local_core_rank
@property
def world_size(self) -> int:
return xm.xrt_world_size()
@property
def root_device(self) -> torch.device:
return xm.xla_device()
@staticmethod
def _validate_dataloader(dataloaders: Union[List[DataLoader], DataLoader]) -> None:
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
if not has_len(dataloader):
raise MisconfigurationException(
"TPUs do not currently support IterableDataset objects, the dataset must implement `__len__`."
" HINT: You can mock the length on your dataset to bypass this MisconfigurationException."
)
@staticmethod
def _validate_patched_dataloaders(model: Module) -> None:
"""Validate and fail fast if the dataloaders were passed directly to fit."""
if hasattr(model, "train_dataloader") and isinstance(model.train_dataloader, _PatchDataLoader):
TPUSpawnPlugin._validate_dataloader(model.train_dataloader.dataloader)
if hasattr(model, "val_dataloader") and isinstance(model.val_dataloader, _PatchDataLoader):
TPUSpawnPlugin._validate_dataloader(model.val_dataloader.dataloader)
if hasattr(model, "test_dataloader") and isinstance(model.test_dataloader, _PatchDataLoader):
TPUSpawnPlugin._validate_dataloader(model.test_dataloader.dataloader)
if hasattr(model, "predict_dataloader") and isinstance(model.predict_dataloader, _PatchDataLoader):
TPUSpawnPlugin._validate_dataloader(model.predict_dataloader.dataloader)
[docs] def connect(self, model: "pl.LightningModule") -> None:
TPUSpawnPlugin._validate_patched_dataloaders(model)
self.wrapped_model = xmp.MpModelWrapper(LightningDistributedModule(model))
return super().connect(model)
def create_mp_queue(self):
self.start_method = "fork"
smp = mp.get_context(self.start_method)
self.mp_queue = smp.SimpleQueue()
@property
def distributed_sampler_kwargs(self) -> Dict[str, int]:
return dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())
@property
def is_distributed(self) -> bool:
# HOST_WORLD_SIZE is None outside the xmp.spawn process
return os.getenv(xenv.HOST_WORLD_SIZE, None) and self.world_size != 1
[docs] def process_dataloader(self, dataloader: DataLoader) -> MpDeviceLoader:
TPUSpawnPlugin._validate_dataloader(dataloader)
return MpDeviceLoader(dataloader, self.root_device)
def configure_ddp(self) -> None:
pass
def init_ddp_connection(self, global_rank: int, world_size: int) -> None:
pass
def set_world_ranks(self, process_idx: int = 0) -> None:
pass
def new_process(self, process_idx: int, trainer, mp_queue) -> None:
self.mp_queue = mp_queue
reset_seed()
self.tpu_local_core_rank = xm.get_local_ordinal()
self.tpu_global_core_rank = xm.get_ordinal()
# set warning rank
rank_zero_only.rank = self.global_rank
if self.tpu_global_core_rank != 0 and trainer.progress_bar_callback is not None:
trainer.progress_bar_callback.disable()
self.model_to_device()
trainer.accelerator.setup_optimizers(trainer)
trainer.precision_plugin.connect(self._model, None, None)
self.barrier("pre-run-stage")
results = trainer.run_stage()
self.transfer_distrib_spawn_state_on_fit_end(results)
# https://github.com/pytorch/xla/issues/1801#issuecomment-602799542
self.barrier("end-process")
# https://github.com/pytorch/xla/issues/2190#issuecomment-641665358
if self.local_rank == 0:
time.sleep(2)
[docs] @parameter_validation
def model_to_device(self) -> None:
self.model = self.wrapped_model.to(self.root_device)
[docs] def barrier(self, name: Optional[str] = None) -> None:
if self.is_distributed:
rendezvous(name)
def transfer_distrib_spawn_state_on_fit_end(self, results):
checkpoint_callback = self.lightning_module.trainer.checkpoint_callback
best_model_path = checkpoint_callback.best_model_path if checkpoint_callback else None
# requires to compute the state_dict on all processes in case Metrics are present
state_dict = self.lightning_module.state_dict()
if self.mp_queue is not None:
rank_zero_warn("cleaning up tpu spawn environment...")
# save the last weights
last_path = None
if (
self.lightning_module.trainer.state.fn == TrainerFn.FITTING
and best_model_path is not None
and len(best_model_path) > 0
):
last_path = re.sub(".ckpt", ".tmp_end.ckpt", best_model_path)
self.save(state_dict, last_path)
if self.local_rank == 0:
# todo, pass complete checkpoint as state dictionary
self.mp_queue.put(best_model_path)
self.mp_queue.put(last_path)
self.mp_queue.put(results)
self.lightning_module.add_to_queue(self.mp_queue) # adds the `callback_metrics` to the queue
def save(self, state_dict: Dict, path: str) -> None:
xm.save(state_dict, path)
[docs] def broadcast(self, obj: object, src: int = 0) -> object:
if not self.is_distributed:
return obj
buffer = io.BytesIO()
torch.save(obj, buffer)
data = bytearray(buffer.getbuffer())
data_tensor = torch.tensor(data, device=self.root_device, dtype=torch.float)
data = xm.all_gather(data_tensor)
buffer = io.BytesIO(data.cpu().byte().numpy())
obj = torch.load(buffer)
return obj
[docs] def reduce_boolean_decision(self, decision: bool) -> bool:
decision = torch.tensor(int(decision), device=self.lightning_module.device)
decision = self.reduce(decision, reduce_op="sum")
decision = bool(decision == self.world_size)
return decision
[docs] def reduce(self, output, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None):
if not isinstance(output, torch.Tensor):
output = torch.tensor(output, device=self.lightning_module.device)
_invalid_reduce_op = isinstance(reduce_op, ReduceOp) and reduce_op != ReduceOp.SUM
_invalid_reduce_op_str = isinstance(reduce_op, str) and reduce_op.lower() not in ("sum", "mean", "avg")
if _invalid_reduce_op or _invalid_reduce_op_str:
raise MisconfigurationException(
"Currently, TPUSpawn TrainingTypePlugin only support `sum`, `mean`, `avg` reduce operation."
)
output = xm.mesh_reduce("reduce", output, sum)
if isinstance(reduce_op, str) and reduce_op.lower() in ("avg", "mean"):
output = output / self.world_size
return output
def _close_logger(self, trainer) -> None:
if trainer.logger is not None:
trainer.logger.finalize("success")
@property
def xmp_spawn_kwargs(self):
return {
"args": (self.lightning_module.trainer, self.mp_queue),
"nprocs": len(self.parallel_devices),
"start_method": self.start_method,
}
def start_training(self, trainer) -> None:
# todo: precision pluging is call in accelerator setup and should be moved
if "XLA_USE_BF16" in os.environ:
del os.environ["XLA_USE_BF16"]
self._close_logger(trainer)
xmp.spawn(self.new_process, **self.xmp_spawn_kwargs)
def start_evaluating(self, trainer) -> None:
self._close_logger(trainer)
xmp.spawn(self.new_process, **self.xmp_spawn_kwargs)
def start_predicting(self, trainer) -> None:
xmp.spawn(self.new_process, **self.xmp_spawn_kwargs)
def training_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def validation_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def test_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def predict_step(self, *args, **kwargs):
return self.model(*args, **kwargs)
def training_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
self._pod_progress_bar_force_stdout()
return output
def validation_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
self._pod_progress_bar_force_stdout()
return output
def test_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
self._pod_progress_bar_force_stdout()
return output
def _pod_progress_bar_force_stdout(self) -> None:
# Why is it required? The way `pytorch_xla.distributed` streams logs
# from different vms to the master worker doesn't work well with tqdm
# Ref: https://github.com/pytorch/xla/blob/master/torch_xla/distributed/xla_dist.py#L140
# The print statement seems to force tqdm to flush stdout.
if self.tpu_global_core_rank == 0 and int(os.getenv(xenv.TPUVM_MODE, 0)) == 1:
print()
[docs] def save_checkpoint(self, checkpoint: Dict[str, Any], filepath: str) -> None:
"""Save model/training states as a checkpoint file through state-dump and file-write.
Args:
checkpoint: dict containing model and trainer state
filepath: write-target file's path
"""
# Todo: TypeError: 'mappingproxy' object does not support item assignment
if _OMEGACONF_AVAILABLE:
checkpoint = apply_to_collection(checkpoint, (DictConfig, ListConfig), OmegaConf.to_container)
self.save({k: v for k, v in checkpoint.items() if k != "callbacks"}, filepath)
[docs] def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
"""
Function to gather a tensor from several distributed processes
Args:
tensor: tensor of shape (batch, ...)
group: not available with TPUs
sync_grads: not available with TPUs
Return:
A tensor of shape (world_size, batch, ...)
"""
if isinstance(tensor, torch.Tensor) and tensor.dim() == 0:
tensor = tensor.unsqueeze(0)
return xm.all_gather(tensor)
[docs] def teardown(self) -> None:
# TPU teardown
os.environ.pop("PT_XLA_DEBUG", None)
self.barrier("teardown")
@property
def should_rank_save_checkpoint(self) -> bool:
return self.local_rank == 0
@classmethod
def register_plugins(cls, plugin_registry: Dict) -> None:
plugin_registry.register("tpu_spawn_debug", cls, description="TPUSpawn Plugin with `debug` as True", debug=True)
