# 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. import math import os import shutil import sys from collections import ChainMap, OrderedDict, defaultdict from collections.abc import Iterable, Iterator from dataclasses import dataclass from typing import Any, Optional, Union from lightning_utilities.core.apply_func import apply_to_collection from torch import Tensor import pytorch_lightning as pl from lightning_fabric.utilities.data import _set_sampler_epoch from pytorch_lightning.callbacks.progress.rich_progress import _RICH_AVAILABLE from pytorch_lightning.loops.fetchers import _DataFetcher, _DataLoaderIterDataFetcher from pytorch_lightning.loops.loop import _Loop from pytorch_lightning.loops.progress import _BatchProgress from pytorch_lightning.loops.utilities import _no_grad_context, _select_data_fetcher, _verify_dataloader_idx_requirement from pytorch_lightning.trainer import call from pytorch_lightning.trainer.connectors.data_connector import ( _check_dataloader_iterable, _DataLoaderSource, _parse_num_batches, _process_dataloader, _request_dataloader, _resolve_overfit_batches, ) from pytorch_lightning.trainer.connectors.logger_connector.result import _OUT_DICT, _ResultCollection from pytorch_lightning.trainer.states import RunningStage, TrainerFn from pytorch_lightning.utilities.combined_loader import CombinedLoader from pytorch_lightning.utilities.data import has_len_all_ranks from pytorch_lightning.utilities.exceptions import SIGTERMException from pytorch_lightning.utilities.model_helpers import _ModuleMode, is_overridden from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature @dataclass class RestartStage: NONE = "none" RESTARTED_MID_EVALUATION = "restarted_mid_evaluation" class _EvaluationLoop(_Loop): """Top-level loop where validation/testing starts.""" def __init__( self, trainer: "pl.Trainer", trainer_fn: TrainerFn, stage: RunningStage, verbose: bool = True, inference_mode: bool = True, ) -> None: super().__init__(trainer) self.verbose = verbose self.inference_mode = inference_mode self.batch_progress = _BatchProgress() # across dataloaders self._max_batches: list[Union[int, float]] = [] self._results = _ResultCollection(training=False) self._logged_outputs: list[_OUT_DICT] = [] self._has_run: bool = False self._trainer_fn = trainer_fn self._stage = stage self._data_source = _DataLoaderSource(None, f"{stage.dataloader_prefix}_dataloader") self._combined_loader: Optional[CombinedLoader] = None self._data_fetcher: Optional[_DataFetcher] = None self._seen_batches_per_dataloader: defaultdict[int, int] = defaultdict(int) self._last_val_dl_reload_epoch = float("-inf") self._module_mode = _ModuleMode() self._restart_stage = RestartStage.NONE @property def num_dataloaders(self) -> int: """Returns the number of prediction dataloaders.""" combined_loader = self._combined_loader assert combined_loader is not None return len(combined_loader.flattened) @property def max_batches(self) -> list[Union[int, float]]: """The max number of batches to run per dataloader.""" max_batches = self._max_batches if not self.trainer.sanity_checking: return max_batches return [min(self.trainer.num_sanity_val_steps, batches) for batches in max_batches] @property def skip(self) -> bool: """Returns whether the evaluation should be skipped.""" return sum(self.max_batches) == 0 @property def _should_reload_val_dl(self) -> bool: """Check if validation dataloader should be reloaded.""" n_epochs = self.trainer.reload_dataloaders_every_n_epochs return bool(n_epochs and self.trainer.current_epoch - self._last_val_dl_reload_epoch >= n_epochs) @property def _is_sequential(self) -> bool: assert self._combined_loader is not None return self._combined_loader._mode == "sequential" @_no_grad_context def run(self) -> list[_OUT_DICT]: self.setup_data() if self.skip: return [] self.reset() self.on_run_start() data_fetcher = self._data_fetcher assert data_fetcher is not None previous_dataloader_idx = 0 while True: try: if isinstance(data_fetcher, _DataLoaderIterDataFetcher): dataloader_iter = next(data_fetcher) # hook's batch_idx and dataloader_idx arguments correctness cannot be guaranteed in this setting batch = data_fetcher._batch batch_idx = data_fetcher._batch_idx dataloader_idx = data_fetcher._dataloader_idx else: dataloader_iter = None batch, batch_idx, dataloader_idx = next(data_fetcher) if previous_dataloader_idx != dataloader_idx: # the dataloader has changed, notify the logger connector self._store_dataloader_outputs() previous_dataloader_idx = dataloader_idx self.batch_progress.is_last_batch = data_fetcher.done # run step hooks self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter) except StopIteration: # this needs to wrap the `*_step` call too (not just `next`) for `dataloader_iter` support break finally: self.on_iteration_done() self._store_dataloader_outputs() return self.on_run_end() def setup_data(self) -> None: trainer = self.trainer trainer_fn = self._trainer_fn if self._combined_loader is not None and trainer_fn == TrainerFn.FITTING and not self._should_reload_val_dl: return pl_module = trainer.lightning_module limit_batches = trainer.limit_test_batches if trainer.testing else trainer.limit_val_batches hook_name = "test_step" if trainer.testing else "validation_step" if limit_batches == 0 or not is_overridden(hook_name, pl_module): return # store epoch of dataloader reset for reload_dataloaders_every_n_epochs # it should not reload again if it has already reloaded during sanity_check if trainer_fn == TrainerFn.FITTING and ( (trainer.sanity_checking and trainer.fit_loop.epoch_loop._should_check_val_epoch()) or not trainer.sanity_checking ): self._last_val_dl_reload_epoch = trainer.current_epoch stage = self._stage source = self._data_source dataloaders = _request_dataloader(source) trainer.strategy.barrier(f"{stage.dataloader_prefix}_dataloader()") if not isinstance(dataloaders, CombinedLoader): combined_loader = CombinedLoader(dataloaders, "sequential") else: combined_loader = dataloaders if trainer_fn == TrainerFn.FITTING and trainer.overfit_batches > 0: _resolve_overfit_batches(combined_loader, stage) dataloaders = [] for dl in combined_loader.flattened: _check_dataloader_iterable(dl, source, trainer_fn) dl = _process_dataloader(trainer, trainer_fn, stage, dl) dataloaders.append(dl) combined_loader.flattened = dataloaders self._combined_loader = combined_loader allow_zero_length = pl_module.allow_zero_length_dataloader_with_multiple_devices if trainer.datamodule is not None: allow_zero_length |= trainer.datamodule.allow_zero_length_dataloader_with_multiple_devices self._max_batches = [] for dl in combined_loader.flattened: # determine number of batches length = len(dl) if has_len_all_ranks(dl, trainer.strategy, allow_zero_length) else float("inf") limit_batches = getattr(trainer, f"limit_{stage.dataloader_prefix}_batches") num_batches = _parse_num_batches(stage, length, limit_batches) self._max_batches.append(num_batches) # this depends on the data used, so reset it too self._seen_batches_per_dataloader = defaultdict(int) @property def restarted_mid_evaluation(self) -> bool: return self._restart_stage == RestartStage.RESTARTED_MID_EVALUATION def update_restart_stage(self) -> None: if ( self.restarting and self.batch_progress.total.started == self.batch_progress.total.ready and self.batch_progress.total.processed == self.batch_progress.total.started - 1 and self.batch_progress.total.completed == self.batch_progress.total.processed ): self._restart_stage = RestartStage.RESTARTED_MID_EVALUATION else: self._restart_stage = RestartStage.NONE def reset_restart_stage(self) -> None: self._restart_stage = RestartStage.NONE def reset(self) -> None: """Resets the internal state of the loop.""" trainer = self.trainer self._has_run = False self._logged_outputs = [] if not self.restarting: self.batch_progress.reset_on_run() else: self.batch_progress.reset_on_restart() fn = trainer.state.fn assert fn is not None # when restarting, if we are running `validate` or `test` twice, since there's no concept of `max_epochs` we # need to reset the current state when the loop has finished running if fn != TrainerFn.FITTING: self.batch_progress.reset_on_run() assert trainer.state.stage is not None data_fetcher = _select_data_fetcher(trainer, trainer.state.stage) combined_loader = self._combined_loader assert combined_loader is not None if fn == TrainerFn.FITTING: for i, dl in enumerate(combined_loader.flattened): # some users want validation shuffling based on the training progress _set_sampler_epoch(dl, trainer.fit_loop.epoch_progress.current.processed) # set the per-dataloader limits combined_loader.limits = self.max_batches data_fetcher.setup(combined_loader) iter(data_fetcher) # creates the iterator inside the fetcher # add the previous `fetched` value to properly track `is_last_batch` with no prefetching data_fetcher.fetched += self.batch_progress.current.ready data_fetcher._start_profiler = self._on_before_fetch data_fetcher._stop_profiler = self._on_after_fetch self._data_fetcher = data_fetcher def increment_progress_to_evaluation_end(self) -> None: self.setup_data() if self.skip: return self.reset() max_batch = max(self.max_batches) if isinstance(max_batch, float) and math.isinf(max_batch): return max_batch = int(max_batch) if max_batch == -1: return self.batch_progress.increment_by(max_batch, True) def on_run_start(self) -> None: """Runs the ``_on_evaluation_model_eval``, ``_on_evaluation_start`` and ``_on_evaluation_epoch_start`` hooks.""" self._verify_dataloader_idx_requirement() self._on_evaluation_model_eval() self._on_evaluation_start() self._on_evaluation_epoch_start() def on_run_end(self) -> list[_OUT_DICT]: """Runs the ``_on_evaluation_epoch_end`` hook.""" # if `done` returned True before any iterations were done, this won't have been called in `on_advance_end` self.trainer._logger_connector.epoch_end_reached() self.trainer._logger_connector._evaluation_epoch_end() # hook self._on_evaluation_epoch_end() logged_outputs, self._logged_outputs = self._logged_outputs, [] # free memory # include any logged outputs on epoch_end epoch_end_logged_outputs = self.trainer._logger_connector.update_eval_epoch_metrics() all_logged_outputs = dict(ChainMap(*logged_outputs)) # list[dict] -> dict all_logged_outputs.update(epoch_end_logged_outputs) for dl_outputs in logged_outputs: dl_outputs.update(epoch_end_logged_outputs) # log metrics self.trainer._logger_connector.log_eval_end_metrics(all_logged_outputs) # hook self._on_evaluation_end() # enable train mode again self._on_evaluation_model_train() if self.verbose and self.trainer.is_global_zero: self._print_results(logged_outputs, self._stage.value) return logged_outputs def teardown(self) -> None: if self._data_fetcher is not None: self._data_fetcher.teardown() self._data_fetcher = None self._results.cpu() def _on_evaluation_start(self, *args: Any, **kwargs: Any) -> None: """Runs ``on_{validation/test}_start`` hooks.""" trainer = self.trainer hook_name = "on_test_start" if trainer.testing else "on_validation_start" call._call_callback_hooks(trainer, hook_name, *args, **kwargs) call._call_lightning_module_hook(trainer, hook_name, *args, **kwargs) call._call_strategy_hook(trainer, hook_name, *args, **kwargs) def _on_evaluation_model_eval(self) -> None: """Sets model to eval mode.""" trainer = self.trainer hook_name = "on_test_model_eval" if trainer.testing else "on_validation_model_eval" self._module_mode.capture(trainer.lightning_module) call._call_lightning_module_hook(trainer, hook_name) def _on_evaluation_model_train(self) -> None: """Undoes the eval mode.""" trainer = self.trainer hook_name = "on_test_model_train" if trainer.testing else "on_validation_model_train" if is_overridden(hook_name, trainer.lightning_module): call._call_lightning_module_hook(trainer, hook_name) else: self._module_mode.restore(trainer.lightning_module) def _on_evaluation_end(self, *args: Any, **kwargs: Any) -> None: """Runs ``on_{validation/test}_end`` hook.""" trainer = self.trainer hook_name = "on_test_end" if trainer.testing else "on_validation_end" call._call_callback_hooks(trainer, hook_name, *args, **kwargs) call._call_lightning_module_hook(trainer, hook_name, *args, **kwargs) call._call_strategy_hook(trainer, hook_name, *args, **kwargs) # reset the logger connector state trainer._logger_connector.reset_results() def _on_evaluation_epoch_start(self, *args: Any, **kwargs: Any) -> None: """Runs the ``on_{validation/test}_epoch_start`` hooks.""" trainer = self.trainer hook_name = "on_test_epoch_start" if trainer.testing else "on_validation_epoch_start" call._call_callback_hooks(trainer, hook_name, *args, **kwargs) call._call_lightning_module_hook(trainer, hook_name, *args, **kwargs) def _on_evaluation_epoch_end(self) -> None: """Runs ``on_{validation/test}_epoch_end`` hook.""" trainer = self.trainer hook_name = "on_test_epoch_end" if trainer.testing else "on_validation_epoch_end" call._call_callback_hooks(trainer, hook_name) call._call_lightning_module_hook(trainer, hook_name) trainer._logger_connector.on_epoch_end() def _store_dataloader_outputs(self) -> None: trainer = self.trainer trainer._logger_connector.epoch_end_reached() self._logged_outputs.append(trainer._logger_connector.update_eval_epoch_metrics()) def _on_before_fetch(self) -> None: self.trainer.profiler.start(f"[{type(self).__name__}].{self._stage.dataloader_prefix}_next") def _on_after_fetch(self) -> None: # the dataloader_idx cannot be easily included here because it might be different from the index used on # profiler start, since the `__next__` call might use a different iterator self.trainer.profiler.stop(f"[{type(self).__name__}].{self._stage.dataloader_prefix}_next") def _evaluation_step( self, batch: Any, batch_idx: int, dataloader_idx: int, dataloader_iter: Optional[Iterator] ) -> None: """Runs the actual evaluation step together with all the necessary bookkeeping and the hooks tied to it. Args: batch: The current batch to run through the step. batch_idx: The index of the current batch. dataloader_idx: the index of the dataloader producing the current batch. dataloader_iter: The iterator if using this step flavor. """ trainer = self.trainer data_fetcher = self._data_fetcher assert data_fetcher is not None if not (using_dataloader_iter := isinstance(data_fetcher, _DataLoaderIterDataFetcher)): batch = trainer.precision_plugin.convert_input(batch) batch = trainer.lightning_module._on_before_batch_transfer(batch, dataloader_idx=dataloader_idx) batch = call._call_strategy_hook(trainer, "batch_to_device", batch, dataloader_idx=dataloader_idx) # the `_step` methods don't take a batch_idx when `dataloader_iter` is used, but all other hooks still do, # so we need different kwargs hook_kwargs = self._build_kwargs( batch, batch_idx, dataloader_idx if self._is_sequential and self.num_dataloaders > 1 else None ) self.batch_progress.increment_ready() trainer._logger_connector.on_batch_start( batch, dataloader_idx if self._is_sequential and self.num_dataloaders > 1 else None ) hook_name = "on_test_batch_start" if trainer.testing else "on_validation_batch_start" call._call_callback_hooks(trainer, hook_name, *hook_kwargs.values()) call._call_lightning_module_hook(trainer, hook_name, *hook_kwargs.values()) self.batch_progress.increment_started() hook_name = "test_step" if trainer.testing else "validation_step" step_args = ( self._build_step_args_from_hook_kwargs(hook_kwargs, hook_name) if not using_dataloader_iter else (dataloader_iter,) ) output = call._call_strategy_hook(trainer, hook_name, *step_args) self.batch_progress.increment_processed() if using_dataloader_iter: # update the hook kwargs now that the step method might have consumed the iterator batch = data_fetcher._batch batch_idx = data_fetcher._batch_idx dataloader_idx = data_fetcher._dataloader_idx hook_kwargs = self._build_kwargs( batch, batch_idx, dataloader_idx if self._is_sequential and self.num_dataloaders > 1 else None ) hook_name = "on_test_batch_end" if trainer.testing else "on_validation_batch_end" call._call_callback_hooks(trainer, hook_name, output, *hook_kwargs.values()) call._call_lightning_module_hook(trainer, hook_name, output, *hook_kwargs.values()) trainer._logger_connector.on_batch_end() self.batch_progress.increment_completed() if not trainer.sanity_checking: # indicate the loop has run self._has_run = True # log batch metrics trainer._logger_connector.update_eval_step_metrics(self._seen_batches_per_dataloader[dataloader_idx]) self._seen_batches_per_dataloader[dataloader_idx] += 1 if not self.batch_progress.is_last_batch and trainer.received_sigterm: raise SIGTERMException def _build_kwargs(self, batch: Any, batch_idx: int, dataloader_idx: Optional[int]) -> OrderedDict: """Helper method to build the arguments for the current step. Args: batch: the current batch to run through the step. batch_idx: the index of the current batch. dataloader_idx: the index of the dataloader producing the current batch. None if not multiple dataloaders in sequential mode. Returns: the dictionary containing all the keyboard arguments for the step """ step_kwargs = OrderedDict([("batch", batch), ("batch_idx", batch_idx)]) if dataloader_idx is not None: step_kwargs["dataloader_idx"] = dataloader_idx return step_kwargs def _build_step_args_from_hook_kwargs(self, hook_kwargs: OrderedDict, step_hook_name: str) -> tuple: """Helper method to build args for `test_step` or `validation_step`.""" kwargs = hook_kwargs.copy() step_hook_fx = getattr(self.trainer.lightning_module, step_hook_name) if not is_param_in_hook_signature(step_hook_fx, "batch_idx", min_args=2): kwargs.pop("batch_idx", None) return tuple(kwargs.values()) def _verify_dataloader_idx_requirement(self) -> None: trainer = self.trainer step_hook = "test_step" if trainer.testing else "validation_step" batch_start_hook = "on_test_batch_start" if trainer.testing else "on_validation_batch_start" batch_end_hook = "on_test_batch_end" if trainer.testing else "on_validation_batch_end" _verify_dataloader_idx_requirement( (step_hook,), self._is_sequential and self.num_dataloaders > 1 and not isinstance(self._data_fetcher, _DataLoaderIterDataFetcher), self._stage, trainer.lightning_module, ) _verify_dataloader_idx_requirement( (batch_start_hook, batch_end_hook), self._is_sequential and self.num_dataloaders > 1, self._stage, trainer.lightning_module, ) @staticmethod def _get_keys(data: dict) -> Iterable[tuple[str, ...]]: for k, v in data.items(): if isinstance(v, dict): for new_key in apply_to_collection(v, dict, _EvaluationLoop._get_keys): yield (k, *new_key) # this need to be in parenthesis for older python versions else: yield (k,) @staticmethod def _find_value(data: dict, target: Iterable[str]) -> Optional[Any]: target_start, *rest = target if target_start not in data: return None result = data[target_start] if not rest: return result return _EvaluationLoop._find_value(result, rest) @staticmethod def _print_results(results: list[_OUT_DICT], stage: str) -> None: # remove the dl idx suffix results = [{k.split("/dataloader_idx_")[0]: v for k, v in result.items()} for result in results] metrics_paths = {k for keys in apply_to_collection(results, dict, _EvaluationLoop._get_keys) for k in keys} if not metrics_paths: return metrics_strs = [":".join(metric) for metric in metrics_paths] # sort both lists based on metrics_strs metrics_strs, metrics_paths = zip(*sorted(zip(metrics_strs, metrics_paths))) headers = [f"DataLoader {i}" for i in range(len(results))] # fallback is useful for testing of printed output term_size = shutil.get_terminal_size(fallback=(120, 30)).columns or 120 max_length = int(min(max(len(max(metrics_strs, key=len)), len(max(headers, key=len)), 25), term_size / 2)) rows: list[list[Any]] = [[] for _ in metrics_paths] for result in results: for metric, row in zip(metrics_paths, rows): val = _EvaluationLoop._find_value(result, metric) if val is not None: if isinstance(val, Tensor): val = val.item() if val.numel() == 1 else val.tolist() row.append(f"{val}") else: row.append(" ") # keep one column with max length for metrics num_cols = int((term_size - max_length) / max_length) for i in range(0, len(headers), num_cols): table_headers = headers[i : (i + num_cols)] table_rows = [row[i : (i + num_cols)] for row in rows] table_headers.insert(0, f"{stage} Metric".capitalize()) if _RICH_AVAILABLE: from rich import get_console from rich.table import Column, Table columns = [Column(h, justify="center", style="magenta", width=max_length) for h in table_headers] columns[0].style = "cyan" table = Table(*columns) for metric, row in zip(metrics_strs, table_rows): row.insert(0, metric) table.add_row(*row) console = get_console() console.print(table) else: row_format = f"{{:^{max_length}}}" * len(table_headers) half_term_size = int(term_size / 2) try: # some terminals do not support this character if sys.stdout.encoding is not None: "─".encode(sys.stdout.encoding) except UnicodeEncodeError: bar_character = "-" else: bar_character = "─" bar = bar_character * term_size lines = [bar, row_format.format(*table_headers).rstrip(), bar] for metric, row in zip(metrics_strs, table_rows): # deal with column overflow if len(metric) > half_term_size: while len(metric) > half_term_size: row_metric = metric[:half_term_size] metric = metric[half_term_size:] lines.append(row_format.format(row_metric, *row).rstrip()) lines.append(row_format.format(metric, " ").rstrip()) else: lines.append(row_format.format(metric, *row).rstrip()) lines.append(bar) print(os.linesep.join(lines))