import collections import inspect import os import re from abc import ABC, abstractmethod from argparse import Namespace from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence import torch import torch.distributed as torch_distrib from torch import Tensor from torch.nn import Module from torch.nn.parallel import DistributedDataParallel from torch.optim.optimizer import Optimizer from torch.utils.data import DataLoader from pytorch_lightning import _logger as log from pytorch_lightning.core.grads import GradInformation from pytorch_lightning.core.hooks import ModelHooks from pytorch_lightning.core.memory import ModelSummary from pytorch_lightning.core.saving import ModelIO, PRIMITIVE_TYPES, ALLOWED_CONFIG_TYPES from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel from pytorch_lightning.utilities import rank_zero_warn from pytorch_lightning.utilities.parsing import AttributeDict, collect_init_args, get_init_args try: import torch_xla.core.xla_model as xm except ImportError: XLA_AVAILABLE = False else: XLA_AVAILABLE = True class LightningModule(ABC, DeviceDtypeModuleMixin, GradInformation, ModelIO, ModelHooks, Module): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.exp_save_path = None #: The current epoch self.current_epoch = 0 #: Total training batches seen across all epochs self.global_step = 0 self.loaded_optimizer_states_dict = {} #: Pointer to the trainer object self.trainer = None #: Pointer to the logger object self.logger = None #: True if using dp self.use_dp = False #: True if using ddp self.use_ddp = False #: True if using ddp2 self.use_ddp2 = False # True if on tpu self.use_tpu = False #: True if using amp self.use_amp = False #: Current dtype self._dtype = torch.float #: device reference self._device = torch.device('cpu') # optionally can be set by user self._example_input_array = None @property def example_input_array(self) -> Any: return self._example_input_array @example_input_array.setter def example_input_array(self, example: Any) -> None: self._example_input_array = example @property def on_gpu(self): """ True if your model is currently running on GPUs. Useful to set flags around the LightningModule for different CPU vs GPU behavior. """ return self.device.type == 'cuda' def print(self, *args, **kwargs) -> None: r""" Prints only from process 0. Use this in any distributed mode to log only once. Args: *args: The thing to print. Will be passed to Python's built-in print function. **kwargs: Will be passed to Python's built-in print function. Example: .. code-block:: python def forward(self, x): self.print(x, 'in forward') """ if self.trainer.is_global_zero: print(*args, **kwargs) @abstractmethod def forward(self, *args, **kwargs): r""" Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define the operations you want to use for prediction (i.e.: on a server or as a feature extractor). Normally you'd call ``self()`` from your :meth:`training_step` method. This makes it easy to write a complex system for training with the outputs you'd want in a prediction setting. You may also find the :func:`~pytorch_lightning.core.decorators.auto_move_data` decorator useful when using the module outside Lightning in a production setting. Args: *args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible. Return: Predicted output Examples: .. code-block:: python # example if we were using this model as a feature extractor def forward(self, x): feature_maps = self.convnet(x) return feature_maps def training_step(self, batch, batch_idx): x, y = batch feature_maps = self(x) logits = self.classifier(feature_maps) # ... return loss # splitting it this way allows model to be used a feature extractor model = MyModelAbove() inputs = server.get_request() results = model(inputs) server.write_results(results) # ------------- # This is in stark contrast to torch.nn.Module where normally you would have this: def forward(self, batch): x, y = batch feature_maps = self.convnet(x) logits = self.classifier(feature_maps) return logits """ def training_step(self, *args, **kwargs) -> Union[ int, Dict[str, Union[Tensor, Dict[str, Tensor]]] ]: r""" Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger. Args: batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]): The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list. batch_idx (int): Integer displaying index of this batch optimizer_idx (int): When using multiple optimizers, this argument will also be present. hiddens(:class:`~torch.Tensor`): Passed in if :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0. Return: Dict with loss key and optional log or progress bar keys. When implementing :meth:`training_step`, return whatever you need in that step: - loss -> tensor scalar **REQUIRED** - progress_bar -> Dict for progress bar display. Must have only tensors - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc) In this step you'd normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something model specific. Examples: .. code-block:: python def training_step(self, batch, batch_idx): x, y, z = batch # implement your own out = self(x) loss = self.loss(out, x) logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS) # if using TestTubeLogger or TensorBoardLogger you can nest scalars logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS) output = { 'loss': loss, # required 'progress_bar': {'training_loss': loss}, # optional (MUST ALL BE TENSORS) 'log': logger_logs } # return a dict return output If you define multiple optimizers, this step will be called with an additional ``optimizer_idx`` parameter. .. code-block:: python # Multiple optimizers (e.g.: GANs) def training_step(self, batch, batch_idx, optimizer_idx): if optimizer_idx == 0: # do training_step with encoder if optimizer_idx == 1: # do training_step with decoder If you add truncated back propagation through time you will also get an additional argument with the hidden states of the previous step. .. code-block:: python # Truncated back-propagation through time def training_step(self, batch, batch_idx, hiddens): # hiddens are the hidden states from the previous truncated backprop step ... out, hiddens = self.lstm(data, hiddens) ... return { "loss": ..., "hiddens": hiddens # remember to detach() this } Notes: The loss value shown in the progress bar is smoothed (averaged) over the last values, so it differs from the actual loss returned in train/validation step. """ rank_zero_warn('`training_step` must be implemented to be used with the Lightning Trainer') def training_end(self, *args, **kwargs): """ Warnings: Deprecated in v0.7.0. Use :meth:`training_step_end` instead. """ def training_epoch_end( self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]] ) -> Dict[str, Dict[str, Tensor]]: """Called at the end of the training epoch with the outputs of all training steps. .. code-block:: python # the pseudocode for these calls train_outs = [] for train_batch in train_data: out = training_step(train_batch) train_outs.append(out) training_epoch_end(train_outs) Args: outputs: List of outputs you defined in :meth:`training_step`, or if there are multiple dataloaders, a list containing a list of outputs for each dataloader. Return: Dict or OrderedDict. May contain the following optional keys: - log (metrics to be added to the logger; only tensors) - progress_bar (dict for progress bar display) - any metric used in a callback (e.g. early stopping). Note: If this method is not overridden, this won't be called. - The outputs here are strictly for logging or progress bar. - If you don't need to display anything, don't return anything. - If you want to manually set current step, you can specify the 'step' key in the 'log' dict. Examples: With a single dataloader: .. code-block:: python def training_epoch_end(self, outputs): train_acc_mean = 0 for output in outputs: train_acc_mean += output['train_acc'] train_acc_mean /= len(outputs) # log training accuracy at the end of an epoch results = { 'log': {'train_acc': train_acc_mean.item()}, 'progress_bar': {'train_acc': train_acc_mean}, } return results With multiple dataloaders, ``outputs`` will be a list of lists. The outer list contains one entry per dataloader, while the inner list contains the individual outputs of each training step for that dataloader. .. code-block:: python def training_epoch_end(self, outputs): train_acc_mean = 0 i = 0 for dataloader_outputs in outputs: for output in dataloader_outputs: train_acc_mean += output['train_acc'] i += 1 train_acc_mean /= i # log training accuracy at the end of an epoch results = { 'log': {'train_acc': train_acc_mean.item(), 'step': self.current_epoch} 'progress_bar': {'train_acc': train_acc_mean}, } return results """ def training_step_end(self, *args, **kwargs) -> Dict[ str, Union[Tensor, Dict[str, Tensor]] ]: """ Use this when training with dp or ddp2 because :meth:`training_step` will operate on only part of the batch. However, this is still optional and only needed for things like softmax or NCE loss. Note: If you later switch to ddp or some other mode, this will still be called so that you don't have to change your code .. code-block:: python # pseudocode sub_batches = split_batches_for_dp(batch) batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches] training_step_end(batch_parts_outputs) Args: batch_parts_outputs: What you return in `training_step` for each batch part. Return: Dict with loss key and optional log or progress bar keys. - loss -> tensor scalar **REQUIRED** - progress_bar -> Dict for progress bar display. Must have only tensors - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc) Examples: .. code-block:: python # WITHOUT training_step_end # if used in DP or DDP2, this batch is 1/num_gpus large def training_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self(x) loss = self.softmax(out) loss = nce_loss(loss) return {'loss': loss} # -------------- # with training_step_end to do softmax over the full batch def training_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self(x) return {'out': out} def training_step_end(self, outputs): # this out is now the full size of the batch out = outputs['out'] # this softmax now uses the full batch size loss = nce_loss(loss) return {'loss': loss} See Also: See the :ref:`multi-gpu-training` guide for more details. """ def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]: r""" Operates on a single batch of data from the validation set. In this step you'd might generate examples or calculate anything of interest like accuracy. .. code-block:: python # the pseudocode for these calls val_outs = [] for val_batch in val_data: out = validation_step(train_batch) val_outs.append(out) validation_epoch_end(val_outs) Args: batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]): The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list. batch_idx (int): The index of this batch dataloader_idx (int): The index of the dataloader that produced this batch (only if multiple val datasets used) Return: Dict or OrderedDict - passed to :meth:`validation_epoch_end`. If you defined :meth:`validation_step_end` it will go to that first. .. code-block:: python # pseudocode of order out = validation_step() if defined('validation_step_end'): out = validation_step_end(out) out = validation_epoch_end(out) .. code-block:: python # if you have one val dataloader: def validation_step(self, batch, batch_idx) # if you have multiple val dataloaders: def validation_step(self, batch, batch_idx, dataloader_idx) Examples: .. code-block:: python # CASE 1: A single validation dataset def validation_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # all optional... # return whatever you need for the collation function validation_epoch_end output = OrderedDict({ 'val_loss': loss_val, 'val_acc': torch.tensor(val_acc), # everything must be a tensor }) # return an optional dict return output If you pass in multiple val datasets, validation_step will have an additional argument. .. code-block:: python # CASE 2: multiple validation datasets def validation_step(self, batch, batch_idx, dataset_idx): # dataset_idx tells you which dataset this is. Note: If you don't need to validate you don't need to implement this method. Note: When the :meth:`validation_step` is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of validation, the model goes back to training mode and gradients are enabled. """ def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]: """ Use this when validating with dp or ddp2 because :meth:`validation_step` will operate on only part of the batch. However, this is still optional and only needed for things like softmax or NCE loss. Note: If you later switch to ddp or some other mode, this will still be called so that you don't have to change your code. .. code-block:: python # pseudocode sub_batches = split_batches_for_dp(batch) batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches] validation_step_end(batch_parts_outputs) Args: batch_parts_outputs: What you return in :meth:`validation_step` for each batch part. Return: Dict or OrderedDict - passed to the :meth:`validation_epoch_end` method. Examples: .. code-block:: python # WITHOUT validation_step_end # if used in DP or DDP2, this batch is 1/num_gpus large def validation_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self(x) loss = self.softmax(out) loss = nce_loss(loss) return {'loss': loss} # -------------- # with validation_step_end to do softmax over the full batch def validation_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self(x) return {'out': out} def validation_epoch_end(self, outputs): # this out is now the full size of the batch out = outputs['out'] # this softmax now uses the full batch size loss = nce_loss(loss) return {'loss': loss} See Also: See the :ref:`multi-gpu-training` guide for more details. """ def validation_end(self, outputs): """ Warnings: Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead. Will be removed in 1.0.0. """ def validation_epoch_end( self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]] ) -> Dict[str, Dict[str, Tensor]]: """ Called at the end of the validation epoch with the outputs of all validation steps. .. code-block:: python # the pseudocode for these calls val_outs = [] for val_batch in val_data: out = validation_step(val_batch) val_outs.append(out) validation_epoch_end(val_outs) Args: outputs: List of outputs you defined in :meth:`validation_step`, or if there are multiple dataloaders, a list containing a list of outputs for each dataloader. Return: Dict or OrderedDict. May have the following optional keys: - progress_bar (dict for progress bar display; only tensors) - log (dict of metrics to add to logger; only tensors). Note: If you didn't define a :meth:`validation_step`, this won't be called. - The outputs here are strictly for logging or progress bar. - If you don't need to display anything, don't return anything. - If you want to manually set current step, you can specify the 'step' key in the 'log' dict. Examples: With a single dataloader: .. code-block:: python def validation_epoch_end(self, outputs): val_acc_mean = 0 for output in outputs: val_acc_mean += output['val_acc'] val_acc_mean /= len(outputs) tqdm_dict = {'val_acc': val_acc_mean.item()} # show val_acc in progress bar but only log val_loss results = { 'progress_bar': tqdm_dict, 'log': {'val_acc': val_acc_mean.item()} } return results With multiple dataloaders, `outputs` will be a list of lists. The outer list contains one entry per dataloader, while the inner list contains the individual outputs of each validation step for that dataloader. .. code-block:: python def validation_epoch_end(self, outputs): val_acc_mean = 0 i = 0 for dataloader_outputs in outputs: for output in dataloader_outputs: val_acc_mean += output['val_acc'] i += 1 val_acc_mean /= i tqdm_dict = {'val_acc': val_acc_mean.item()} # show val_loss and val_acc in progress bar but only log val_loss results = { 'progress_bar': tqdm_dict, 'log': {'val_acc': val_acc_mean.item(), 'step': self.current_epoch} } return results """ def test_step(self, *args, **kwargs) -> Dict[str, Tensor]: r""" Operates on a single batch of data from the test set. In this step you'd normally generate examples or calculate anything of interest such as accuracy. .. code-block:: python # the pseudocode for these calls test_outs = [] for test_batch in test_data: out = test_step(test_batch) test_outs.append(out) test_epoch_end(test_outs) Args: batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]): The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list. batch_idx (int): The index of this batch. dataloader_idx (int): The index of the dataloader that produced this batch (only if multiple test datasets used). Return: Dict or OrderedDict - passed to the :meth:`test_epoch_end` method. If you defined :meth:`test_step_end` it will go to that first. .. code-block:: python # if you have one test dataloader: def test_step(self, batch, batch_idx) # if you have multiple test dataloaders: def test_step(self, batch, batch_idx, dataloader_idx) Examples: .. code-block:: python # CASE 1: A single test dataset def test_step(self, batch, batch_idx): x, y = batch # implement your own out = self(x) loss = self.loss(out, y) # log 6 example images # or generated text... or whatever sample_imgs = x[:6] grid = torchvision.utils.make_grid(sample_imgs) self.logger.experiment.add_image('example_images', grid, 0) # calculate acc labels_hat = torch.argmax(out, dim=1) val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0) # all optional... # return whatever you need for the collation function test_epoch_end output = OrderedDict({ 'val_loss': loss_val, 'val_acc': torch.tensor(val_acc), # everything must be a tensor }) # return an optional dict return output If you pass in multiple validation datasets, :meth:`test_step` will have an additional argument. .. code-block:: python # CASE 2: multiple test datasets def test_step(self, batch, batch_idx, dataset_idx): # dataset_idx tells you which dataset this is. Note: If you don't need to validate you don't need to implement this method. Note: When the :meth:`test_step` is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled. """ def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]: """ Use this when testing with dp or ddp2 because :meth:`test_step` will operate on only part of the batch. However, this is still optional and only needed for things like softmax or NCE loss. Note: If you later switch to ddp or some other mode, this will still be called so that you don't have to change your code. .. code-block:: python # pseudocode sub_batches = split_batches_for_dp(batch) batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches] test_step_end(batch_parts_outputs) Args: batch_parts_outputs: What you return in :meth:`test_step` for each batch part. Return: Dict or OrderedDict - passed to the :meth:`test_epoch_end`. Examples: .. code-block:: python # WITHOUT test_step_end # if used in DP or DDP2, this batch is 1/num_gpus large def test_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self(x) loss = self.softmax(out) loss = nce_loss(loss) return {'loss': loss} # -------------- # with test_step_end to do softmax over the full batch def test_step(self, batch, batch_idx): # batch is 1/num_gpus big x, y = batch out = self(x) return {'out': out} def test_step_end(self, outputs): # this out is now the full size of the batch out = outputs['out'] # this softmax now uses the full batch size loss = nce_loss(loss) return {'loss': loss} See Also: See the :ref:`multi-gpu-training` guide for more details. """ def test_end(self, outputs): """ Warnings: Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead. Will be removed in 1.0.0. """ def test_epoch_end( self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]] ) -> Dict[str, Dict[str, Tensor]]: """ Called at the end of a test epoch with the output of all test steps. .. code-block:: python # the pseudocode for these calls test_outs = [] for test_batch in test_data: out = test_step(test_batch) test_outs.append(out) test_epoch_end(test_outs) Args: outputs: List of outputs you defined in :meth:`test_step_end`, or if there are multiple dataloaders, a list containing a list of outputs for each dataloader Return: Dict or OrderedDict: Dict has the following optional keys: - progress_bar -> Dict for progress bar display. Must have only tensors. - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc). Note: If you didn't define a :meth:`test_step`, this won't be called. - The outputs here are strictly for logging or progress bar. - If you don't need to display anything, don't return anything. - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict Examples: With a single dataloader: .. code-block:: python def test_epoch_end(self, outputs): test_acc_mean = 0 for output in outputs: test_acc_mean += output['test_acc'] test_acc_mean /= len(outputs) tqdm_dict = {'test_acc': test_acc_mean.item()} # show test_loss and test_acc in progress bar but only log test_loss results = { 'progress_bar': tqdm_dict, 'log': {'test_acc': test_acc_mean.item()} } return results With multiple dataloaders, `outputs` will be a list of lists. The outer list contains one entry per dataloader, while the inner list contains the individual outputs of each test step for that dataloader. .. code-block:: python def test_epoch_end(self, outputs): test_acc_mean = 0 i = 0 for dataloader_outputs in outputs: for output in dataloader_outputs: test_acc_mean += output['test_acc'] i += 1 test_acc_mean /= i tqdm_dict = {'test_acc': test_acc_mean.item()} # show test_loss and test_acc in progress bar but only log test_loss results = { 'progress_bar': tqdm_dict, 'log': {'test_acc': test_acc_mean.item(), 'step': self.current_epoch} } return results """ def configure_ddp( self, model: 'LightningModule', device_ids: List[int] ) -> DistributedDataParallel: r""" Override to init DDP in your own way or with your own wrapper. The only requirements are that: 1. On a validation batch the call goes to ``model.validation_step``. 2. On a training batch the call goes to ``model.training_step``. 3. On a testing batch, the call goes to ``model.test_step``.+ Args: model: the :class:`LightningModule` currently being optimized. device_ids: the list of GPU ids. Return: DDP wrapped model Examples: .. code-block:: python # default implementation used in Trainer def configure_ddp(self, model, device_ids): # Lightning DDP simply routes to test_step, val_step, etc... model = LightningDistributedDataParallel( model, device_ids=device_ids, find_unused_parameters=True ) return model """ model = LightningDistributedDataParallel( model, device_ids=device_ids, find_unused_parameters=True ) return model def _init_slurm_connection(self) -> None: """ Sets up environment variables necessary for pytorch distributed communications based on slurm environment. """ # use slurm job id for the port number # guarantees unique ports across jobs from same grid search try: # use the last 4 numbers in the job id as the id default_port = os.environ['SLURM_JOB_ID'] default_port = default_port[-4:] # all ports should be in the 10k+ range default_port = int(default_port) + 15000 except Exception: default_port = 12910 # if user gave a port number, use that one instead try: default_port = os.environ['MASTER_PORT'] except Exception: os.environ['MASTER_PORT'] = str(default_port) # figure out the root node addr try: root_node = os.environ['SLURM_NODELIST'].split(' ')[0] except Exception: root_node = '127.0.0.1' root_node = self.trainer.resolve_root_node_address(root_node) os.environ['MASTER_ADDR'] = root_node def init_ddp_connection( self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True ) -> None: """ Override to define your custom way of setting up a distributed environment. Lightning's implementation uses env:// init by default and sets the first node as root for SLURM managed cluster. Args: global_rank: The global process idx. world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus). is_slurm_managing_tasks: is cluster managed by SLURM. """ if is_slurm_managing_tasks: self._init_slurm_connection() if 'MASTER_ADDR' not in os.environ: rank_zero_warn("MASTER_ADDR environment variable is not defined. Set as localhost") os.environ['MASTER_ADDR'] = '127.0.0.1' log.debug(f"MASTER_ADDR: {os.environ['MASTER_ADDR']}") if 'MASTER_PORT' not in os.environ: rank_zero_warn("MASTER_PORT environment variable is not defined. Set as 12910") os.environ['MASTER_PORT'] = '12910' log.debug(f"MASTER_PORT: {os.environ['MASTER_PORT']}") if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size: rank_zero_warn(f"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) " f"is not equal to the computed world size ({world_size}). Ignored.") torch_backend = "nccl" if self.trainer.on_gpu else "gloo" log.info(f"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank+1}/{world_size}") torch_distrib.init_process_group(torch_backend, rank=global_rank, world_size=world_size) def configure_apex( self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str ) -> Tuple['LightningModule', List[Optimizer]]: r""" Override to init AMP your own way. Must return a model and list of optimizers. Args: amp: pointer to amp library object. model: pointer to current :class:`LightningModule`. optimizers: list of optimizers passed in :meth:`configure_optimizers`. amp_level: AMP mode chosen ('O1', 'O2', etc...) Return: Apex wrapped model and optimizers Examples: .. code-block:: python # Default implementation used by Trainer. def configure_apex(self, amp, model, optimizers, amp_level): model, optimizers = amp.initialize( model, optimizers, opt_level=amp_level, ) return model, optimizers """ model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level) return model, optimizers def configure_optimizers(self) -> Optional[Union[ Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List] ]]: r""" Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you'd need one. But in the case of GANs or similar you might have multiple. Return: Any of these 6 options. - Single optimizer. - List or Tuple - List of optimizers. - Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict). - Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler' key which value is a single LR scheduler or lr_dict. - Tuple of dictionaries as described, with an optional 'frequency' key. - None - Fit will run without any optimizer. Note: The 'frequency' value is an int corresponding to the number of sequential batches optimized with the specific optimizer. It should be given to none or to all of the optimizers. There is a difference between passing multiple optimizers in a list, and passing multiple optimizers in dictionaries with a frequency of 1: In the former case, all optimizers will operate on the given batch in each optimization step. In the latter, only one optimizer will operate on the given batch at every step. The lr_dict is a dictionary which contains scheduler and its associated configuration. It has five keys. The default configuration is shown below. .. code-block:: python { 'scheduler': lr_scheduler, # The LR schduler 'interval': 'epoch', # The unit of the scheduler's step size 'frequency': 1, # The frequency of the scheduler 'reduce_on_plateau': False, # For ReduceLROnPlateau scheduler 'monitor': 'val_loss' # Metric to monitor } If user only provides LR schedulers, then their configuration will set to default as shown above. Examples: .. code-block:: python # most cases def configure_optimizers(self): opt = Adam(self.parameters(), lr=1e-3) return opt # multiple optimizer case (e.g.: GAN) def configure_optimizers(self): generator_opt = Adam(self.model_gen.parameters(), lr=0.01) disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02) return generator_opt, disriminator_opt # example with learning rate schedulers def configure_optimizers(self): generator_opt = Adam(self.model_gen.parameters(), lr=0.01) disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02) discriminator_sched = CosineAnnealing(discriminator_opt, T_max=10) return [generator_opt, disriminator_opt], [discriminator_sched] # example with step-based learning rate schedulers def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_disc.parameters(), lr=0.02) gen_sched = {'scheduler': ExponentialLR(gen_opt, 0.99), 'interval': 'step'} # called after each training step dis_sched = CosineAnnealing(discriminator_opt, T_max=10) # called every epoch return [gen_opt, dis_opt], [gen_sched, dis_sched] # example with optimizer frequencies # see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1 # https://arxiv.org/abs/1704.00028 def configure_optimizers(self): gen_opt = Adam(self.model_gen.parameters(), lr=0.01) dis_opt = Adam(self.model_disc.parameters(), lr=0.02) n_critic = 5 return ( {'optimizer': dis_opt, 'frequency': n_critic}, {'optimizer': gen_opt, 'frequency': 1} ) Note: Some things to know: - Lightning calls ``.backward()`` and ``.step()`` on each optimizer and learning rate scheduler as needed. - If you use 16-bit precision (``precision=16``), Lightning will automatically handle the optimizers for you. - If you use multiple optimizers, :meth:`training_step` will have an additional ``optimizer_idx`` parameter. - If you use LBFGS Lightning handles the closure function automatically for you. - If you use multiple optimizers, gradients will be calculated only for the parameters of current optimizer at each training step. - If you need to control how often those optimizers step or override the default ``.step()`` schedule, override the :meth:`optimizer_step` hook. - If you only want to call a learning rate scheduler every ``x`` step or epoch, or want to monitor a custom metric, you can specify these in a lr_dict: .. code-block:: python { 'scheduler': lr_scheduler, 'interval': 'step', # or 'epoch' 'monitor': 'val_f1', 'frequency': x, } """ rank_zero_warn('`configure_optimizers` must be implemented to be used with the Lightning Trainer') def optimizer_step( self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable] = None, on_tpu: bool = False, using_native_amp: bool = False, using_lbfgs: bool = False, ) -> None: r""" Override this method to adjust the default way the :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer. By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example once per optimizer. Args: epoch: Current epoch batch_idx: Index of current batch optimizer: A PyTorch optimizer optimizer_idx: If you used multiple optimizers this indexes into that list. second_order_closure: closure for second order methods on_tpu: true if TPU backward is required using_native_amp: True if using native amp using_lbfgs: True if the matching optimizer is lbfgs Examples: .. code-block:: python # DEFAULT def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx, second_order_closure, on_tpu, using_native_amp, using_lbfgs): optimizer.step() # Alternating schedule for optimizer steps (i.e.: GANs) def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx, second_order_closure, on_tpu, using_native_amp, using_lbfgs): # update generator opt every 2 steps if optimizer_idx == 0: if batch_idx % 2 == 0 : optimizer.step() optimizer.zero_grad() # update discriminator opt every 4 steps if optimizer_idx == 1: if batch_idx % 4 == 0 : optimizer.step() optimizer.zero_grad() # ... # add as many optimizers as you want Here's another example showing how to use this for more advanced things such as learning rate warm-up: .. code-block:: python # learning rate warm-up def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx, second_order_closure, on_tpu, using_native_amp, using_lbfgs): # warm up lr if self.trainer.global_step < 500: lr_scale = min(1., float(self.trainer.global_step + 1) / 500.) for pg in optimizer.param_groups: pg['lr'] = lr_scale * self.learning_rate # update params optimizer.step() optimizer.zero_grad() Note: If you also override the :meth:`~pytorch_lightning.core.hooks.ModelHooks.on_before_zero_grad` model hook don't forget to add the call to it before ``optimizer.zero_grad()`` yourself. """ if on_tpu: xm.optimizer_step(optimizer) elif using_native_amp: self.trainer.scaler.step(optimizer) elif using_lbfgs: optimizer.step(second_order_closure) else: optimizer.step() def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int): optimizer.zero_grad() def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list: r""" When using truncated backpropagation through time, each batch must be split along the time dimension. Lightning handles this by default, but for custom behavior override this function. Args: batch: Current batch split_size: The size of the split Return: List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated back propagation through time. The default implementation splits root level Tensors and Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length. Examples: .. code-block:: python def tbptt_split_batch(self, batch, split_size): splits = [] for t in range(0, time_dims[0], split_size): batch_split = [] for i, x in enumerate(batch): if isinstance(x, torch.Tensor): split_x = x[:, t:t + split_size] elif isinstance(x, collections.Sequence): split_x = [None] * len(x) for batch_idx in range(len(x)): split_x[batch_idx] = x[batch_idx][t:t + split_size] batch_split.append(split_x) splits.append(batch_split) return splits Note: Called in the training loop after :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start` if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0. Each returned batch split is passed separately to :meth:`training_step`. """ time_dims = [len(x[0]) for x in batch if isinstance(x, (torch.Tensor, collections.Sequence))] assert len(time_dims) >= 1, "Unable to determine batch time dimension" assert all(x == time_dims[0] for x in time_dims), "Batch time dimension length is ambiguous" splits = [] for t in range(0, time_dims[0], split_size): batch_split = [] for i, x in enumerate(batch): if isinstance(x, torch.Tensor): split_x = x[:, t:t + split_size] elif isinstance(x, collections.Sequence): split_x = [None] * len(x) for batch_idx in range(len(x)): split_x[batch_idx] = x[batch_idx][t:t + split_size] batch_split.append(split_x) splits.append(batch_split) return splits def prepare_data(self) -> None: """ Use this to download and prepare data. .. warning:: DO NOT set state to the model (use `setup` instead) since this is NOT called on every GPU in DDP/TPU Example:: def prepare_data(self): # good download_data() tokenize() etc() # bad self.split = data_split self.some_state = some_other_state() In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)): 1. Once per node. This is the default and is only called on LOCAL_RANK=0. 2. Once in total. Only called on GLOBAL_RANK=0. Example:: # DEFAULT # called once per node on LOCAL_RANK=0 of that node Trainer(prepare_data_per_node=True) # call on GLOBAL_RANK=0 (great for shared file systems) Trainer(prepare_data_per_node=False) This is called before requesting the dataloaders: .. code-block:: python model.prepare_data() if ddp/tpu: init() model.setup(stage) model.train_dataloader() model.val_dataloader() model.test_dataloader() """ def train_dataloader(self) -> DataLoader: """ Implement a PyTorch DataLoader for training. Return: Single PyTorch :class:`~torch.utils.data.DataLoader`. The dataloader you return will not be called every epoch unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``. For data processing use the following pattern: - download in :meth:`prepare_data` - process and split in :meth:`setup` However, the above are only necessary for distributed processing. .. warning:: do not assign state in prepare_data - :meth:`~pytorch_lightning.trainer.Trainer.fit` - ... - :meth:`prepare_data` - :meth:`setup` - :meth:`train_dataloader` Note: Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself. Example: .. code-block:: python def train_dataloader(self): transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))]) dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform, download=True) loader = torch.utils.data.DataLoader( dataset=dataset, batch_size=self.batch_size, shuffle=True ) return loader """ rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer') def tng_dataloader(self): # todo: remove in v1.0.0 """ Warnings: Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0. """ output = self.train_dataloader() rank_zero_warn("`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0." " and this method will be removed in v1.0.0", DeprecationWarning) return output def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]: r""" Implement one or multiple PyTorch DataLoaders for testing. The dataloader you return will not be called every epoch unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``. For data processing use the following pattern: - download in :meth:`prepare_data` - process and split in :meth:`setup` However, the above are only necessary for distributed processing. .. warning:: do not assign state in prepare_data - :meth:`~pytorch_lightning.trainer.Trainer.fit` - ... - :meth:`prepare_data` - :meth:`setup` - :meth:`train_dataloader` - :meth:`val_dataloader` - :meth:`test_dataloader` Note: Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself. Return: Single or multiple PyTorch DataLoaders. Example: .. code-block:: python def test_dataloader(self): transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))]) dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True) loader = torch.utils.data.DataLoader( dataset=dataset, batch_size=self.batch_size, shuffle=False ) return loader Note: If you don't need a test dataset and a :meth:`test_step`, you don't need to implement this method. """ def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]: r""" Implement one or multiple PyTorch DataLoaders for validation. The dataloader you return will not be called every epoch unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``. It's recommended that all data downloads and preparation happen in :meth:`prepare_data`. - :meth:`~pytorch_lightning.trainer.Trainer.fit` - ... - :meth:`prepare_data` - :meth:`train_dataloader` - :meth:`val_dataloader` - :meth:`test_dataloader` Note: Lightning adds the correct sampler for distributed and arbitrary hardware There is no need to set it yourself. Return: Single or multiple PyTorch DataLoaders. Examples: .. code-block:: python def val_dataloader(self): transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))]) dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True) loader = torch.utils.data.DataLoader( dataset=dataset, batch_size=self.batch_size, shuffle=False ) return loader # can also return multiple dataloaders def val_dataloader(self): return [loader_a, loader_b, ..., loader_n] Note: If you don't need a validation dataset and a :meth:`validation_step`, you don't need to implement this method. Note: In the case where you return multiple validation dataloaders, the :meth:`validation_step` will have an argument ``dataset_idx`` which matches the order here. """ def summarize(self, mode: str = ModelSummary.MODE_DEFAULT) -> ModelSummary: model_summary = ModelSummary(self, mode=mode) log.info('\n' + str(model_summary)) return model_summary def freeze(self) -> None: r""" Freeze all params for inference. Example: .. code-block:: python model = MyLightningModule(...) model.freeze() """ for param in self.parameters(): param.requires_grad = False self.eval() def unfreeze(self) -> None: """ Unfreeze all parameters for training. .. code-block:: python model = MyLightningModule(...) model.unfreeze() """ for param in self.parameters(): param.requires_grad = True self.train() def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None: r""" Called by Lightning to restore your model. If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this. Args: checkpoint: Loaded checkpoint Example: .. code-block:: python def on_load_checkpoint(self, checkpoint): # 99% of the time you don't need to implement this method self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save'] Note: Lightning auto-restores global step, epoch, and train state including amp scaling. There is no need for you to restore anything regarding training. """ def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: r""" Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save. Args: checkpoint: Checkpoint to be saved Example: .. code-block:: python def on_save_checkpoint(self, checkpoint): # 99% of use cases you don't need to implement this method checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object Note: Lightning saves all aspects of training (epoch, global step, etc...) including amp scaling. There is no need for you to store anything about training. """ def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]: r""" Additional items to be displayed in the progress bar. Return: Dictionary with the items to be displayed in the progress bar. """ # call .item() only once but store elements without graphs running_train_loss = self.trainer.running_loss.mean() avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN') tqdm_dict = { 'loss': '{:.3f}'.format(avg_training_loss) } if self.trainer.truncated_bptt_steps is not None: tqdm_dict['split_idx'] = self.trainer.split_idx if self.trainer.logger is not None and self.trainer.logger.version is not None: tqdm_dict['v_num'] = self.trainer.logger.version return tqdm_dict def get_tqdm_dict(self) -> Dict[str, Union[int, str]]: """ Additional items to be displayed in the progress bar. Return: Dictionary with the items to be displayed in the progress bar. Warning: Deprecated since v0.7.3. Use :meth:`get_progress_bar_dict` instead. """ rank_zero_warn("`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3" " and this method will be removed in v1.0.0", DeprecationWarning) return self.get_progress_bar_dict() @classmethod def _auto_collect_arguments(cls, frame=None) -> Tuple[Dict, Dict]: """ Collect all module arguments in the current constructor and all child constructors. The child constructors are all the ``__init__`` methods that reach the current class through (chained) ``super().__init__()`` calls. Args: frame: instance frame Returns: self_arguments: arguments dictionary of the first instance parents_arguments: arguments dictionary of the parent's instances """ if not frame: frame = inspect.currentframe() frame_args = collect_init_args(frame.f_back, []) self_arguments = frame_args[-1] # set module_arguments in child self_arguments = self_arguments parents_arguments = {} # add all arguments from parents for args in frame_args[:-1]: parents_arguments.update(args) return self_arguments, parents_arguments def save_hyperparameters(self, *args, frame=None) -> None: """Save all model arguments. Args: args: single object of `dict`, `NameSpace` or `OmegaConf` or string names or argumenst from class `__init__` >>> from collections import OrderedDict >>> class ManuallyArgsModel(LightningModule): ... def __init__(self, arg1, arg2, arg3): ... super().__init__() ... # manually assine arguments ... self.save_hyperparameters('arg1', 'arg3') ... def forward(self, *args, **kwargs): ... ... >>> model = ManuallyArgsModel(1, 'abc', 3.14) >>> model.hparams "arg1": 1 "arg3": 3.14 >>> class AutomaticArgsModel(LightningModule): ... def __init__(self, arg1, arg2, arg3): ... super().__init__() ... # equivalent automatic ... self.save_hyperparameters() ... def forward(self, *args, **kwargs): ... ... >>> model = AutomaticArgsModel(1, 'abc', 3.14) >>> model.hparams "arg1": 1 "arg2": abc "arg3": 3.14 >>> class SingleArgModel(LightningModule): ... def __init__(self, params): ... super().__init__() ... # manually assign single argument ... self.save_hyperparameters(params) ... def forward(self, *args, **kwargs): ... ... >>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14)) >>> model.hparams "p1": 1 "p2": abc "p3": 3.14 """ if not frame: frame = inspect.currentframe().f_back init_args = get_init_args(frame) assert init_args, 'failed to inspect the self init' if not args: hp = init_args self._hparams_name = 'kwargs' if hp else None else: isx_non_str = [i for i, arg in enumerate(args) if not isinstance(arg, str)] if len(isx_non_str) == 1: hp = args[isx_non_str[0]] cand_names = [k for k, v in init_args.items() if v == hp] self._hparams_name = cand_names[0] if cand_names else None else: hp = {arg: init_args[arg] for arg in args if isinstance(arg, str)} self._hparams_name = 'kwargs' # `hparams` are expected here if hp: self._set_hparams(hp) def _set_hparams(self, hp: Union[dict, Namespace, str]) -> None: if isinstance(hp, Namespace): hp = vars(hp) if isinstance(hp, dict): hp = AttributeDict(hp) elif isinstance(hp, PRIMITIVE_TYPES): raise ValueError(f'Primitives {PRIMITIVE_TYPES} are not allowed.') elif not isinstance(hp, ALLOWED_CONFIG_TYPES): raise ValueError(f'Unsupported config type of {type(hp)}.') if isinstance(hp, dict) and isinstance(self.hparams, dict): self.hparams.update(hp) else: self._hparams = hp @property def hparams(self) -> Union[AttributeDict, str]: if not hasattr(self, '_hparams'): self._hparams = AttributeDict() return self._hparams @hparams.setter def hparams(self, hp: Union[dict, Namespace, Any]): hparams_assignment_name = self.__get_hparams_assignment_variable() self._hparams_name = hparams_assignment_name self._set_hparams(hp) def __get_hparams_assignment_variable(self): """ looks at the code of the class to figure out what the user named self.hparams this only happens when the user explicitly sets self.hparams """ try: class_code = inspect.getsource(self.__class__) lines = class_code.split('\n') for line in lines: line = re.sub(r"\s+", "", line, flags=re.UNICODE) if '.hparams=' in line: return line.split('=')[1] except Exception as e: return 'hparams' return None