From b419e4d8298fbb31a5ae5be9730b952393b7a7be Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Thu, 18 Nov 2021 11:11:42 -0800 Subject: [PATCH 01/44] several recommended fixes for lightning_examples/finetuning-scheduler/finetuning-scheduler.py Co-authored-by: thomas chaton --- .../finetuning-scheduler/.meta.yml | 20 ++ .../finetuning-scheduler.py | 326 ++++++++++++++++++ .../text-transformers/text-transformers.py | 2 +- 3 files changed, 347 insertions(+), 1 deletion(-) create mode 100644 lightning_examples/finetuning-scheduler/.meta.yml create mode 100644 lightning_examples/finetuning-scheduler/finetuning-scheduler.py diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml new file mode 100644 index 000000000..6a2a38ce8 --- /dev/null +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -0,0 +1,20 @@ +title: Finetuning Scheduler +author: PL team +created: 2021-01-31 +updated: 2021-06-21 +license: CC BY-SA +build: 1 +tags: + - Text +description: | + This notebook will use HuggingFace's `datasets` library to get data, which will be wrapped in a `LightningDataModule`. + Then, we write a class to perform text classification on any dataset from the [GLUE Benchmark](https://gluebenchmark.com/). + (We just show CoLA and MRPC due to constraint on compute/disk) +requirements: + - transformers + - datasets + - scipy + - scikit-learn +accelerator: + - CPU + - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py new file mode 100644 index 000000000..b97a1012e --- /dev/null +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -0,0 +1,326 @@ +# %% +from datetime import datetime +from typing import Optional + +import datasets +import torch +from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything +from torch.utils.data import DataLoader +from transformers import ( + AdamW, + AutoConfig, + AutoModelForSequenceClassification, + AutoTokenizer, + get_linear_schedule_with_warmup, +) + +AVAIL_GPUS = min(1, torch.cuda.device_count()) + +# %% [markdown] +# ## Training BERT with Lightning + +# %% [markdown] +# ### Lightning DataModule for GLUE + + +# %% +class GLUEDataModule(LightningDataModule): + + task_text_field_map = { + "cola": ["sentence"], + "sst2": ["sentence"], + "mrpc": ["sentence1", "sentence2"], + "qqp": ["question1", "question2"], + "stsb": ["sentence1", "sentence2"], + "mnli": ["premise", "hypothesis"], + "qnli": ["question", "sentence"], + "rte": ["sentence1", "sentence2"], + "wnli": ["sentence1", "sentence2"], + "ax": ["premise", "hypothesis"], + } + + glue_task_num_labels = { + "cola": 2, + "sst2": 2, + "mrpc": 2, + "qqp": 2, + "stsb": 1, + "mnli": 3, + "qnli": 2, + "rte": 2, + "wnli": 2, + "ax": 3, + } + + loader_columns = [ + "datasets_idx", + "input_ids", + "token_type_ids", + "attention_mask", + "start_positions", + "end_positions", + "labels", + ] + + def __init__( + self, + model_name_or_path: str, + task_name: str = "mrpc", + max_seq_length: int = 128, + train_batch_size: int = 32, + eval_batch_size: int = 32, + **kwargs, + ): + super().__init__() + self.model_name_or_path = model_name_or_path + self.task_name = task_name + self.max_seq_length = max_seq_length + self.train_batch_size = train_batch_size + self.eval_batch_size = eval_batch_size + + self.text_fields = self.task_text_field_map[task_name] + self.num_labels = self.glue_task_num_labels[task_name] + self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + + def setup(self, stage: str): + self.dataset = datasets.load_dataset("glue", self.task_name) + + for split in self.dataset.keys(): + self.dataset[split] = self.dataset[split].map( + self.convert_to_features, + batched=True, + remove_columns=["label"], + ) + self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] + self.dataset[split].set_format(type="torch", columns=self.columns) + + self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] + + def prepare_data(self): + datasets.load_dataset("glue", self.task_name) + AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + + def train_dataloader(self): + return DataLoader(self.dataset["train"], batch_size=self.train_batch_size) + + def val_dataloader(self): + if len(self.eval_splits) == 1: + return DataLoader(self.dataset["validation"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits] + + def test_dataloader(self): + if len(self.eval_splits) == 1: + return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits] + + def convert_to_features(self, example_batch, indices=None): + + # Either encode single sentence or sentence pairs + if len(self.text_fields) > 1: + texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) + else: + texts_or_text_pairs = example_batch[self.text_fields[0]] + + # Tokenize the text/text pairs + features = self.tokenizer.batch_encode_plus( + texts_or_text_pairs, max_length=self.max_seq_length, pad_to_max_length=True, truncation=True + ) + + # Rename label to labels to make it easier to pass to model forward + features["labels"] = example_batch["label"] + + return features + + +# %% [markdown] +# **You could use this datamodule with standalone PyTorch if you wanted...** + +# %% +dm = GLUEDataModule("distilbert-base-uncased") +dm.prepare_data() +dm.setup("fit") +next(iter(dm.train_dataloader())) + +# %% [markdown] +# ### Transformer LightningModule + + +# %% +class GLUETransformer(LightningModule): + def __init__( + self, + model_name_or_path: str, + num_labels: int, + task_name: str, + learning_rate: float = 2e-5, + adam_epsilon: float = 1e-8, + warmup_steps: int = 0, + weight_decay: float = 0.0, + train_batch_size: int = 32, + eval_batch_size: int = 32, + eval_splits: Optional[list] = None, + **kwargs, + ): + super().__init__() + + self.save_hyperparameters() + + self.config = AutoConfig.from_pretrained(model_name_or_path, num_labels=num_labels) + self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=self.config) + self.metric = datasets.load_metric( + "glue", self.hparams.task_name, experiment_id=datetime.now().strftime("%d-%m-%Y_%H-%M-%S") + ) + + def forward(self, **inputs): + return self.model(**inputs) + + def training_step(self, batch, batch_idx): + outputs = self(**batch) + loss = outputs[0] + return loss + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + outputs = self(**batch) + val_loss, logits = outputs[:2] + + if self.hparams.num_labels >= 1: + preds = torch.argmax(logits, axis=1) + elif self.hparams.num_labels == 1: + preds = logits.squeeze() + + labels = batch["labels"] + + return {"loss": val_loss, "preds": preds, "labels": labels} + + def validation_epoch_end(self, outputs): + if self.hparams.task_name == "mnli": + for i, output in enumerate(outputs): + # matched or mismatched + split = self.hparams.eval_splits[i].split("_")[-1] + preds = torch.cat([x["preds"] for x in output]).detach().cpu().numpy() + labels = torch.cat([x["labels"] for x in output]).detach().cpu().numpy() + loss = torch.stack([x["loss"] for x in output]).mean() + self.log(f"val_loss_{split}", loss, prog_bar=True) + split_metrics = { + f"{k}_{split}": v for k, v in self.metric.compute(predictions=preds, references=labels).items() + } + self.log_dict(split_metrics, prog_bar=True) + return loss + + preds = torch.cat([x["preds"] for x in outputs]).detach().cpu().numpy() + labels = torch.cat([x["labels"] for x in outputs]).detach().cpu().numpy() + loss = torch.stack([x["loss"] for x in outputs]).mean() + self.log("val_loss", loss, prog_bar=True) + self.log_dict(self.metric.compute(predictions=preds, references=labels), prog_bar=True) + return loss + + def setup(self, stage=None) -> None: + if stage != "fit": + return + # Get dataloader by calling it - train_dataloader() is called after setup() by default + # train_loader = self.train_dataloader() + train_loader = self.trainer.datamodule.train_dataloader() + # Calculate total steps + tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus) + ab_size = self.trainer.accumulate_grad_batches * float(self.trainer.max_epochs) + self.total_steps = (len(train_loader.dataset) // tb_size) // ab_size + + def configure_optimizers(self): + """Prepare optimizer and schedule (linear warmup and decay)""" + model = self.model + no_decay = ["bias", "LayerNorm.weight"] + optimizer_grouped_parameters = [ + { + "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], + "weight_decay": self.hparams.weight_decay, + }, + { + "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], + "weight_decay": 0.0, + }, + ] + optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon) + + scheduler = get_linear_schedule_with_warmup( + optimizer, + num_warmup_steps=self.hparams.warmup_steps, + num_training_steps=self.total_steps, + ) + scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1} + return [optimizer], [scheduler] + + +# %% [markdown] +# ## Training + +# %% [markdown] +# ### CoLA +# +# See an interactive view of the +# CoLA dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=cola) + +# %% +seed_everything(42) + +dm = GLUEDataModule(model_name_or_path="albert-base-v2", task_name="cola") +dm.setup("fit") +model = GLUETransformer( + model_name_or_path="albert-base-v2", + num_labels=dm.num_labels, + eval_splits=dm.eval_splits, + task_name=dm.task_name, +) + +trainer = Trainer(max_epochs=1, gpus=AVAIL_GPUS) +trainer.fit(model, dm) + +# %% [markdown] +# ### MRPC +# +# See an interactive view of the +# MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mrpc) + +# %% +seed_everything(42) + +dm = GLUEDataModule( + model_name_or_path="distilbert-base-cased", + task_name="mrpc", +) +dm.setup("fit") +model = GLUETransformer( + model_name_or_path="distilbert-base-cased", + num_labels=dm.num_labels, + eval_splits=dm.eval_splits, + task_name=dm.task_name, +) + +trainer = Trainer(max_epochs=3, gpus=AVAIL_GPUS) +trainer.fit(model, dm) + +# %% [markdown] +# ### MNLI +# +# - The MNLI dataset is huge, so we aren't going to bother trying to train on it here. +# - We will skip over training and go straight to validation. +# +# See an interactive view of the +# MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mnli) + +# %% +dm = GLUEDataModule( + model_name_or_path="distilbert-base-cased", + task_name="mnli", +) +dm.setup("fit") +model = GLUETransformer( + model_name_or_path="distilbert-base-cased", + num_labels=dm.num_labels, + eval_splits=dm.eval_splits, + task_name=dm.task_name, +) + +trainer = Trainer(gpus=AVAIL_GPUS, progress_bar_refresh_rate=20) +trainer.validate(model, dm.val_dataloader()) diff --git a/lightning_examples/text-transformers/text-transformers.py b/lightning_examples/text-transformers/text-transformers.py index 57786f010..d14a2e397 100644 --- a/lightning_examples/text-transformers/text-transformers.py +++ b/lightning_examples/text-transformers/text-transformers.py @@ -220,7 +220,7 @@ def setup(self, stage=None) -> None: if stage != "fit": return # Get dataloader by calling it - train_dataloader() is called after setup() by default - train_loader = self.trainer.datamodule.train_dataloader() + train_loader = self.train_dataloader() # Calculate total steps tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus) From 441104ad3e3cc8fbc2d92ac835f7862ca84c93b6 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Mon, 29 Nov 2021 17:22:25 -0800 Subject: [PATCH 02/44] notebook-based fts tutorial and example --- .../finetuning-scheduler/.meta.yml | 19 +- .../finetuning-scheduler/emphasized_yaml.svg | 1176 +++++++++++++++++ .../finetuning-scheduler.py | 704 +++++++--- .../fts_explicit_loss_anim.gif | Bin 0 -> 302052 bytes .../side_by_side_yaml.svg | 754 +++++++++++ 5 files changed, 2437 insertions(+), 216 deletions(-) create mode 100644 lightning_examples/finetuning-scheduler/emphasized_yaml.svg create mode 100644 lightning_examples/finetuning-scheduler/fts_explicit_loss_anim.gif create mode 100644 lightning_examples/finetuning-scheduler/side_by_side_yaml.svg diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 6a2a38ce8..33296fba5 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -1,20 +1,19 @@ title: Finetuning Scheduler -author: PL team -created: 2021-01-31 -updated: 2021-06-21 +author: Dan Dale +created: 2021-11-26 +updated: 2021-11-26 license: CC BY-SA build: 1 tags: - - Text + - finetuning description: | - This notebook will use HuggingFace's `datasets` library to get data, which will be wrapped in a `LightningDataModule`. - Then, we write a class to perform text classification on any dataset from the [GLUE Benchmark](https://gluebenchmark.com/). - (We just show CoLA and MRPC due to constraint on compute/disk) + This notebook introduces the FinetuningScheduler callback and demonstrates the use of FinetuningScheduler to finetune + a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task + of [SuperGLUE](https://super.gluebenchmark.com/) with iterative earlystopping defined according to a user-specified + schedule. It uses HuggingFace's `datasets` and `transformers`` libraries to retrieve the relevant benchmark data and + foundational model weights. requirements: - transformers - datasets - - scipy - - scikit-learn accelerator: - - CPU - GPU diff --git a/lightning_examples/finetuning-scheduler/emphasized_yaml.svg b/lightning_examples/finetuning-scheduler/emphasized_yaml.svg new file mode 100644 index 000000000..eda7e7891 --- /dev/null +++ b/lightning_examples/finetuning-scheduler/emphasized_yaml.svg @@ -0,0 +1,1176 @@ + + + + + + + + image/svg+xml + + + + + + + + + diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index b97a1012e..9669cb632 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,57 +1,274 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.1 +# kernelspec: +# display_name: 'Python 3.7.11 64-bit (''pldev_tutorials'': conda)' +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Scheduled Finetuning +# +#
+# +# The ``FinetuningScheduler`` callback enables multi-phase, +# scheduled finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize +# foundational model knowledge retention while allowing (typically upper layers of) the model to +# optimally adapt to new tasks during transfer learning [1, 2, 3](#f1) +# +#
+# +# ``FinetuningScheduler`` orchestrates the gradual unfreezing +# of models via a finetuning schedule that is either implicitly generated (the default) or explicitly provided by the user +# (more computationally efficient). Finetuning phase transitions are driven by +# ``FTSEarlyStopping`` criteria (a multi-phase +# extension of ``EarlyStopping``), user-specified epoch transitions or a composition of the two (the default mode). +# A ``FinetuningScheduler`` training session completes when the +# final phase of the schedule has its stopping criteria met. See +# the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/latest/extensions/generated/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. +# +# + +# %% [markdown] +# ## Basic Usage +# +# If no finetuning schedule is user-provided, ``FinetuningScheduler`` will generate a +# [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, using default ``FTSEarlyStopping`` and ``FTSCheckpoint`` callbacks with ``monitor=val_loss``. +# +# ```python +# from pytorch_lightning import Trainer +# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# trainer = Trainer(callbacks=[FinetuningScheduler()]) +# ``` + +# %% [markdown] +# ## The Default Finetuning Schedule +# +# Schedule definition is facilitated via the ``gen_ft_schedule`` method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as +# desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will often be less computationally efficient than a user-defined finetuning schedule but can often serve as a good baseline for subsquent explicit schedule refinement and will marginally outperform many explicit schedules. + +# %% [markdown] +# ## Specifying a Finetuning Schedule +# +# To specify a finetuning schedule, it's convenient to first generate the default schedule and then alter the thawed/unfrozen parameter groups associated with each finetuning phase as desired. Finetuning phases are zero-indexed and executed in ascending order. +# +# 1. Generate the default schedule to ``Trainer.log_dir`` with the name +# (``lightning_module.__class__.__name__``)_ft_schedule.yaml +# +# ```python +# from pytorch_lightning import Trainer +# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# trainer = Trainer(callbacks=[FinetuningScheduler(gen_ft_sched_only=True)]) +# ``` +# +# 2. Alter the schedule as desired. +# +# +# +# 3. Once the finetuning schedule has been altered as desired, pass it to +# ``FinetuningScheduler`` to commence scheduled training: +# +# ```python +# from pytorch_lightning import Trainer +# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# +# trainer = Trainer(callbacks=[FinetuningScheduler(ft_schedule="/path/to/my/schedule/my_schedule.yaml")]) +# ``` + +# %% [markdown] +# ## EarlyStopping and Epoch-Driven Phase Transition Criteria +# +# +# By default, ``FTSEarlyStopping`` and epoch-driven +# transition criteria are composed. If a ``max_transition_epoch`` is specified for a given phase, the next finetuning phase will begin at that epoch unless ``FTSEarlyStopping`` criteria are met first. +# If ``FinetuningScheduler.epoch_transitions_only`` is ``True``, ``FTSEarlyStopping`` will not be used +# and transitions will be exclusively epoch-driven. +# +# +#
+# +# **Tip:** Use of regex expressions can be convenient for specifying more complex schedules: +# +# +# +#
+# +# +# +# The end-to-end example in this notebook ([Scheduled Finetuning For SuperGLUE](#superglue)) uses ``FinetuningScheduler`` in explicit mode to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). + +# %% [markdown] +# ## Resuming Scheduled Finetuning Training Sessions +# +# Resumption of scheduled finetuning training is identical to the continuation of +# [other training sessions](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) with the caveat that the provided checkpoint must have been saved by a ``FinetuningScheduler`` session. +# ``FinetuningScheduler`` uses ``FTSCheckpoint`` (an extension of ``ModelCheckpoint``) to maintain schedule state with special metadata. +# +# +# ```python +# from pytorch_lightning import Trainer +# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# trainer = Trainer(callbacks=[FinetuningScheduler()], resume_from_checkpoint="some/path/to/my_checkpoint.ckpt") +# ``` +# +# Training will resume at the depth/level of the provided checkpoint according the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions. +# +# By default (``FinetuningScheduler.restore_best`` is ``True``), ``FinetuningScheduler`` will attempt to restore the best available checkpoint before finetuning depth transitions. +# +# ```python +# trainer = Trainer( +# callbacks=[FinetuningScheduler(new_incarnation_mode=True)], +# resume_from_checkpoint="some/path/to/my_kth_best_checkpoint.ckpt", +# ) +# ``` +# +# To handle the edge case wherein one is resuming scheduled finetuning from a non-best checkpoint and the previous best checkpoints may not be accessible, setting ``FinetuningScheduler.new_incarnation_mode`` to +# ``True`` as above will re-intialize the checkpoint state with a new best checkpoint at the resumption depth. + +# %% [markdown] +#
+# +# **Note:** Currently, _FinetuningScheduler_ only supports the following _TrainingTypePlugins_: +# +# - ``DDPPlugin`` +# - ``DDPShardedPlugin`` +# - ``DDPSpawnPlugin`` +# - ``DDPSpawnShardedPlugin`` +# - ``DataParallelPlugin`` +# - ``SingleDevicePlugin`` +# +#
+ +# %% [markdown] +#
+# +# # Scheduled Finetuning For SuperGLUE +# +# The following example demonstrates the use of ``FinetuningScheduler`` to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. +# +# ``FinetuningScheduler`` can be used to achieve non-trivial model performance improvements in both implicit and explicit scheduling contexts at an also non-trivial computational cost. + # %% +import os +import warnings from datetime import datetime -from typing import Optional +from typing import Any, Dict, List, Optional, Tuple, Union +from importlib import import_module +import logging -import datasets import torch -from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything from torch.utils.data import DataLoader -from transformers import ( - AdamW, - AutoConfig, - AutoModelForSequenceClassification, - AutoTokenizer, - get_linear_schedule_with_warmup, -) -AVAIL_GPUS = min(1, torch.cuda.device_count()) +import pytorch_lightning as pl +from pytorch_lightning.utilities import rank_zero_warn +from pytorch_lightning.utilities.cli import CALLBACK_REGISTRY, _Registry +from pytorch_lightning.utilities.exceptions import MisconfigurationException +from pytorch_lightning.loggers.tensorboard import TensorBoardLogger -# %% [markdown] -# ## Training BERT with Lightning +import datasets +from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer -# %% [markdown] -# ### Lightning DataModule for GLUE + +# %% +# a couple helper functions to prepare code to work with the forthcoming hub and user module registry +MOCK_HUB_REGISTRY = _Registry() + + +def module_hub_mock(key: str, require_fqn: bool = False) -> List: + if key.lower() == "finetuningscheduler": + mod = import_module("pytorch_lightning.callbacks.finetuning_scheduler") + MOCK_HUB_REGISTRY.register_classes(mod, pl.callbacks.Callback) + else: + raise MisconfigurationException(f"user module key '{key}' not found") + registered_list = [] + # make registered class available by unqualified class name by default + if not require_fqn: + for n, c in MOCK_HUB_REGISTRY.items(): + globals()[f"{n}"] = c + registered_list = ", ".join([n for n in MOCK_HUB_REGISTRY.names]) + else: + registered_list = ", ".join( + [c.__module__ + "." + c.__name__ for c in MOCK_HUB_REGISTRY.classes] + ) + print(f"Imported and registered the following callbacks: {registered_list}") + + +def instantiate_registered_class( + init: Dict[str, Any], args: Optional[Union[Any, Tuple[Any, ...]]] = None +) -> Any: + """Instantiates a class with the given args and init. Accepts class definitions in the form + of a "class_path" or "callback_key" associated with a _Registry + + Args: + init: Dict of the form {"class_path":... or "callback_key":..., "init_args":...}. + args: Positional arguments required for instantiation. + + Returns: + The instantiated class object. + """ + kwargs = init.get("init_args", {}) + if args and not isinstance(args, tuple): + args = (args,) + shortcircuit_local = False if "." in init["class_path"] else True + class_module, class_name, args_class = None, None, None + if init.get("class_path", None): + if not shortcircuit_local: + class_module, class_name = init["class_path"].rsplit(".", 1) + else: # class is expected to be locally defined + args_class = globals()[init["class_path"]] + elif init.get("callback_key", None): + callback_path = CALLBACK_REGISTRY.get( + init["callback_key"], None + ) or MOCK_HUB_REGISTRY.get(init["callback_key"], None) + assert callback_path, MisconfigurationException( + f'specified callback_key {init["callback_key"]} has not been registered' + ) + class_module, class_name = callback_path.__module__, callback_path.__name__ + else: + raise MisconfigurationException( + "Neither a class_path nor callback_key were included in a configuration that" + "requires one" + ) + if not shortcircuit_local: + module = __import__(class_module, fromlist=[class_name]) + args_class = getattr(module, class_name) + return args_class(**kwargs) if not args else args_class(*args, **kwargs) + + +# %% +# load the pl extension module we want to use. This will import all necessary callbacks. +module_hub_mock("finetuningscheduler") +# set notebook-level variables +AVAIL_GPUS = min(1, torch.cuda.device_count()) +TASK_NUM_LABELS = {"boolq": 2, "rte": 2} +DEFAULT_TASK = "rte" + +# narrow our logging to adapt it for a notebook environment +for l_key in logging.Logger.manager.loggerDict.keys(): + if "pytorch_lightning" in l_key: + logging.getLogger(l_key).setLevel("INFO") + else: + logging.getLogger(l_key).setLevel("CRITICAL") +pl_logger = logging.getLogger("pytorch_lightning") +pl_logger.removeHandler(pl_logger.handlers[0]) +rz_logger = logging.getLogger("pytorch_lightning.utilities.distributed") +rz_logger.addHandler(logging.StreamHandler()) +rz_logger.handlers[0].setLevel("INFO") # %% -class GLUEDataModule(LightningDataModule): - - task_text_field_map = { - "cola": ["sentence"], - "sst2": ["sentence"], - "mrpc": ["sentence1", "sentence2"], - "qqp": ["question1", "question2"], - "stsb": ["sentence1", "sentence2"], - "mnli": ["premise", "hypothesis"], - "qnli": ["question", "sentence"], - "rte": ["sentence1", "sentence2"], - "wnli": ["sentence1", "sentence2"], - "ax": ["premise", "hypothesis"], - } - - glue_task_num_labels = { - "cola": 2, - "sst2": 2, - "mrpc": 2, - "qqp": 2, - "stsb": 1, - "mnli": 3, - "qnli": 2, - "rte": 2, - "wnli": 2, - "ax": 3, - } +class RteBoolqDataModule(pl.LightningDataModule): + """A ``LightningDataModule`` for using either the RTE or BoolQ SuperGLUE Hugging Face datasets""" + task_text_field_map = {"rte": ["premise", "hypothesis"], "boolq": ["question", "passage"]} loader_columns = [ "datasets_idx", "input_ids", @@ -61,117 +278,167 @@ class GLUEDataModule(LightningDataModule): "end_positions", "labels", ] + # ignore warnings related tokenizers_parallelism/DataLoader parallelism tradeoff and + # expected logging behavior + for warnf in [".*does not have many workers*", ".*The number of training samples.*"]: + warnings.filterwarnings("ignore", warnf) def __init__( self, model_name_or_path: str, - task_name: str = "mrpc", + task_name: str = DEFAULT_TASK, + prep_on_init: bool = False, max_seq_length: int = 128, train_batch_size: int = 32, eval_batch_size: int = 32, - **kwargs, + pin_memory: bool = False, + tokenizers_parallelism: bool = True, + num_workers: int = 0, ): super().__init__() self.model_name_or_path = model_name_or_path - self.task_name = task_name + self.task_name = task_name if task_name in TASK_NUM_LABELS.keys() else DEFAULT_TASK self.max_seq_length = max_seq_length self.train_batch_size = train_batch_size self.eval_batch_size = eval_batch_size - - self.text_fields = self.task_text_field_map[task_name] - self.num_labels = self.glue_task_num_labels[task_name] - self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) - - def setup(self, stage: str): - self.dataset = datasets.load_dataset("glue", self.task_name) - + self.tokenizers_parallelism = tokenizers_parallelism + self.dataloader_kwargs = {"num_workers": num_workers, "pin_memory": pin_memory} + self.text_fields = self.task_text_field_map[self.task_name] + self.num_labels = TASK_NUM_LABELS[self.task_name] + os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.tokenizers_parallelism else "false" + self.tokenizer = AutoTokenizer.from_pretrained( + self.model_name_or_path, use_fast=True, local_files_only=False + ) + if ( + prep_on_init + ): # useful if one wants to load datasets as soon as the ``LightningDataModule`` is + # instantiated + self.prepare_data() + self.setup("fit") + + def setup(self, stage): + self.dataset = datasets.load_dataset("super_glue", self.task_name) for split in self.dataset.keys(): self.dataset[split] = self.dataset[split].map( - self.convert_to_features, - batched=True, - remove_columns=["label"], + self.convert_to_features, batched=True, remove_columns=["label"] ) - self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] + self.columns = [ + c for c in self.dataset[split].column_names if c in self.loader_columns + ] self.dataset[split].set_format(type="torch", columns=self.columns) self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] def prepare_data(self): - datasets.load_dataset("glue", self.task_name) - AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + # N.B. PL calls prepare_data from a single process (rank 0) so do not use it to assign + # state (e.g. self.x=y) + datasets.load_dataset("super_glue", self.task_name) def train_dataloader(self): - return DataLoader(self.dataset["train"], batch_size=self.train_batch_size) + return DataLoader( + self.dataset["train"], batch_size=self.train_batch_size, **self.dataloader_kwargs + ) def val_dataloader(self): if len(self.eval_splits) == 1: - return DataLoader(self.dataset["validation"], batch_size=self.eval_batch_size) + return DataLoader( + self.dataset["validation"], + batch_size=self.eval_batch_size, + **self.dataloader_kwargs, + ) elif len(self.eval_splits) > 1: - return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits] + return [ + DataLoader( + self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs + ) + for x in self.eval_splits + ] def test_dataloader(self): if len(self.eval_splits) == 1: - return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size) + return DataLoader( + self.dataset["test"], batch_size=self.eval_batch_size, **self.dataloader_kwargs + ) elif len(self.eval_splits) > 1: - return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits] - - def convert_to_features(self, example_batch, indices=None): - - # Either encode single sentence or sentence pairs - if len(self.text_fields) > 1: - texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) - else: - texts_or_text_pairs = example_batch[self.text_fields[0]] - + return [ + DataLoader( + self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs + ) + for x in self.eval_splits + ] + + def convert_to_features(self, example_batch): + text_pairs = list( + zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]) + ) # Tokenize the text/text pairs features = self.tokenizer.batch_encode_plus( - texts_or_text_pairs, max_length=self.max_seq_length, pad_to_max_length=True, truncation=True + text_pairs, max_length=self.max_seq_length, padding="longest", truncation=True ) - # Rename label to labels to make it easier to pass to model forward features["labels"] = example_batch["label"] - return features -# %% [markdown] -# **You could use this datamodule with standalone PyTorch if you wanted...** - # %% -dm = GLUEDataModule("distilbert-base-uncased") -dm.prepare_data() -dm.setup("fit") -next(iter(dm.train_dataloader())) - -# %% [markdown] -# ### Transformer LightningModule - +class RteBoolqModule(pl.LightningModule): + """A ``LightningModule`` that can be used to finetune a foundational + model on either the RTE or BoolQ SuperGLUE tasks using Hugging Face + implementations of a given model and the `SuperGLUE Hugging Face dataset. + """ -# %% -class GLUETransformer(LightningModule): def __init__( self, model_name_or_path: str, - num_labels: int, - task_name: str, - learning_rate: float = 2e-5, - adam_epsilon: float = 1e-8, - warmup_steps: int = 0, - weight_decay: float = 0.0, - train_batch_size: int = 32, - eval_batch_size: int = 32, - eval_splits: Optional[list] = None, - **kwargs, + optimizer_init: Dict[str, Any], + lr_scheduler_init: Dict[str, Any], + pl_lrs_cfg: Optional[Dict[str, Any]] = None, + model_cfg: Optional[Dict[str, Any]] = None, + task_name: str = DEFAULT_TASK, + experiment_tag: str = "default", + plot_liveloss: bool = False, ): + """ + Args: + model_name_or_path (str): Path to pretrained model or identifier from https://huggingface.co/models + optimizer_init (Dict[str, Any]): The desired optimizer configuration. + lr_scheduler_init (Dict[str, Any]): The desired learning rate scheduler config + pl_lrs_cfg (Optional[Dict[str, Any]]): Defines custom overrides of pytorch lightning lr_scheduler defaults + defined in ``_get_default_scheduler_config`` + model_cfg (Optional[Dict[str, Any]], optional): Defines overrides of the default model config. Defaults to + ``None``. + task_name (str, optional): The SuperGLUE task to execute, one of ``'rte'``, ``'boolq'``. Defaults to "rte". + experiment_tag (str, optional): The tag to use for the experiment and tensorboard logs. Defaults to + "default". + """ super().__init__() - + self.optimizer_init = optimizer_init + self.lr_scheduler_init = lr_scheduler_init + self.plot_liveloss = plot_liveloss + self.pl_lrs_cfg = pl_lrs_cfg or {} + if task_name in TASK_NUM_LABELS.keys(): + self.task_name = task_name + else: + self.task_name = DEFAULT_TASK + rank_zero_warn( + f"Invalid task_name '{task_name}'. Proceeding with the default task: '{DEFAULT_TASK}'" + ) + self.num_labels = TASK_NUM_LABELS[self.task_name] self.save_hyperparameters() - - self.config = AutoConfig.from_pretrained(model_name_or_path, num_labels=num_labels) - self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=self.config) + self.experiment_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{experiment_tag}" + self.model_cfg = model_cfg or {} + conf = AutoConfig.from_pretrained( + model_name_or_path, num_labels=self.num_labels, local_files_only=False + ) + self.model = AutoModelForSequenceClassification.from_pretrained( + model_name_or_path, config=conf + ) + self.model.config.update(self.model_cfg) # apply model config overrides self.metric = datasets.load_metric( - "glue", self.hparams.task_name, experiment_id=datetime.now().strftime("%d-%m-%Y_%H-%M-%S") + "super_glue", self.task_name, experiment_id=self.experiment_id ) + self.no_decay = ["bias", "LayerNorm.weight"] + self.finetuningscheduler_callback = None def forward(self, **inputs): return self.model(**inputs) @@ -181,146 +448,171 @@ def training_step(self, batch, batch_idx): loss = outputs[0] return loss + def training_epoch_end(self, outputs: List[Any]) -> None: + loss = torch.stack([x["loss"] for x in outputs]).mean() + self.log("train_loss", loss, prog_bar=True, sync_dist=True) + if self.finetuningscheduler_callback: + self.log("finetuning_schedule_depth", self.finetuningscheduler_callback.curr_depth) + def validation_step(self, batch, batch_idx, dataloader_idx=0): outputs = self(**batch) val_loss, logits = outputs[:2] - if self.hparams.num_labels >= 1: + if self.num_labels >= 1: preds = torch.argmax(logits, axis=1) - elif self.hparams.num_labels == 1: + elif self.num_labels == 1: preds = logits.squeeze() labels = batch["labels"] - return {"loss": val_loss, "preds": preds, "labels": labels} def validation_epoch_end(self, outputs): - if self.hparams.task_name == "mnli": - for i, output in enumerate(outputs): - # matched or mismatched - split = self.hparams.eval_splits[i].split("_")[-1] - preds = torch.cat([x["preds"] for x in output]).detach().cpu().numpy() - labels = torch.cat([x["labels"] for x in output]).detach().cpu().numpy() - loss = torch.stack([x["loss"] for x in output]).mean() - self.log(f"val_loss_{split}", loss, prog_bar=True) - split_metrics = { - f"{k}_{split}": v for k, v in self.metric.compute(predictions=preds, references=labels).items() - } - self.log_dict(split_metrics, prog_bar=True) - return loss - preds = torch.cat([x["preds"] for x in outputs]).detach().cpu().numpy() labels = torch.cat([x["labels"] for x in outputs]).detach().cpu().numpy() loss = torch.stack([x["loss"] for x in outputs]).mean() - self.log("val_loss", loss, prog_bar=True) - self.log_dict(self.metric.compute(predictions=preds, references=labels), prog_bar=True) + self.log("val_loss", loss, prog_bar=True, sync_dist=True) + metric_dict = self.metric.compute(predictions=preds, references=labels) + self.log_dict(metric_dict, prog_bar=True, sync_dist=True) return loss - def setup(self, stage=None) -> None: - if stage != "fit": - return - # Get dataloader by calling it - train_dataloader() is called after setup() by default - # train_loader = self.train_dataloader() - train_loader = self.trainer.datamodule.train_dataloader() - # Calculate total steps - tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus) - ab_size = self.trainer.accumulate_grad_batches * float(self.trainer.max_epochs) - self.total_steps = (len(train_loader.dataset) // tb_size) // ab_size + def init_pgs(self) -> List[Dict]: + """Initialize the parameter groups. Used to ensure weight_decay is not applied + to our specified bias parameters when we initialize the optimizer. - def configure_optimizers(self): - """Prepare optimizer and schedule (linear warmup and decay)""" - model = self.model - no_decay = ["bias", "LayerNorm.weight"] - optimizer_grouped_parameters = [ + Returns: + List[Dict]: A list of parameter group dictionaries. + """ + pgs = [ { - "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], - "weight_decay": self.hparams.weight_decay, + "params": [ + p + for n, p in self.model.named_parameters() + if not any(nd in n for nd in self.no_decay) and p.requires_grad + ], + "weight_decay": self.optimizer_init["init_args"]["weight_decay"], }, { - "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], + "params": [ + p + for n, p in self.model.named_parameters() + if any(nd in n for nd in self.no_decay) and p.requires_grad + ], "weight_decay": 0.0, }, ] - optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon) + return pgs - scheduler = get_linear_schedule_with_warmup( - optimizer, - num_warmup_steps=self.hparams.warmup_steps, - num_training_steps=self.total_steps, - ) - scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1} + def configure_optimizers(self): + # the phase 0 parameters will have been set to require gradients during setup + # you can initialize the optimizer with a simple requires.grad filter as is often done, + # but in this case we pass a list of parameter groups to ensure weight_decay is + # not applied to the bias parameter (for completeness, in this case it won't make much + # performance difference) + optimizer = instantiate_registered_class(args=self.init_pgs(), init=self.optimizer_init) + scheduler = { + "scheduler": instantiate_registered_class( + args=optimizer, init=self.lr_scheduler_init + ), + **self.pl_lrs_cfg, + } return [optimizer], [scheduler] + def configure_callbacks(self): + found_fts = [c for c in self.trainer.callbacks if isinstance(c, FinetuningScheduler)] # type: ignore # noqa + if found_fts: + self.finetuningscheduler_callback = found_fts[0] + return super().configure_callbacks() -# %% [markdown] -# ## Training - -# %% [markdown] -# ### CoLA -# -# See an interactive view of the -# CoLA dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=cola) # %% -seed_everything(42) +# let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it +# Please see the documentation for a full description of the schedule format +ft_schedule_yaml = """ +0: + params: # the parameters for each phase definition can be fully specified + - model.classifier.bias + - model.classifier.weight +1: + params: # or specified via a regex + - model.albert.pooler.* +2: + params: + - model.albert.encoder.*.ffn_output.* +""" +ft_schedule_name = "RteBoolqModule_ft_schedule_albert_base.yaml" +with open(ft_schedule_name, "w") as f: + f.write(ft_schedule_yaml) -dm = GLUEDataModule(model_name_or_path="albert-base-v2", task_name="cola") +# %% +datasets.set_progress_bar_enabled(False) +pl.seed_everything(42) +dm = RteBoolqDataModule(model_name_or_path="albert-base-v2", tokenizers_parallelism=False) dm.setup("fit") -model = GLUETransformer( +optimizer_init = { + "class_path": "torch.optim.AdamW", + "init_args": {"weight_decay": 1e-05, "eps": 1e-07, "lr": 1e-05}, +} +lr_scheduler_init = { + "class_path": "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts", + "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, +} +pl_lrs_cfg = {"interval": "epoch", "frequency": 1, "name": "CosineAnnealingWarmRestarts"} + +model = RteBoolqModule( model_name_or_path="albert-base-v2", - num_labels=dm.num_labels, - eval_splits=dm.eval_splits, - task_name=dm.task_name, + optimizer_init=optimizer_init, + lr_scheduler_init=lr_scheduler_init, + pl_lrs_cfg=pl_lrs_cfg, + experiment_tag="fts_explicit", ) - -trainer = Trainer(max_epochs=1, gpus=AVAIL_GPUS) -trainer.fit(model, dm) - -# %% [markdown] -# ### MRPC -# -# See an interactive view of the -# MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mrpc) +callbacks = [ + FinetuningScheduler(ft_schedule=ft_schedule_name, max_depth=2), # type: ignore # noqa + FTSEarlyStopping(monitor="val_loss", min_delta=0.001, patience=2), # type: ignore # noqa + FTSCheckpoint(monitor="val_loss", save_top_k=5), # type: ignore # noqa +] +example_logdir = "lightning_logs" +logger = TensorBoardLogger(example_logdir, name="fts_explicit") # %% -seed_everything(42) +# optionally start tensorboard and monitor progress graphically while viewing multi-phase finetuning specific training +# logs in the cell output below by uncommenting the next 3 lines +# # !mkdir -p $example_logdir +# # %load_ext tensorboard +# # %tensorboard --logdir example_logdir +# disable progress bar by default to focus on multi-phase training logs. Set to True to re-enable if desired +show_progress = False -dm = GLUEDataModule( - model_name_or_path="distilbert-base-cased", - task_name="mrpc", -) -dm.setup("fit") -model = GLUETransformer( - model_name_or_path="distilbert-base-cased", - num_labels=dm.num_labels, - eval_splits=dm.eval_splits, - task_name=dm.task_name, +# %% +trainer = pl.Trainer( + enable_progress_bar=show_progress, + precision=16, + gpus=AVAIL_GPUS, + callbacks=callbacks, + logger=logger, ) - -trainer = Trainer(max_epochs=3, gpus=AVAIL_GPUS) -trainer.fit(model, dm) +trainer.fit(model, datamodule=dm) # %% [markdown] -# ### MNLI +# ## Footnotes # -# - The MNLI dataset is huge, so we aren't going to bother trying to train on it here. -# - We will skip over training and go straight to validation. +#
    +#
  1. # -# See an interactive view of the -# MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mnli) - -# %% -dm = GLUEDataModule( - model_name_or_path="distilbert-base-cased", - task_name="mnli", -) -dm.setup("fit") -model = GLUETransformer( - model_name_or_path="distilbert-base-cased", - num_labels=dm.num_labels, - eval_splits=dm.eval_splits, - task_name=dm.task_name, -) - -trainer = Trainer(gpus=AVAIL_GPUS, progress_bar_refresh_rate=20) -trainer.validate(model, dm.val_dataloader()) +# [Howard, J., & Ruder, S. (2018)](https://arxiv.org/pdf/1801.06146.pdf). Fine-tuned Language +# Models for Text Classification. ArXiv, abs/1801.06146. [↩](#a1) +# +#
    2. +#
  2. +# +# [Chronopoulou, A., Baziotis, C., & Potamianos, A. (2019)](https://arxiv.org/pdf/1902.10547.pdf). +# An embarrassingly simple approach for transfer learning from pretrained language models. arXiv +# preprint arXiv:1902.10547. [↩](#a1) +# +#
    3. +#
  3. +# +# [Peters, M. E., Ruder, S., & Smith, N. A. (2019)](https://arxiv.org/pdf/1903.05987.pdf). To tune or not to +# tune? adapting pretrained representations to diverse tasks. arXiv preprint arXiv:1903.05987. [↩](#a1) +# +#
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zq_{Zp^CKxPri`xP;>MX{uF(wa{FKxdJD7r>;q<&{r&a)25dqrbWA@P!TdcGbAB{GT zxuzwx#WF^(NSw%AHF+95qb|8H9iC})GVm=GfzoA!wEh9_Kbhyn%h7-)+d z!$zgHxK?zo?E+&+l;10SB(cR@R)hm+i!HuHgSOZo4~Vam&VaahQ55NV<8ogih>I^* z%;LG*;`(H+N^r5I6c-P<)2(9(zs3?PNMTo@r%SCKNpLalp2(h;s1D*{Zt^-VE{f7Y znFkqr;`NsBu?jb`M8H)d!r$UU`Q1CN7mvb&L0deTEBOpBEip@h+*7$QN;00Ctf3#Q z&Hs(oI8TKu05_KJyIxGIS*G#jCNB_SdU-C8OF0$5&TYTv|5jbVOIBx=xcbY*t3y>6 zqv*J=6=`3ysb8zIfvl_0yV(E>zpRmKl|c;J1lr=+&^23(8ly%+d_h}0abFu2l9_Fl zRxV4M{zJTmiybb$!t3GE<*(+F;@U?+T-Jeke|Bc7+Rxi)u^g`|=5I9G7AJI}f))=g!zyPS{#(*1}yX^CsM{nff#+ zHu@rdp|K*l4J-Cl#4)ZCn_mgO5LXxPtu7U=E?2C6eYN_nXLV(4Rs3ajReIswg`3q| zQ@{V`!rMsE2QR##67}o(IAUVX@Xea~m0oxe__(K~(6&gr@OEzM zBcAD1KGR1#R;s!!maTw + + + + + + + image/svg+xml + + + + + + + + + From 2dfc99f5d4bad54205e545688a4eb74ccb8293d3 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Mon, 29 Nov 2021 19:48:08 -0800 Subject: [PATCH 03/44] tweak md format to H2 and update meta info --- lightning_examples/finetuning-scheduler/.meta.yml | 6 +++--- .../finetuning-scheduler/finetuning-scheduler.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 33296fba5..d1cfeb47d 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -1,7 +1,7 @@ title: Finetuning Scheduler author: Dan Dale -created: 2021-11-26 -updated: 2021-11-26 +created: 2021-11-29 +updated: 2021-11-29 license: CC BY-SA build: 1 tags: @@ -10,7 +10,7 @@ description: | This notebook introduces the FinetuningScheduler callback and demonstrates the use of FinetuningScheduler to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/) with iterative earlystopping defined according to a user-specified - schedule. It uses HuggingFace's `datasets` and `transformers`` libraries to retrieve the relevant benchmark data and + schedule. It uses HuggingFace's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data and foundational model weights. requirements: - transformers diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 9669cb632..4a38f96c4 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -15,7 +15,7 @@ # --- # %% [markdown] -# # Scheduled Finetuning +# ## Scheduled Finetuning # #
    # @@ -150,7 +150,7 @@ # %% [markdown] #
    # -# # Scheduled Finetuning For SuperGLUE +# ## Scheduled Finetuning For SuperGLUE # # The following example demonstrates the use of ``FinetuningScheduler`` to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. # From b6922503c2c0e70ab3c93c3834b48db4aca4c311 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Tue, 30 Nov 2021 10:57:11 -0800 Subject: [PATCH 04/44] update tutorial reqs --- docs/requirements.txt | 3 + .../finetuning-scheduler/.meta.yml | 11 +- .../finetuning-scheduler.py | 108 ++++++------------ .../fts_explicit_loss_anim.gif | Bin 302052 -> 172945 bytes 4 files changed, 44 insertions(+), 78 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index f721f461a..cd93ce2ee 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -6,6 +6,9 @@ docutils>=0.16 sphinx-paramlinks>=0.4.0 ipython[notebook] +# temporarily included until hub available to evaluate finetuning_scheduler +git+git://github.com/speediedan/pytorch-lightning.git@24d3e43568814ec381ac5be91627629808d62081#egg=pytorch-lightning + https://github.com/PyTorchLightning/lightning_sphinx_theme/archive/master.zip#egg=pt-lightning-sphinx-theme -r ../.actions/requirements.txt diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index d1cfeb47d..3b63b73e5 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -7,13 +7,14 @@ build: 1 tags: - finetuning description: | - This notebook introduces the FinetuningScheduler callback and demonstrates the use of FinetuningScheduler to finetune - a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task - of [SuperGLUE](https://super.gluebenchmark.com/) with iterative earlystopping defined according to a user-specified - schedule. It uses HuggingFace's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data and - foundational model weights. + This notebook introduces the FinetuningScheduler callback and demonstrates the use of FinetuningScheduler to finetune + a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task + of [SuperGLUE](https://super.gluebenchmark.com/) with iterative early-stopping defined according to a user-specified + schedule. It uses HuggingFace's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data + and foundational model weights. requirements: - transformers - datasets + - scikit-learn accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 4a38f96c4..5aab8f6df 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- # --- # jupyter: # jupytext: @@ -7,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.13.1 +# jupytext_version: 1.13.2 # kernelspec: # display_name: 'Python 3.7.11 64-bit (''pldev_tutorials'': conda)' # language: python @@ -84,7 +83,7 @@ # ``` # %% [markdown] -# ## EarlyStopping and Epoch-Driven Phase Transition Criteria +# ## Early-Stopping and Epoch-Driven Phase Transition Criteria # # # By default, ``FTSEarlyStopping`` and epoch-driven @@ -155,28 +154,26 @@ # The following example demonstrates the use of ``FinetuningScheduler`` to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. # # ``FinetuningScheduler`` can be used to achieve non-trivial model performance improvements in both implicit and explicit scheduling contexts at an also non-trivial computational cost. +# # %% +import logging import os import warnings from datetime import datetime -from typing import Any, Dict, List, Optional, Tuple, Union from importlib import import_module -import logging - -import torch -from torch.utils.data import DataLoader +from typing import Any, Dict, List, Optional, Tuple, Union +import datasets import pytorch_lightning as pl +import torch +from pytorch_lightning.loggers.tensorboard import TensorBoardLogger from pytorch_lightning.utilities import rank_zero_warn from pytorch_lightning.utilities.cli import CALLBACK_REGISTRY, _Registry from pytorch_lightning.utilities.exceptions import MisconfigurationException -from pytorch_lightning.loggers.tensorboard import TensorBoardLogger - -import datasets +from torch.utils.data import DataLoader from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer - # %% # a couple helper functions to prepare code to work with the forthcoming hub and user module registry MOCK_HUB_REGISTRY = _Registry() @@ -195,17 +192,13 @@ def module_hub_mock(key: str, require_fqn: bool = False) -> List: globals()[f"{n}"] = c registered_list = ", ".join([n for n in MOCK_HUB_REGISTRY.names]) else: - registered_list = ", ".join( - [c.__module__ + "." + c.__name__ for c in MOCK_HUB_REGISTRY.classes] - ) + registered_list = ", ".join([c.__module__ + "." + c.__name__ for c in MOCK_HUB_REGISTRY.classes]) print(f"Imported and registered the following callbacks: {registered_list}") -def instantiate_registered_class( - init: Dict[str, Any], args: Optional[Union[Any, Tuple[Any, ...]]] = None -) -> Any: - """Instantiates a class with the given args and init. Accepts class definitions in the form - of a "class_path" or "callback_key" associated with a _Registry +def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, Tuple[Any, ...]]] = None) -> Any: + """Instantiates a class with the given args and init. Accepts class definitions in the form of a "class_path" + or "callback_key" associated with a _Registry. Args: init: Dict of the form {"class_path":... or "callback_key":..., "init_args":...}. @@ -225,17 +218,16 @@ def instantiate_registered_class( else: # class is expected to be locally defined args_class = globals()[init["class_path"]] elif init.get("callback_key", None): - callback_path = CALLBACK_REGISTRY.get( + callback_path = CALLBACK_REGISTRY.get(init["callback_key"], None) or MOCK_HUB_REGISTRY.get( init["callback_key"], None - ) or MOCK_HUB_REGISTRY.get(init["callback_key"], None) + ) assert callback_path, MisconfigurationException( f'specified callback_key {init["callback_key"]} has not been registered' ) class_module, class_name = callback_path.__module__, callback_path.__name__ else: raise MisconfigurationException( - "Neither a class_path nor callback_key were included in a configuration that" - "requires one" + "Neither a class_path nor callback_key were included in a configuration that" "requires one" ) if not shortcircuit_local: module = __import__(class_module, fromlist=[class_name]) @@ -266,7 +258,7 @@ def instantiate_registered_class( # %% class RteBoolqDataModule(pl.LightningDataModule): - """A ``LightningDataModule`` for using either the RTE or BoolQ SuperGLUE Hugging Face datasets""" + """A ``LightningDataModule`` for using either the RTE or BoolQ SuperGLUE Hugging Face datasets.""" task_text_field_map = {"rte": ["premise", "hypothesis"], "boolq": ["question", "passage"]} loader_columns = [ @@ -306,12 +298,8 @@ def __init__( self.text_fields = self.task_text_field_map[self.task_name] self.num_labels = TASK_NUM_LABELS[self.task_name] os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.tokenizers_parallelism else "false" - self.tokenizer = AutoTokenizer.from_pretrained( - self.model_name_or_path, use_fast=True, local_files_only=False - ) - if ( - prep_on_init - ): # useful if one wants to load datasets as soon as the ``LightningDataModule`` is + self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True, local_files_only=False) + if prep_on_init: # useful if one wants to load datasets as soon as the ``LightningDataModule`` is # instantiated self.prepare_data() self.setup("fit") @@ -322,9 +310,7 @@ def setup(self, stage): self.dataset[split] = self.dataset[split].map( self.convert_to_features, batched=True, remove_columns=["label"] ) - self.columns = [ - c for c in self.dataset[split].column_names if c in self.loader_columns - ] + self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] self.dataset[split].set_format(type="torch", columns=self.columns) self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] @@ -335,9 +321,7 @@ def prepare_data(self): datasets.load_dataset("super_glue", self.task_name) def train_dataloader(self): - return DataLoader( - self.dataset["train"], batch_size=self.train_batch_size, **self.dataloader_kwargs - ) + return DataLoader(self.dataset["train"], batch_size=self.train_batch_size, **self.dataloader_kwargs) def val_dataloader(self): if len(self.eval_splits) == 1: @@ -348,29 +332,21 @@ def val_dataloader(self): ) elif len(self.eval_splits) > 1: return [ - DataLoader( - self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs - ) + DataLoader(self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs) for x in self.eval_splits ] def test_dataloader(self): if len(self.eval_splits) == 1: - return DataLoader( - self.dataset["test"], batch_size=self.eval_batch_size, **self.dataloader_kwargs - ) + return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size, **self.dataloader_kwargs) elif len(self.eval_splits) > 1: return [ - DataLoader( - self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs - ) + DataLoader(self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs) for x in self.eval_splits ] def convert_to_features(self, example_batch): - text_pairs = list( - zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]) - ) + text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) # Tokenize the text/text pairs features = self.tokenizer.batch_encode_plus( text_pairs, max_length=self.max_seq_length, padding="longest", truncation=True @@ -382,10 +358,8 @@ def convert_to_features(self, example_batch): # %% class RteBoolqModule(pl.LightningModule): - """A ``LightningModule`` that can be used to finetune a foundational - model on either the RTE or BoolQ SuperGLUE tasks using Hugging Face - implementations of a given model and the `SuperGLUE Hugging Face dataset. - """ + """A ``LightningModule`` that can be used to finetune a foundational model on either the RTE or BoolQ SuperGLUE + tasks using Hugging Face implementations of a given model and the `SuperGLUE Hugging Face dataset.""" def __init__( self, @@ -396,7 +370,6 @@ def __init__( model_cfg: Optional[Dict[str, Any]] = None, task_name: str = DEFAULT_TASK, experiment_tag: str = "default", - plot_liveloss: bool = False, ): """ Args: @@ -414,29 +387,20 @@ def __init__( super().__init__() self.optimizer_init = optimizer_init self.lr_scheduler_init = lr_scheduler_init - self.plot_liveloss = plot_liveloss self.pl_lrs_cfg = pl_lrs_cfg or {} if task_name in TASK_NUM_LABELS.keys(): self.task_name = task_name else: self.task_name = DEFAULT_TASK - rank_zero_warn( - f"Invalid task_name '{task_name}'. Proceeding with the default task: '{DEFAULT_TASK}'" - ) + rank_zero_warn(f"Invalid task_name '{task_name}'. Proceeding with the default task: '{DEFAULT_TASK}'") self.num_labels = TASK_NUM_LABELS[self.task_name] self.save_hyperparameters() self.experiment_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{experiment_tag}" self.model_cfg = model_cfg or {} - conf = AutoConfig.from_pretrained( - model_name_or_path, num_labels=self.num_labels, local_files_only=False - ) - self.model = AutoModelForSequenceClassification.from_pretrained( - model_name_or_path, config=conf - ) + conf = AutoConfig.from_pretrained(model_name_or_path, num_labels=self.num_labels, local_files_only=False) + self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=conf) self.model.config.update(self.model_cfg) # apply model config overrides - self.metric = datasets.load_metric( - "super_glue", self.task_name, experiment_id=self.experiment_id - ) + self.metric = datasets.load_metric("super_glue", self.task_name, experiment_id=self.experiment_id) self.no_decay = ["bias", "LayerNorm.weight"] self.finetuningscheduler_callback = None @@ -476,8 +440,8 @@ def validation_epoch_end(self, outputs): return loss def init_pgs(self) -> List[Dict]: - """Initialize the parameter groups. Used to ensure weight_decay is not applied - to our specified bias parameters when we initialize the optimizer. + """Initialize the parameter groups. Used to ensure weight_decay is not applied to our specified bias + parameters when we initialize the optimizer. Returns: List[Dict]: A list of parameter group dictionaries. @@ -510,9 +474,7 @@ def configure_optimizers(self): # performance difference) optimizer = instantiate_registered_class(args=self.init_pgs(), init=self.optimizer_init) scheduler = { - "scheduler": instantiate_registered_class( - args=optimizer, init=self.lr_scheduler_init - ), + "scheduler": instantiate_registered_class(args=optimizer, init=self.lr_scheduler_init), **self.pl_lrs_cfg, } return [optimizer], [scheduler] @@ -568,7 +530,7 @@ def configure_callbacks(self): callbacks = [ FinetuningScheduler(ft_schedule=ft_schedule_name, max_depth=2), # type: ignore # noqa FTSEarlyStopping(monitor="val_loss", min_delta=0.001, patience=2), # type: ignore # noqa - FTSCheckpoint(monitor="val_loss", save_top_k=5), # type: ignore # noqa + FTSCheckpoint(monitor="val_loss", save_top_k=5), # type: ignore # noqa ] example_logdir = "lightning_logs" logger = TensorBoardLogger(example_logdir, name="fts_explicit") diff --git 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ipython[notebook] -# temporarily included until hub available to evaluate finetuning_scheduler -git+git://github.com/speediedan/pytorch-lightning.git@24d3e43568814ec381ac5be91627629808d62081#egg=pytorch-lightning - https://github.com/PyTorchLightning/lightning_sphinx_theme/archive/master.zip#egg=pt-lightning-sphinx-theme -r ../.actions/requirements.txt diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 5aab8f6df..027275b92 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,18 +1,3 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.2 -# kernelspec: -# display_name: 'Python 3.7.11 64-bit (''pldev_tutorials'': conda)' -# language: python -# name: python3 -# --- - # %% [markdown] # ## Scheduled Finetuning # @@ -34,7 +19,7 @@ # final phase of the schedule has its stopping criteria met. See # the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/latest/extensions/generated/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. # -# +# ![FinetuningScheduler explicit loss animation](fts_explicit_loss_anim.gif) # %% [markdown] # ## Basic Usage @@ -416,7 +401,7 @@ def training_epoch_end(self, outputs: List[Any]) -> None: loss = torch.stack([x["loss"] for x in outputs]).mean() self.log("train_loss", loss, prog_bar=True, sync_dist=True) if self.finetuningscheduler_callback: - self.log("finetuning_schedule_depth", self.finetuningscheduler_callback.curr_depth) + self.log("finetuning_schedule_depth", float(self.finetuningscheduler_callback.curr_depth)) def validation_step(self, batch, batch_idx, dataloader_idx=0): outputs = self(**batch) From 33366fe336f5fa5531d283c5eabc8e52c6e61168 Mon Sep 17 00:00:00 2001 From: Dan Dale Date: Thu, 2 Dec 2021 09:06:05 -0800 Subject: [PATCH 06/44] Update lightning_examples/finetuning-scheduler/finetuning-scheduler.py Co-authored-by: thomas chaton --- .../finetuning-scheduler.py | 21 +++++++++---------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 027275b92..9cc473742 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -24,7 +24,7 @@ # %% [markdown] # ## Basic Usage # -# If no finetuning schedule is user-provided, ``FinetuningScheduler`` will generate a +# If no finetuning schedule is provided by the user, ``FinetuningScheduler`` will generate a # [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, using default ``FTSEarlyStopping`` and ``FTSCheckpoint`` callbacks with ``monitor=val_loss``. # # ```python @@ -268,9 +268,8 @@ def __init__( max_seq_length: int = 128, train_batch_size: int = 32, eval_batch_size: int = 32, - pin_memory: bool = False, tokenizers_parallelism: bool = True, - num_workers: int = 0, + **dataloader_kwargs: Any, ): super().__init__() self.model_name_or_path = model_name_or_path @@ -399,7 +398,7 @@ def training_step(self, batch, batch_idx): def training_epoch_end(self, outputs: List[Any]) -> None: loss = torch.stack([x["loss"] for x in outputs]).mean() - self.log("train_loss", loss, prog_bar=True, sync_dist=True) + self.log("train_loss", loss, prog_bar=True) if self.finetuningscheduler_callback: self.log("finetuning_schedule_depth", float(self.finetuningscheduler_callback.curr_depth)) @@ -424,14 +423,14 @@ def validation_epoch_end(self, outputs): self.log_dict(metric_dict, prog_bar=True, sync_dist=True) return loss - def init_pgs(self) -> List[Dict]: + def _init_param_groups(self) -> List[Dict]: """Initialize the parameter groups. Used to ensure weight_decay is not applied to our specified bias parameters when we initialize the optimizer. Returns: List[Dict]: A list of parameter group dictionaries. """ - pgs = [ + return [ { "params": [ p @@ -449,7 +448,6 @@ def init_pgs(self) -> List[Dict]: "weight_decay": 0.0, }, ] - return pgs def configure_optimizers(self): # the phase 0 parameters will have been set to require gradients during setup @@ -472,7 +470,7 @@ def configure_callbacks(self): # %% -# let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it +# Let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it # Please see the documentation for a full description of the schedule format ft_schedule_yaml = """ 0: @@ -527,13 +525,14 @@ def configure_callbacks(self): # # %load_ext tensorboard # # %tensorboard --logdir example_logdir # disable progress bar by default to focus on multi-phase training logs. Set to True to re-enable if desired -show_progress = False +enable_progress_bar = False # %% trainer = pl.Trainer( - enable_progress_bar=show_progress, + enable_progress_bar=enable_progress_bar, precision=16, - gpus=AVAIL_GPUS, + accelerator="auto", + devices="auto", callbacks=callbacks, logger=logger, ) From 3d0fcfe7b844c26e794a363e75f6c6f2d11fbda9 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Thu, 2 Dec 2021 14:34:09 -0800 Subject: [PATCH 07/44] additional tuning literature references and several additional recommended improvements for finetuning_scheduler tutorial --- .../finetuning-scheduler.py | 182 +++++++++++------- 1 file changed, 115 insertions(+), 67 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 9cc473742..52a10fb23 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -100,7 +100,8 @@ # ```python # from pytorch_lightning import Trainer # from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler -# trainer = Trainer(callbacks=[FinetuningScheduler()], resume_from_checkpoint="some/path/to/my_checkpoint.ckpt") +# trainer = Trainer(callbacks=[FinetuningScheduler()]) +# trainer.fit(..., ckpt_path="some/path/to/my_checkpoint.ckpt") # ``` # # Training will resume at the depth/level of the provided checkpoint according the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions. @@ -108,10 +109,8 @@ # By default (``FinetuningScheduler.restore_best`` is ``True``), ``FinetuningScheduler`` will attempt to restore the best available checkpoint before finetuning depth transitions. # # ```python -# trainer = Trainer( -# callbacks=[FinetuningScheduler(new_incarnation_mode=True)], -# resume_from_checkpoint="some/path/to/my_kth_best_checkpoint.ckpt", -# ) +# trainer = Trainer(callbacks=[FinetuningScheduler(new_incarnation_mode=True)]) +# trainer.fit(..., ckpt_path="some/path/to/my_kth_best_checkpoint.ckpt") # ``` # # To handle the edge case wherein one is resuming scheduled finetuning from a non-best checkpoint and the previous best checkpoints may not be accessible, setting ``FinetuningScheduler.new_incarnation_mode`` to @@ -120,14 +119,12 @@ # %% [markdown] #

    # -# **Note:** Currently, _FinetuningScheduler_ only supports the following _TrainingTypePlugins_: -# -# - ``DDPPlugin`` -# - ``DDPShardedPlugin`` -# - ``DDPSpawnPlugin`` -# - ``DDPSpawnShardedPlugin`` -# - ``DataParallelPlugin`` -# - ``SingleDevicePlugin`` +# **Note:** Currently, _FinetuningScheduler_ only supports the following ``StrategyType``s: +# - ``DP`` +# - ``DDP`` +# - ``DDP_SPAWN`` +# - ``DDP_SHARDED`` +# - ``DDP_SHARDED_SPAWN`` # #
    @@ -142,7 +139,6 @@ # # %% -import logging import os import warnings from datetime import datetime @@ -160,24 +156,24 @@ from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer # %% -# a couple helper functions to prepare code to work with the forthcoming hub and user module registry -MOCK_HUB_REGISTRY = _Registry() +# a couple helper functions to prepare code to work with a user module registry +MOCK_REGISTRY = _Registry() -def module_hub_mock(key: str, require_fqn: bool = False) -> List: +def mock_register_module(key: str, require_fqn: bool = False) -> List: if key.lower() == "finetuningscheduler": mod = import_module("pytorch_lightning.callbacks.finetuning_scheduler") - MOCK_HUB_REGISTRY.register_classes(mod, pl.callbacks.Callback) + MOCK_REGISTRY.register_classes(mod, pl.callbacks.Callback) else: raise MisconfigurationException(f"user module key '{key}' not found") registered_list = [] # make registered class available by unqualified class name by default if not require_fqn: - for n, c in MOCK_HUB_REGISTRY.items(): + for n, c in MOCK_REGISTRY.items(): globals()[f"{n}"] = c - registered_list = ", ".join([n for n in MOCK_HUB_REGISTRY.names]) + registered_list = ", ".join([n for n in MOCK_REGISTRY.names]) else: - registered_list = ", ".join([c.__module__ + "." + c.__name__ for c in MOCK_HUB_REGISTRY.classes]) + registered_list = ", ".join([c.__module__ + "." + c.__name__ for c in MOCK_REGISTRY.classes]) print(f"Imported and registered the following callbacks: {registered_list}") @@ -203,7 +199,7 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, else: # class is expected to be locally defined args_class = globals()[init["class_path"]] elif init.get("callback_key", None): - callback_path = CALLBACK_REGISTRY.get(init["callback_key"], None) or MOCK_HUB_REGISTRY.get( + callback_path = CALLBACK_REGISTRY.get(init["callback_key"], None) or MOCK_REGISTRY.get( init["callback_key"], None ) assert callback_path, MisconfigurationException( @@ -222,31 +218,24 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, # %% # load the pl extension module we want to use. This will import all necessary callbacks. -module_hub_mock("finetuningscheduler") +mock_register_module("finetuningscheduler") # set notebook-level variables -AVAIL_GPUS = min(1, torch.cuda.device_count()) +AVAIL_GPUS = torch.cuda.device_count() TASK_NUM_LABELS = {"boolq": 2, "rte": 2} DEFAULT_TASK = "rte" -# narrow our logging to adapt it for a notebook environment -for l_key in logging.Logger.manager.loggerDict.keys(): - if "pytorch_lightning" in l_key: - logging.getLogger(l_key).setLevel("INFO") - else: - logging.getLogger(l_key).setLevel("CRITICAL") -pl_logger = logging.getLogger("pytorch_lightning") -pl_logger.removeHandler(pl_logger.handlers[0]) -rz_logger = logging.getLogger("pytorch_lightning.utilities.distributed") -rz_logger.addHandler(logging.StreamHandler()) -rz_logger.handlers[0].setLevel("INFO") +# ignore warnings related tokenizers_parallelism/DataLoader parallelism tradeoff and +# expected logging behavior +for warnf in [".*does not have many workers*", ".*The number of training samples.*"]: + warnings.filterwarnings("ignore", warnf) # %% class RteBoolqDataModule(pl.LightningDataModule): """A ``LightningDataModule`` for using either the RTE or BoolQ SuperGLUE Hugging Face datasets.""" - task_text_field_map = {"rte": ["premise", "hypothesis"], "boolq": ["question", "passage"]} - loader_columns = [ + TASK_TEXT_FIELD_MAP = {"rte": ("premise", "hypothesis"), "boolq": ("question", "passage")} + LOADER_COLUMNS = ( "datasets_idx", "input_ids", "token_type_ids", @@ -254,17 +243,12 @@ class RteBoolqDataModule(pl.LightningDataModule): "start_positions", "end_positions", "labels", - ] - # ignore warnings related tokenizers_parallelism/DataLoader parallelism tradeoff and - # expected logging behavior - for warnf in [".*does not have many workers*", ".*The number of training samples.*"]: - warnings.filterwarnings("ignore", warnf) + ) def __init__( self, model_name_or_path: str, task_name: str = DEFAULT_TASK, - prep_on_init: bool = False, max_seq_length: int = 128, train_batch_size: int = 32, eval_batch_size: int = 32, @@ -278,23 +262,22 @@ def __init__( self.train_batch_size = train_batch_size self.eval_batch_size = eval_batch_size self.tokenizers_parallelism = tokenizers_parallelism - self.dataloader_kwargs = {"num_workers": num_workers, "pin_memory": pin_memory} - self.text_fields = self.task_text_field_map[self.task_name] + self.dataloader_kwargs = { + "num_workers": dataloader_kwargs.get("num_workers", 0), + "pin_memory": dataloader_kwargs.get("pin_memory", False), + } + self.text_fields = self.TASK_TEXT_FIELD_MAP[self.task_name] self.num_labels = TASK_NUM_LABELS[self.task_name] os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.tokenizers_parallelism else "false" self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True, local_files_only=False) - if prep_on_init: # useful if one wants to load datasets as soon as the ``LightningDataModule`` is - # instantiated - self.prepare_data() - self.setup("fit") def setup(self, stage): self.dataset = datasets.load_dataset("super_glue", self.task_name) for split in self.dataset.keys(): self.dataset[split] = self.dataset[split].map( - self.convert_to_features, batched=True, remove_columns=["label"] + self._convert_to_features, batched=True, remove_columns=["label"] ) - self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] + self.columns = [c for c in self.dataset[split].column_names if c in self.LOADER_COLUMNS] self.dataset[split].set_format(type="torch", columns=self.columns) self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] @@ -329,7 +312,7 @@ def test_dataloader(self): for x in self.eval_splits ] - def convert_to_features(self, example_batch): + def _convert_to_features(self, example_batch): text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) # Tokenize the text/text pairs features = self.tokenizer.batch_encode_plus( @@ -412,15 +395,15 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): preds = logits.squeeze() labels = batch["labels"] + self.log("val_loss", val_loss, prog_bar=True) return {"loss": val_loss, "preds": preds, "labels": labels} def validation_epoch_end(self, outputs): preds = torch.cat([x["preds"] for x in outputs]).detach().cpu().numpy() labels = torch.cat([x["labels"] for x in outputs]).detach().cpu().numpy() loss = torch.stack([x["loss"] for x in outputs]).mean() - self.log("val_loss", loss, prog_bar=True, sync_dist=True) metric_dict = self.metric.compute(predictions=preds, references=labels) - self.log_dict(metric_dict, prog_bar=True, sync_dist=True) + self.log_dict(metric_dict, prog_bar=True) return loss def _init_param_groups(self) -> List[Dict]: @@ -455,7 +438,7 @@ def configure_optimizers(self): # but in this case we pass a list of parameter groups to ensure weight_decay is # not applied to the bias parameter (for completeness, in this case it won't make much # performance difference) - optimizer = instantiate_registered_class(args=self.init_pgs(), init=self.optimizer_init) + optimizer = instantiate_registered_class(args=self._init_param_groups(), init=self.optimizer_init) scheduler = { "scheduler": instantiate_registered_class(args=optimizer, init=self.lr_scheduler_init), **self.pl_lrs_cfg, @@ -485,6 +468,8 @@ def configure_callbacks(self): - model.albert.encoder.*.ffn_output.* """ ft_schedule_name = "RteBoolqModule_ft_schedule_albert_base.yaml" +# Let's write the schedule to a file so we can simulate loading an explicitly defined finetuning +# schedule. with open(ft_schedule_name, "w") as f: f.write(ft_schedule_yaml) @@ -492,17 +477,52 @@ def configure_callbacks(self): datasets.set_progress_bar_enabled(False) pl.seed_everything(42) dm = RteBoolqDataModule(model_name_or_path="albert-base-v2", tokenizers_parallelism=False) -dm.setup("fit") + +# %% [markdown] +# ### Optimizer Configuration +# +#
    +# +# Though other optimizers can arguably yield some marginal advantage contingent on the context, +# the Adam optimizer (and the [AdamW version](https://pytorch.org/docs/stable/_modules/torch/optim/adamw.html#AdamW) which +# implements decoupled weight decay) remains robust to hyperparameter choices and is commonly used for finetuning +# foundational language models. See [(Sivaprasad et al., 2020)](#f2) and [(Mosbach, Andriushchenko & Klakow, 2020)](#f3) for theoretical and systematic empirical justifications of Adam and its use in finetuning +# large transformer-based language models. The values used here have some justification +# in the referenced literature but have been largely empirically determined and while a good +# starting point could be could be further tuned. +# +#
    + +# %% optimizer_init = { "class_path": "torch.optim.AdamW", "init_args": {"weight_decay": 1e-05, "eps": 1e-07, "lr": 1e-05}, } + +# %% [markdown] +# ### LR Scheduler Configuration +# +#
    +# +# The [CosineAnnealingWarmRestarts scheduler](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html?highlight=cosineannealingwarm#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts) nicely fits with our iterative finetuning since it does not depend upon a global max_epoch +# value. The importance of initial warmup is reduced due to the innate warmup effect of Adam bias correction [[5]](#f3) +# and the gradual thawing we are performing. Note that commonly used LR schedulers that depend on providing +# max_iterations/epochs (e.g. the +# [CosineWarmupScheduler](https://github.com/PyTorchLightning/lightning-tutorials/blob/0c325829101d5a6ebf32ed99bbf5b09badf04a59/course_UvA-DL/05-transformers-and-MH-attention/Transformers_MHAttention.py#L688) +# used in other pytorch-lightning tutorials) also work with FinetuningScheduler. Though the LR scheduler is theoretically +# justified [(Loshchilov & Hutter, 2016)](#f4), the particular values provided here are primarily empircally driven. +# +#
    + + +# %% lr_scheduler_init = { "class_path": "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts", "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, } pl_lrs_cfg = {"interval": "epoch", "frequency": 1, "name": "CosineAnnealingWarmRestarts"} +# %% model = RteBoolqModule( model_name_or_path="albert-base-v2", optimizer_init=optimizer_init, @@ -528,15 +548,23 @@ def configure_callbacks(self): enable_progress_bar = False # %% -trainer = pl.Trainer( - enable_progress_bar=enable_progress_bar, - precision=16, - accelerator="auto", - devices="auto", - callbacks=callbacks, - logger=logger, -) -trainer.fit(model, datamodule=dm) +def train() -> None: + trainer = pl.Trainer( + enable_progress_bar=enable_progress_bar, + precision=16, + gpus=1, + # accelerator="auto", + # devices="auto", + callbacks=callbacks, + logger=logger, + ) + trainer.fit(model, datamodule=dm) + + +if AVAIL_GPUS > 0: + train() +else: + print("Given the multiple phases of finetuning demonstrated, this notebook is best used with a GPU") # %% [markdown] # ## Footnotes @@ -560,5 +588,25 @@ def configure_callbacks(self): # [Peters, M. E., Ruder, S., & Smith, N. A. (2019)](https://arxiv.org/pdf/1903.05987.pdf). To tune or not to # tune? adapting pretrained representations to diverse tasks. arXiv preprint arXiv:1903.05987. [↩](#a1) # -# -# +# +#
  4. +# +# [Sivaprasad, P. T., Mai, F., Vogels, T., Jaggi, M., & Fleuret, F. (2020)](https://arxiv.org/pdf/1910.11758.pdf). +# Optimizer benchmarking needs to account for hyperparameter tuning. In International Conference on Machine Learning +# (pp. 9036-9045). PMLR. [↩](#a2) +# +#
    5. +#
  5. +# +# [Mosbach, M., Andriushchenko, M., & Klakow, D. (2020)](https://arxiv.org/pdf/2006.04884.pdf). On the stability of +# fine-tuning bert: Misconceptions, explanations, and strong baselines. arXiv preprint arXiv:2006.04884. [↩](#a2) +# +#
    6. +#
  6. +# +# [Loshchilov, I., & Hutter, F. (2016)](https://arxiv.org/pdf/1608.03983.pdf). Sgdr: Stochastic gradient descent with +# warm restarts. arXiv preprint arXiv:1608.03983. [↩](#a3) +# +#
    7. +# +#
From d13d9d956bb322e1c588c0ff3940433ff73df1e7 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Sat, 4 Dec 2021 14:34:37 -0800 Subject: [PATCH 08/44] misc tutorial enhancements --- .../finetuning-scheduler.py | 22 +++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 52a10fb23..37729e295 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,3 +1,15 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: -all +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.2 +# --- + # %% [markdown] # ## Scheduled Finetuning # @@ -396,6 +408,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): labels = batch["labels"] self.log("val_loss", val_loss, prog_bar=True) + return {"loss": val_loss, "preds": preds, "labels": labels} def validation_epoch_end(self, outputs): @@ -518,7 +531,8 @@ def configure_callbacks(self): # %% lr_scheduler_init = { "class_path": "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts", - "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, + # "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, + "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 0}, } pl_lrs_cfg = {"interval": "epoch", "frequency": 1, "name": "CosineAnnealingWarmRestarts"} @@ -551,10 +565,10 @@ def configure_callbacks(self): def train() -> None: trainer = pl.Trainer( enable_progress_bar=enable_progress_bar, + # max_epochs=1, precision=16, - gpus=1, - # accelerator="auto", - # devices="auto", + accelerator="gpu", + devices=1, callbacks=callbacks, logger=logger, ) From eb7e09516a6157010224a16147ee8a65b858d896 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Wed, 8 Dec 2021 12:33:03 -0800 Subject: [PATCH 09/44] numerous expository enhancements and switch to DeBERTav3 --- .../finetuning-scheduler/.meta.yml | 1 + ...eBoolqModule_ft_schedule_deberta_base.yaml | 18 ++ .../finetuning-scheduler.py | 233 +++++++++++++----- .../fts_explicit_accuracy.png | Bin 0 -> 24409 bytes .../implicit_training_transition.png | Bin 0 -> 22919 bytes .../nofts_baseline_accuracy.png | Bin 0 -> 24750 bytes 6 files changed, 197 insertions(+), 55 deletions(-) create mode 100644 lightning_examples/finetuning-scheduler/RteBoolqModule_ft_schedule_deberta_base.yaml create mode 100644 lightning_examples/finetuning-scheduler/fts_explicit_accuracy.png create mode 100644 lightning_examples/finetuning-scheduler/implicit_training_transition.png create mode 100644 lightning_examples/finetuning-scheduler/nofts_baseline_accuracy.png diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 3b63b73e5..6954f680c 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -16,5 +16,6 @@ requirements: - transformers - datasets - scikit-learn + - sentencepiece accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/RteBoolqModule_ft_schedule_deberta_base.yaml b/lightning_examples/finetuning-scheduler/RteBoolqModule_ft_schedule_deberta_base.yaml new file mode 100644 index 000000000..62bdbae28 --- /dev/null +++ b/lightning_examples/finetuning-scheduler/RteBoolqModule_ft_schedule_deberta_base.yaml @@ -0,0 +1,18 @@ + +0: + params: + - model.classifier.bias + - model.classifier.weight + - model.pooler.dense.bias + - model.pooler.dense.weight + - model.deberta.encoder.LayerNorm.bias + - model.deberta.encoder.LayerNorm.weight + - model.deberta.encoder.rel_embeddings.weight + - model.deberta.encoder.layer.{0,11}.(output|attention|intermediate).* +1: + params: + - model.deberta.embeddings.LayerNorm.bias + - model.deberta.embeddings.LayerNorm.weight +2: + params: + - model.deberta.embeddings.word_embeddings.weight diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 37729e295..968395d0c 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,25 +1,21 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: -all -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.2 -# --- - # %% [markdown] # ## Scheduled Finetuning # +# Training with the ``FinetuningScheduler`` callback confers a host of benefits. It was created to meet the following +# objectives in roughly descending order of priority: +# +# - maximize finetuning flexibility +# - expedite and facilitate exploration of model tuning dynamics in research +# - allow marginal performance improvements of finetuned models +# #
# -# The ``FinetuningScheduler`` callback enables multi-phase, -# scheduled finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize +# Fundamentally, the ``FinetuningScheduler`` callback enables scheduled, multi-phase, +# finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize # foundational model knowledge retention while allowing (typically upper layers of) the model to # optimally adapt to new tasks during transfer learning [1, 2, 3](#f1) # +# #
# # ``FinetuningScheduler`` orchestrates the gradual unfreezing @@ -34,11 +30,16 @@ # ![FinetuningScheduler explicit loss animation](fts_explicit_loss_anim.gif) # %% [markdown] +# # ## Basic Usage # +#
+# # If no finetuning schedule is provided by the user, ``FinetuningScheduler`` will generate a # [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, using default ``FTSEarlyStopping`` and ``FTSCheckpoint`` callbacks with ``monitor=val_loss``. # +#
+# # ```python # from pytorch_lightning import Trainer # from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler @@ -49,7 +50,7 @@ # ## The Default Finetuning Schedule # # Schedule definition is facilitated via the ``gen_ft_schedule`` method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as -# desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will often be less computationally efficient than a user-defined finetuning schedule but can often serve as a good baseline for subsquent explicit schedule refinement and will marginally outperform many explicit schedules. +# desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will likely be less computationally efficient than a user-defined finetuning schedule but is useful for exploring a model's finetuning behavior and can serve as a good baseline for subsquent explicit schedule refinement. # %% [markdown] # ## Specifying a Finetuning Schedule @@ -147,8 +148,6 @@ # # The following example demonstrates the use of ``FinetuningScheduler`` to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. # -# ``FinetuningScheduler`` can be used to achieve non-trivial model performance improvements in both implicit and explicit scheduling contexts at an also non-trivial computational cost. -# # %% import os @@ -166,6 +165,7 @@ from pytorch_lightning.utilities.exceptions import MisconfigurationException from torch.utils.data import DataLoader from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer +from transformers import logging as transformers_logging # %% # a couple helper functions to prepare code to work with a user module registry @@ -200,12 +200,14 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, Returns: The instantiated class object. """ + class_module, class_name, args_class = None, None, None + shortcircuit_local = False kwargs = init.get("init_args", {}) + class_path = init.get("class_path", None) if args and not isinstance(args, tuple): args = (args,) - shortcircuit_local = False if "." in init["class_path"] else True - class_module, class_name, args_class = None, None, None - if init.get("class_path", None): + if class_path: + shortcircuit_local = False if "." in class_path else True if not shortcircuit_local: class_module, class_name = init["class_path"].rsplit(".", 1) else: # class is expected to be locally defined @@ -236,6 +238,7 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, TASK_NUM_LABELS = {"boolq": 2, "rte": 2} DEFAULT_TASK = "rte" +transformers_logging.set_verbosity_error() # ignore warnings related tokenizers_parallelism/DataLoader parallelism tradeoff and # expected logging behavior for warnf in [".*does not have many workers*", ".*The number of training samples.*"]: @@ -262,8 +265,8 @@ def __init__( model_name_or_path: str, task_name: str = DEFAULT_TASK, max_seq_length: int = 128, - train_batch_size: int = 32, - eval_batch_size: int = 32, + train_batch_size: int = 16, + eval_batch_size: int = 16, tokenizers_parallelism: bool = True, **dataloader_kwargs: Any, ): @@ -373,12 +376,21 @@ def __init__( self.task_name = DEFAULT_TASK rank_zero_warn(f"Invalid task_name '{task_name}'. Proceeding with the default task: '{DEFAULT_TASK}'") self.num_labels = TASK_NUM_LABELS[self.task_name] - self.save_hyperparameters() self.experiment_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{experiment_tag}" self.model_cfg = model_cfg or {} conf = AutoConfig.from_pretrained(model_name_or_path, num_labels=self.num_labels, local_files_only=False) self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=conf) self.model.config.update(self.model_cfg) # apply model config overrides + self.init_hparams = { + "optimizer": self.optimizer_init, + "lr_scheduler": self.lr_scheduler_init, + "pl_lrs_cfg": self.pl_lrs_cfg, + "model_config": self.model.config, + "model_name_or_path": model_name_or_path, + "task_name": self.task_name, + "experiment_id": self.experiment_id, + } + self.save_hyperparameters(self.init_hparams) self.metric = datasets.load_metric("super_glue", self.task_name, experiment_id=self.experiment_id) self.no_decay = ["bias", "LayerNorm.weight"] self.finetuningscheduler_callback = None @@ -400,24 +412,14 @@ def training_epoch_end(self, outputs: List[Any]) -> None: def validation_step(self, batch, batch_idx, dataloader_idx=0): outputs = self(**batch) val_loss, logits = outputs[:2] - if self.num_labels >= 1: preds = torch.argmax(logits, axis=1) elif self.num_labels == 1: preds = logits.squeeze() - labels = batch["labels"] self.log("val_loss", val_loss, prog_bar=True) - - return {"loss": val_loss, "preds": preds, "labels": labels} - - def validation_epoch_end(self, outputs): - preds = torch.cat([x["preds"] for x in outputs]).detach().cpu().numpy() - labels = torch.cat([x["labels"] for x in outputs]).detach().cpu().numpy() - loss = torch.stack([x["loss"] for x in outputs]).mean() metric_dict = self.metric.compute(predictions=preds, references=labels) self.log_dict(metric_dict, prog_bar=True) - return loss def _init_param_groups(self) -> List[Dict]: """Initialize the parameter groups. Used to ensure weight_decay is not applied to our specified bias @@ -465,22 +467,46 @@ def configure_callbacks(self): return super().configure_callbacks() +# %% [markdown] +# ### Our Training Sessions +# +# We'll be comparing three different finetuning training configurations. Every configuration in this example depends +# upon a shared set of defaults, only differing in their respective finetuning schedules. +# +# | Experiment Tag | Training Scenario Description | +# |:-----------------:| ---------------------------------------------------------------------- | +# | ``fts_explicit`` | Training with a finetuning schedule explicitly provided by the user | +# | ``nofts_baseline``| A baseline finetuning training session (without scheduled finetuning) | +# | ``fts_implicit`` | Training with an implicitly generated finetuning schedule (the default)| +# +# Let's begin by configuring the ``fts_explicit`` scenario. We'll subsequently run the other two scenarios for +# comparison. + # %% # Let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it -# Please see the documentation for a full description of the schedule format +# Please see the ``FinetuningScheduler`` documentation for a full description of the schedule format + + ft_schedule_yaml = """ 0: - params: # the parameters for each phase definition can be fully specified + params: - model.classifier.bias - model.classifier.weight + - model.pooler.dense.bias + - model.pooler.dense.weight + - model.deberta.encoder.LayerNorm.bias + - model.deberta.encoder.LayerNorm.weight + - model.deberta.encoder.rel_embeddings.weight + - model.deberta.encoder.layer.{0,11}.(output|attention|intermediate).* 1: - params: # or specified via a regex - - model.albert.pooler.* + params: + - model.deberta.embeddings.LayerNorm.bias + - model.deberta.embeddings.LayerNorm.weight 2: params: - - model.albert.encoder.*.ffn_output.* + - model.deberta.embeddings.word_embeddings.weight """ -ft_schedule_name = "RteBoolqModule_ft_schedule_albert_base.yaml" +ft_schedule_name = "RteBoolqModule_ft_schedule_deberta_base.yaml" # Let's write the schedule to a file so we can simulate loading an explicitly defined finetuning # schedule. with open(ft_schedule_name, "w") as f: @@ -489,7 +515,7 @@ def configure_callbacks(self): # %% datasets.set_progress_bar_enabled(False) pl.seed_everything(42) -dm = RteBoolqDataModule(model_name_or_path="albert-base-v2", tokenizers_parallelism=False) +dm = RteBoolqDataModule(model_name_or_path="microsoft/deberta-v3-base", tokenizers_parallelism=False) # %% [markdown] # ### Optimizer Configuration @@ -531,28 +557,40 @@ def configure_callbacks(self): # %% lr_scheduler_init = { "class_path": "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts", - # "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, - "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 0}, + "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, } pl_lrs_cfg = {"interval": "epoch", "frequency": 1, "name": "CosineAnnealingWarmRestarts"} # %% -model = RteBoolqModule( - model_name_or_path="albert-base-v2", - optimizer_init=optimizer_init, - lr_scheduler_init=lr_scheduler_init, - pl_lrs_cfg=pl_lrs_cfg, - experiment_tag="fts_explicit", -) +# Load our lightning module... +lightning_module_kwargs = { + "model_name_or_path": "microsoft/deberta-v3-base", + "optimizer_init": optimizer_init, + "lr_scheduler_init": lr_scheduler_init, + "pl_lrs_cfg": pl_lrs_cfg, +} +model = RteBoolqModule(**lightning_module_kwargs, experiment_tag="fts_explicit") + +# %% [markdown] +# ### Callback Configuration +# +# The only callback required to invoke the ``FinetuningScheduler`` is the ``FinetuningScheduler`` callback itself. +# Default versions of ``FTSCheckpoint`` and ``FTSEarlyStopping`` (if not specifying ``epoch_only_transitions``) will be +# included ([as discussed above](#basic_usage)) if not provided in the callbacks list. For demonstration purposes I'm +# including example configurations of all three callbacks below. + +# %% +earlystopping_kwargs = {"monitor": "val_loss", "min_delta": 0.001, "patience": 2} +checkpoint_kwargs = {"monitor": "val_loss", "save_top_k": 1} +fts_kwargs = {"max_depth": 1} callbacks = [ - FinetuningScheduler(ft_schedule=ft_schedule_name, max_depth=2), # type: ignore # noqa - FTSEarlyStopping(monitor="val_loss", min_delta=0.001, patience=2), # type: ignore # noqa - FTSCheckpoint(monitor="val_loss", save_top_k=5), # type: ignore # noqa + FinetuningScheduler(ft_schedule=ft_schedule_name, **fts_kwargs), # type: ignore # noqa + FTSEarlyStopping(**earlystopping_kwargs), # type: ignore # noqa + FTSCheckpoint(**checkpoint_kwargs), # type: ignore # noqa ] +# %% example_logdir = "lightning_logs" logger = TensorBoardLogger(example_logdir, name="fts_explicit") - -# %% # optionally start tensorboard and monitor progress graphically while viewing multi-phase finetuning specific training # logs in the cell output below by uncommenting the next 3 lines # # !mkdir -p $example_logdir @@ -565,7 +603,7 @@ def configure_callbacks(self): def train() -> None: trainer = pl.Trainer( enable_progress_bar=enable_progress_bar, - # max_epochs=1, + max_epochs=100, precision=16, accelerator="gpu", devices=1, @@ -580,6 +618,91 @@ def train() -> None: else: print("Given the multiple phases of finetuning demonstrated, this notebook is best used with a GPU") +# %% [markdown] +# ### Running the Baseline and Implicit Finetuning Scenarios +# +# Let's now compare our ``nofts_baseline`` and ``fts_implicit`` scenarios with the ``fts_explicit`` one we just ran. +# We'll need to update our callbacks list, using the core PL ``EarlyStopping`` and ``ModelCheckpoint`` callbacks for the +# ``nofts_baseline`` (which operate identically to their FTS analogs apart from the recursive training support). +# For both core PyTorch Lightning and user-registered callbacks, we can define our callbacks using a dictionary as we do +# with the LightningCLI. This allows us to avoid managing imports and support more complex configuration separated from +# code. +# We'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping ``max_depth`` for the +# implicit schedule will limit finetuning to just the last 4 parameters of the model, which is only a small fraction +# of the parameters you'd want to tune for maximum performance. Since the implicit schedule is quite computationally +# intensive and most useful for exploring model behavior, leaving ``max_depth`` 1 allows us to demo implicit mode +# behavior while keeping the computational cost and runtime of this notebook reasonable. To review how a full implicit +# mode run compares to the ``nofts_baseline`` and ``fts_explicit`` scenarios, please see the the following +# [tensorboard experiment summary](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/). + + +# %% + +# reference the core callbacks using the relevant callback_key +nofts_callback_cfg = [ + {"callback_key": "EarlyStopping", "init_args": earlystopping_kwargs}, + {"callback_key": "ModelCheckpoint", "init_args": checkpoint_kwargs}, +] +# we can use class_paths, class names or fully qualified names for referencing user-registered callbacks as well +fts_implicit_cfg = [ + {"callback_key": "FinetuningScheduler", "init_args": fts_kwargs}, # use the callback_key + {"class_path": "FTSEarlyStopping", "init_args": earlystopping_kwargs}, # just the class_name + # or the fully qualified name + { + "class_path": "pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSCheckpoint", + "init_args": checkpoint_kwargs, + }, +] +nofts_callbacks = [instantiate_registered_class(c) for c in nofts_callback_cfg] +fts_implicit_callbacks = [instantiate_registered_class(c) for c in fts_implicit_cfg] +scenario_callbacks = {"nofts_baseline": nofts_callbacks, "fts_implicit": fts_implicit_callbacks} + +# %% +if AVAIL_GPUS > 0: + for scenario_name, scenario_callbacks in scenario_callbacks.items(): + model = RteBoolqModule(**lightning_module_kwargs, experiment_tag=scenario_name) + logger = TensorBoardLogger(example_logdir, name=scenario_name) + callbacks = scenario_callbacks + print(f"Beginning training the '{scenario_name}' scenario") + train() +else: + print("Given the multiple phases of finetuning demonstrated, this notebook is best used with a GPU") + +# %% [markdown] +# ### Reviewing the Training Results +# +# See the [tensorboard experiment summaries](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/) to get a sense +# of the relative computational and performance tradeoffs associated with these ``FinetuningScheduler`` configurations. +# The summary compares a full ``fts_implicit`` execution to ``fts_explicit`` and ``nofts_baseline`` scenarios using DDP +# training with 2 GPUs. The full logs/schedules for all three scenarios are available +# [here](https://drive.google.com/file/d/1LrUcisRLHeJgh_BDOOD_GUBPp5iHAkoR/view?usp=sharing) and the checkpoints +# produced in the scenarios [here](https://drive.google.com/file/d/1t7myBgcqcZ9ax_IT9QVk-vFH_l_o5UXB/view?usp=sharing) +# (caution, ~3.5GB). +# +# [![fts_explicit_accuracy](fts_explicit_accuracy.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOnRydWUsIm5vZnRzX2Jhc2VsaW5lIjpmYWxzZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D) +# [![nofts_baseline](nofts_baseline_accuracy.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6dHJ1ZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D) +# +# Note there could be around ~1% variation in performance from the tensorboard summaries generated by this notebook +# which uses DDP_SPAWN and 1 GPU. +# +# ``FinetuningScheduler`` expands the space of possible finetuning schedules and the composition of more sophisticated schedules can +# yield mariginal finetuning performance gains. That stated, it should be emphasized the primary utility of ``FinetuningScheduler`` is to grant +# greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTav3's implicit training +# run, a critical tuning transition point is immediately apparent: +# +# [![implicit_training_transition](implicit_training_transition.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6ZmFsc2UsImZ0c19pbXBsaWNpdCI6dHJ1ZX0%3D) +# +# Our val_loss begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our +# schedule, in phase 17 we're beginning tuning the attention parameters of our 10th encoder layer (of 11). Interesting! +# Though beyond the scope of this tutorial, it might be worth investigating these dynamics further and +# ``FinetuningScheduler`` allows one to do just that quite easily. +# +# %% [markdown] +# +# Note that though this example is intended to capture a common usage scenario, substantial variation is expected +# among use cases and models. +# In summary, ``FinetuningScheduler`` provides increased finetuning flexibility that can be useful in a variety of +# contexts from exploring model tuning behavior to maximizing performance. # %% [markdown] # ## Footnotes # diff --git a/lightning_examples/finetuning-scheduler/fts_explicit_accuracy.png b/lightning_examples/finetuning-scheduler/fts_explicit_accuracy.png new file mode 100644 index 0000000000000000000000000000000000000000..b5d8f554a7f7b9b87ed28c016ecfa8d0b1c36deb GIT binary patch literal 24409 zcmb4r1yogS*X<^x1Sthn8VONC1f)|$5U>!XLmH%!lui*45fA|Z5mD*xZjo+~?oJ7* zyEgCp{r|mp+;Q)?e)^4 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ziiwHI9^4l@hq4IZgKx0mMiGQNkvSci7P6>>gqwxTVfgzeV5krjfBy7I&Dhwf*Mh}h zXjIiY61Q#Pqqp^lNnJfU*>uF)8+!)ztHnP!Zz259+KC?$f`8KNH^>*StRe{QK?Pcs z>~m~pW7|Q@<3I0+q<%MlUSzX3CO2&<$jjT}SKEp7MQ-R&9qf6G_2Q2+n{ literal 0 HcmV?d00001 From 542980b33ae68ee7f50073f719f754b7115076ec Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Sat, 11 Dec 2021 13:13:22 -0800 Subject: [PATCH 10/44] minor spelling and stylistic updates --- .../finetuning-scheduler/finetuning-scheduler.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 968395d0c..cb8dc2913 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -133,6 +133,7 @@ # # diff --git a/lightning_examples/finetuning-scheduler/side_by_side_yaml.png 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6fb7ea3a3..db2b60aa8 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -92,7 +92,7 @@ # #

# -# **Tip:** Use of regex expressions can be convenient for specifying more complex schedules: +# **Tip:** Use of regex expressions can be convenient for specifying more complex schedules. Also, a per-phase base lr can be specified: # # ![emphasized_yaml](emphasized_yaml.png) # From 7b25d48774a7f159a3cb7fd49487c9bf0b38fb0a Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Thu, 13 Jan 2022 15:17:16 -0800 Subject: [PATCH 26/44] clarify finetuning schedule definition configuration options and update fts module requirement ref --- lightning_examples/finetuning-scheduler/.meta.yml | 1 + .../finetuning-scheduler/finetuning-scheduler.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 6954f680c..f2a81e2a1 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -17,5 +17,6 @@ requirements: - datasets - scikit-learn - sentencepiece + - git+git://github.com/speediedan/pytorch-lightning.git@9079069a7bb3bbc20f7c68fe7932b46bf76b21cf#egg=pytorch-lightning accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index db2b60aa8..fe6f3374d 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -51,6 +51,7 @@ # # Schedule definition is facilitated via the ``gen_ft_schedule`` method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as # desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will likely be less computationally efficient than a user-defined finetuning schedule but is useful for exploring a model's finetuning behavior and can serve as a good baseline for subsquent explicit schedule refinement. +# While the current version of ``FinetuningScheduler`` only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning rates can be set as demonstrated in the next section. # %% [markdown] # ## Specifying a Finetuning Schedule @@ -92,7 +93,7 @@ # #
# -# **Tip:** Use of regex expressions can be convenient for specifying more complex schedules. Also, a per-phase base lr can be specified: +# **Tip:** Use of regex expressions can be convenient for specifying more complex schedules. Also, a per-phase base maximum lr can be specified: # # ![emphasized_yaml](emphasized_yaml.png) # From 822ce2f3d23f4d9a64eeebea56cd1a1617f68a91 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Sat, 22 Jan 2022 15:52:01 -0800 Subject: [PATCH 27/44] update notebook author metadata --- lightning_examples/finetuning-scheduler/.meta.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index f2a81e2a1..8e8dbf3f5 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -1,5 +1,5 @@ title: Finetuning Scheduler -author: Dan Dale +author: "[Dan Dale](https://github.com/speediedan)" created: 2021-11-29 updated: 2021-11-29 license: CC BY-SA From 1b4a8fe287b95fd62d19cd8691b709bbd0cdbefe Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Sat, 22 Jan 2022 16:50:50 -0800 Subject: [PATCH 28/44] add links to finetuning-scheduler rtd documentation site --- .../finetuning-scheduler/.meta.yml | 7 +- .../finetuning-scheduler.py | 71 ++++++++++--------- 2 files changed, 41 insertions(+), 37 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 8e8dbf3f5..52aa85441 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -7,9 +7,10 @@ build: 1 tags: - finetuning description: | - This notebook introduces the FinetuningScheduler callback and demonstrates the use of FinetuningScheduler to finetune - a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task - of [SuperGLUE](https://super.gluebenchmark.com/) with iterative early-stopping defined according to a user-specified + This notebook introduces the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) + callback and demonstrates the use of it to finetune a small foundational model on the + [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of + [SuperGLUE](https://super.gluebenchmark.com/) with iterative early-stopping defined according to a user-specified schedule. It uses HuggingFace's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data and foundational model weights. requirements: diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index fe6f3374d..a49a3072c 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,8 +1,9 @@ # %% [markdown] # ## Scheduled Finetuning # -# Training with the ``FinetuningScheduler`` callback confers a host of benefits. It was created to meet the following -# objectives in roughly descending order of priority: +# The [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) callback expedites and enhances foundational model experimentation with flexible finetuning schedules. +# +# Training with the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) confers a host of benefits: # # - maximize finetuning flexibility # - expedite and facilitate exploration of model tuning dynamics in research @@ -10,7 +11,7 @@ # #
# -# Fundamentally, the ``FinetuningScheduler`` callback enables scheduled, multi-phase, +# Fundamentally, the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) callback enables scheduled, multi-phase, # finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize # foundational model knowledge retention while allowing (typically upper layers of) the model to # optimally adapt to new tasks during transfer learning [1, 2, 3](#f1) @@ -18,12 +19,12 @@ # #
# -# ``FinetuningScheduler`` orchestrates the gradual unfreezing +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) orchestrates the gradual unfreezing # of models via a finetuning schedule that is either implicitly generated (the default) or explicitly provided by the user # (more computationally efficient). Finetuning phase transitions are driven by -# ``FTSEarlyStopping`` criteria (a multi-phase -# extension of ``EarlyStopping``), user-specified epoch transitions or a composition of the two (the default mode). -# A ``FinetuningScheduler`` training session completes when the +# [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) +# criteria (a multi-phase extension of ``EarlyStopping``), user-specified epoch transitions or a composition of the two (the default mode). +# A [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) training session completes when the # final phase of the schedule has its stopping criteria met. See # the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/latest/extensions/generated/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. # @@ -35,8 +36,9 @@ # #
# -# If no finetuning schedule is provided by the user, ``FinetuningScheduler`` will generate a -# [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, using default ``FTSEarlyStopping`` and ``FTSCheckpoint`` callbacks with ``monitor=val_loss``. +# If no finetuning schedule is provided by the user, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) will generate a +# [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, +# using default [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) and [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSCheckpoint) callbacks with ``monitor=val_loss``. # #
# @@ -49,9 +51,9 @@ # %% [markdown] # ## The Default Finetuning Schedule # -# Schedule definition is facilitated via the ``gen_ft_schedule`` method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as +# Schedule definition is facilitated via the [gen_ft_schedule](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.SchedulingMixin.gen_ft_schedule) method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as # desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will likely be less computationally efficient than a user-defined finetuning schedule but is useful for exploring a model's finetuning behavior and can serve as a good baseline for subsquent explicit schedule refinement. -# While the current version of ``FinetuningScheduler`` only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning rates can be set as demonstrated in the next section. +# While the current version of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning rates can be set as demonstrated in the next section. # %% [markdown] # ## Specifying a Finetuning Schedule @@ -72,7 +74,7 @@ # ![side_by_side_yaml](side_by_side_yaml.png) # # 3. Once the finetuning schedule has been altered as desired, pass it to -# ``FinetuningScheduler`` to commence scheduled training: +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) to commence scheduled training: # # ```python # from pytorch_lightning import Trainer @@ -85,9 +87,9 @@ # ## Early-Stopping and Epoch-Driven Phase Transition Criteria # # -# By default, ``FTSEarlyStopping`` and epoch-driven -# transition criteria are composed. If a ``max_transition_epoch`` is specified for a given phase, the next finetuning phase will begin at that epoch unless ``FTSEarlyStopping`` criteria are met first. -# If ``FinetuningScheduler.epoch_transitions_only`` is ``True``, ``FTSEarlyStopping`` will not be used +# By default, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) and epoch-driven +# transition criteria are composed. If a ``max_transition_epoch`` is specified for a given phase, the next finetuning phase will begin at that epoch unless [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) criteria are met first. +# If [FinetuningScheduler.epoch_transitions_only](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.epoch_transitions_only) is ``True``, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) will not be used # and transitions will be exclusively epoch-driven. # # @@ -101,14 +103,15 @@ # # # -# The end-to-end example in this notebook ([Scheduled Finetuning For SuperGLUE](#superglue)) uses ``FinetuningScheduler`` in explicit mode to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). +# The end-to-end example in this notebook ([Scheduled Finetuning For SuperGLUE](#superglue)) uses [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) in explicit mode to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). +# Please see the [official Finetuning Scheduler documentation](https://finetuning-scheduler.readthedocs.io/en/latest/advanced/finetuning_scheduler.html) if you are interested in a similar [CLI-based example](https://finetuning-scheduler.readthedocs.io/en/latest/advanced/finetuning_scheduler.html#example-scheduled-finetuning-for-superglue) using the LightningCLI. # %% [markdown] # ## Resuming Scheduled Finetuning Training Sessions # # Resumption of scheduled finetuning training is identical to the continuation of -# [other training sessions](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) with the caveat that the provided checkpoint must have been saved by a ``FinetuningScheduler`` session. -# ``FinetuningScheduler`` uses ``FTSCheckpoint`` (an extension of ``ModelCheckpoint``) to maintain schedule state with special metadata. +# [other training sessions](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) with the caveat that the provided checkpoint must have been saved by a [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) session. +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) uses [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSCheckpoint) (an extension of ``ModelCheckpoint``) to maintain schedule state with special metadata. # # # ```python @@ -120,20 +123,20 @@ # # Training will resume at the depth/level of the provided checkpoint according the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions. # -# By default (``FinetuningScheduler.restore_best`` is ``True``), ``FinetuningScheduler`` will attempt to restore the best available checkpoint before finetuning depth transitions. +# By default ([FinetuningScheduler.restore_best](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html?highlight=restore_best#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.restore_best) is ``True``), [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) will attempt to restore the best available checkpoint before finetuning depth transitions. # # ```python # trainer = Trainer(callbacks=[FinetuningScheduler(new_incarnation_mode=True)]) # trainer.fit(..., ckpt_path="some/path/to/my_kth_best_checkpoint.ckpt") # ``` # -# To handle the edge case wherein one is resuming scheduled finetuning from a non-best checkpoint and the previous best checkpoints may not be accessible, setting ``FinetuningScheduler.new_incarnation_mode`` to +# To handle the edge case wherein one is resuming scheduled finetuning from a non-best checkpoint and the previous best checkpoints may not be accessible, setting [FinetuningScheduler.new_incarnation_mode](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html?highlight=new_Incarnation_mode#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.new_incarnation_mode) to # ``True`` as above will re-intialize the checkpoint state with a new best checkpoint at the resumption depth. # %% [markdown] #
# -# **Note:** Currently, _FinetuningScheduler_ only supports the following ``StrategyType``s: +# **Note:** Currently, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) only supports the following ``StrategyType``s: # # - ``DP`` # - ``DDP`` @@ -148,7 +151,7 @@ # # ## Scheduled Finetuning For SuperGLUE # -# The following example demonstrates the use of ``FinetuningScheduler`` to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. +# The following example demonstrates the use of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. # # %% @@ -454,7 +457,7 @@ def configure_callbacks(self): # %% # Let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it -# Please see the ``FinetuningScheduler`` documentation for a full description of the schedule format +# Please see the [FinetuningScheduler documentation](https://finetuning-scheduler.readthedocs.io/en/latest/advanced/finetuning_scheduler.html) for a full description of the schedule format ft_schedule_yaml = """ @@ -536,10 +539,10 @@ def configure_callbacks(self): # %% [markdown] # ### Callback Configuration # -# The only callback required to invoke the ``FinetuningScheduler`` is the ``FinetuningScheduler`` callback itself. -# Default versions of ``FTSCheckpoint`` and ``FTSEarlyStopping`` (if not specifying ``epoch_only_transitions``) will be -# included ([as discussed above](#basic_usage)) if not provided in the callbacks list. For demonstration purposes I'm -# including example configurations of all three callbacks below. +# The only callback required to invoke the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) is the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) callback itself. +# Default versions of [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSCheckpoint) and [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) +# (if not specifying ``epoch_only_transitions``) will be included ([as discussed above](#basic_usage)) if not provided +# in the callbacks list. For demonstration purposes I'm including example configurations of all three callbacks below. # %% # let's save our callback configurations for the explicit scenario since we'll be reusing the same @@ -595,10 +598,10 @@ def train() -> None: # with the LightningCLI. This allows us to avoid managing imports and support more complex configuration separated from # code. # -# Note that we'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping ``max_depth`` for +# Note that we'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping [max_depth](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html?highlight=max_depth#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) for # the implicit schedule will limit finetuning to just the last 4 parameters of the model, which is only a small fraction # of the parameters you'd want to tune for maximum performance. Since the implicit schedule is quite computationally -# intensive and most useful for exploring model behavior, leaving ``max_depth`` 1 allows us to demo implicit mode +# intensive and most useful for exploring model behavior, leaving [max_depth](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html?highlight=max_depth#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) 1 allows us to demo implicit mode # behavior while keeping the computational cost and runtime of this notebook reasonable. To review how a full implicit # mode run compares to the ``nofts_baseline`` and ``fts_explicit`` scenarios, please see the the following # [tensorboard experiment summary](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/). @@ -628,7 +631,7 @@ def train() -> None: # ### Reviewing the Training Results # # See the [tensorboard experiment summaries](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/) to get a sense -# of the relative computational and performance tradeoffs associated with these ``FinetuningScheduler`` configurations. +# of the relative computational and performance tradeoffs associated with these [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) configurations. # The summary compares a full ``fts_implicit`` execution to ``fts_explicit`` and ``nofts_baseline`` scenarios using DDP # training with 2 GPUs. The full logs/schedules for all three scenarios are available # [here](https://drive.google.com/file/d/1LrUcisRLHeJgh_BDOOD_GUBPp5iHAkoR/view?usp=sharing) and the checkpoints @@ -641,8 +644,8 @@ def train() -> None: # Note there could be around ~1% variation in performance from the tensorboard summaries generated by this notebook # which uses DDP_SPAWN and 1 GPU. # -# ``FinetuningScheduler`` expands the space of possible finetuning schedules and the composition of more sophisticated schedules can -# yield marginal finetuning performance gains. That stated, it should be emphasized the primary utility of ``FinetuningScheduler`` is to grant +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) expands the space of possible finetuning schedules and the composition of more sophisticated schedules can +# yield marginal finetuning performance gains. That stated, it should be emphasized the primary utility of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) is to grant # greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTa-v3's implicit training # run, a critical tuning transition point is immediately apparent: # @@ -651,13 +654,13 @@ def train() -> None: # Our `val_loss` begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our # schedule, in phase 17 we're beginning tuning the attention parameters of our 10th encoder layer (of 11). Interesting! # Though beyond the scope of this tutorial, it might be worth investigating these dynamics further and -# ``FinetuningScheduler`` allows one to do just that quite easily. +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) allows one to do just that quite easily. # # %% [markdown] # # Note that though this example is intended to capture a common usage scenario, substantial variation is expected # among use cases and models. -# In summary, ``FinetuningScheduler`` provides increased finetuning flexibility that can be useful in a variety of +# In summary, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) provides increased finetuning flexibility that can be useful in a variety of # contexts from exploring model tuning behavior to maximizing performance. # %% [markdown] # ## Footnotes From c4a3dd963f781ccef0f3def916ab727298fc21cc Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Thu, 27 Jan 2022 12:52:20 -0800 Subject: [PATCH 29/44] update finetuning_scheduler dependency sha --- lightning_examples/finetuning-scheduler/.meta.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 52aa85441..ef6fa1550 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -18,6 +18,6 @@ requirements: - datasets - scikit-learn - sentencepiece - - git+git://github.com/speediedan/pytorch-lightning.git@9079069a7bb3bbc20f7c68fe7932b46bf76b21cf#egg=pytorch-lightning + - git+https://github.com/speediedan/pytorch-lightning.git@90e1976a0099f100b1acefcb9b9cc69ab7ec7ac1#egg=pytorch-lightning accelerator: - GPU From 98c55dfce022ffdf4da296245d7331b7811a577a Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Wed, 9 Feb 2022 17:09:40 -0800 Subject: [PATCH 30/44] updated tutorial with initial version of finetuning-scheduler dependency --- .../finetuning-scheduler/.meta.yml | 8 +- .../finetuning-scheduler.py | 78 +++++++++---------- 2 files changed, 41 insertions(+), 45 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index ef6fa1550..f2814d3fa 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -7,17 +7,13 @@ build: 1 tags: - finetuning description: | - This notebook introduces the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) + This notebook introduces the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback and demonstrates the use of it to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/) with iterative early-stopping defined according to a user-specified schedule. It uses HuggingFace's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data and foundational model weights. requirements: - - transformers - - datasets - - scikit-learn - - sentencepiece - - git+https://github.com/speediedan/pytorch-lightning.git@90e1976a0099f100b1acefcb9b9cc69ab7ec7ac1#egg=pytorch-lightning + - git+https://github.com/speediedan/finetuning-scheduler.git@736ae0e481729ca2519fc7abf0d38b7f9ba425fe#egg=finetuning-scheduler[examples] accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index a49a3072c..3e6aefb6c 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,9 +1,9 @@ # %% [markdown] # ## Scheduled Finetuning # -# The [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) callback expedites and enhances foundational model experimentation with flexible finetuning schedules. +# The [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback expedites and enhances foundational model experimentation with flexible finetuning schedules. # -# Training with the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) confers a host of benefits: +# Training with the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) confers a host of benefits: # # - maximize finetuning flexibility # - expedite and facilitate exploration of model tuning dynamics in research @@ -11,7 +11,7 @@ # #
# -# Fundamentally, the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) callback enables scheduled, multi-phase, +# Fundamentally, the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback enables scheduled, multi-phase, # finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize # foundational model knowledge retention while allowing (typically upper layers of) the model to # optimally adapt to new tasks during transfer learning [1, 2, 3](#f1) @@ -19,12 +19,12 @@ # #
# -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) orchestrates the gradual unfreezing +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) orchestrates the gradual unfreezing # of models via a finetuning schedule that is either implicitly generated (the default) or explicitly provided by the user # (more computationally efficient). Finetuning phase transitions are driven by -# [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) +# [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) # criteria (a multi-phase extension of ``EarlyStopping``), user-specified epoch transitions or a composition of the two (the default mode). -# A [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) training session completes when the +# A [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) training session completes when the # final phase of the schedule has its stopping criteria met. See # the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/latest/extensions/generated/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. # @@ -36,36 +36,36 @@ # #
# -# If no finetuning schedule is provided by the user, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) will generate a +# If no finetuning schedule is provided by the user, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) will generate a # [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, -# using default [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) and [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSCheckpoint) callbacks with ``monitor=val_loss``. +# using default [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) and [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) callbacks with ``monitor=val_loss``. # #
# # ```python # from pytorch_lightning import Trainer -# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# from finetuning_scheduler import FinetuningScheduler # trainer = Trainer(callbacks=[FinetuningScheduler()]) # ``` # %% [markdown] # ## The Default Finetuning Schedule # -# Schedule definition is facilitated via the [gen_ft_schedule](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.SchedulingMixin.gen_ft_schedule) method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as +# Schedule definition is facilitated via the [gen_ft_schedule](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.SchedulingMixin.gen_ft_schedule) method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as # desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will likely be less computationally efficient than a user-defined finetuning schedule but is useful for exploring a model's finetuning behavior and can serve as a good baseline for subsquent explicit schedule refinement. -# While the current version of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning rates can be set as demonstrated in the next section. +# While the current version of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning rates can be set as demonstrated in the next section. # %% [markdown] # ## Specifying a Finetuning Schedule # # To specify a finetuning schedule, it's convenient to first generate the default schedule and then alter the thawed/unfrozen parameter groups associated with each finetuning phase as desired. Finetuning phases are zero-indexed and executed in ascending order. # -# 1. Generate the default schedule to ``Trainer.log_dir`` with the name -# (``lightning_module.__class__.__name__``)_ft_schedule.yaml +# 1. First, generate the default schedule to ``Trainer.log_dir``. It will be named after your +# ``LightningModule`` subclass with the suffix ``_ft_schedule.yaml``. # # ```python # from pytorch_lightning import Trainer -# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# from finetuning_scheduler import FinetuningScheduler # trainer = Trainer(callbacks=[FinetuningScheduler(gen_ft_sched_only=True)]) # ``` # @@ -74,11 +74,11 @@ # ![side_by_side_yaml](side_by_side_yaml.png) # # 3. Once the finetuning schedule has been altered as desired, pass it to -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) to commence scheduled training: +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) to commence scheduled training: # # ```python # from pytorch_lightning import Trainer -# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# from finetuning_scheduler import FinetuningScheduler # # trainer = Trainer(callbacks=[FinetuningScheduler(ft_schedule="/path/to/my/schedule/my_schedule.yaml")]) # ``` @@ -87,9 +87,9 @@ # ## Early-Stopping and Epoch-Driven Phase Transition Criteria # # -# By default, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) and epoch-driven -# transition criteria are composed. If a ``max_transition_epoch`` is specified for a given phase, the next finetuning phase will begin at that epoch unless [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) criteria are met first. -# If [FinetuningScheduler.epoch_transitions_only](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.epoch_transitions_only) is ``True``, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) will not be used +# By default, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) and epoch-driven +# transition criteria are composed. If a ``max_transition_epoch`` is specified for a given phase, the next finetuning phase will begin at that epoch unless [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) criteria are met first. +# If [FinetuningScheduler.epoch_transitions_only](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler.params.epoch_transitions_only) is ``True``, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) will not be used # and transitions will be exclusively epoch-driven. # # @@ -103,40 +103,40 @@ # # # -# The end-to-end example in this notebook ([Scheduled Finetuning For SuperGLUE](#superglue)) uses [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) in explicit mode to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). -# Please see the [official Finetuning Scheduler documentation](https://finetuning-scheduler.readthedocs.io/en/latest/advanced/finetuning_scheduler.html) if you are interested in a similar [CLI-based example](https://finetuning-scheduler.readthedocs.io/en/latest/advanced/finetuning_scheduler.html#example-scheduled-finetuning-for-superglue) using the LightningCLI. +# The end-to-end example in this notebook ([Scheduled Finetuning For SuperGLUE](#superglue)) uses [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) in explicit mode to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). +# Please see the [official Finetuning Scheduler documentation](https://finetuning-scheduler.readthedocs.io/en/latest/index.html) if you are interested in a similar [CLI-based example](https://finetuning-scheduler.readthedocs.io/en/latest/index.html#scheduled-finetuning-superglue) using the LightningCLI. # %% [markdown] # ## Resuming Scheduled Finetuning Training Sessions # # Resumption of scheduled finetuning training is identical to the continuation of -# [other training sessions](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) with the caveat that the provided checkpoint must have been saved by a [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) session. -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) uses [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSCheckpoint) (an extension of ``ModelCheckpoint``) to maintain schedule state with special metadata. +# [other training sessions](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) with the caveat that the provided checkpoint must have been saved by a [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) session. +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) uses [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) (an extension of ``ModelCheckpoint``) to maintain schedule state with special metadata. # # # ```python # from pytorch_lightning import Trainer -# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# from finetuning_scheduler import FinetuningScheduler # trainer = Trainer(callbacks=[FinetuningScheduler()]) # trainer.fit(..., ckpt_path="some/path/to/my_checkpoint.ckpt") # ``` # # Training will resume at the depth/level of the provided checkpoint according the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions. # -# By default ([FinetuningScheduler.restore_best](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html?highlight=restore_best#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.restore_best) is ``True``), [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) will attempt to restore the best available checkpoint before finetuning depth transitions. +# By default ([FinetuningScheduler.restore_best](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html?highlight=restore_best#finetuning_scheduler.fts.FinetuningScheduler.params.restore_best) is ``True``), [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) will attempt to restore the best available checkpoint before finetuning depth transitions. # # ```python # trainer = Trainer(callbacks=[FinetuningScheduler(new_incarnation_mode=True)]) # trainer.fit(..., ckpt_path="some/path/to/my_kth_best_checkpoint.ckpt") # ``` # -# To handle the edge case wherein one is resuming scheduled finetuning from a non-best checkpoint and the previous best checkpoints may not be accessible, setting [FinetuningScheduler.new_incarnation_mode](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html?highlight=new_Incarnation_mode#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.new_incarnation_mode) to +# To handle the edge case wherein one is resuming scheduled finetuning from a non-best checkpoint and the previous best checkpoints may not be accessible, setting [FinetuningScheduler.new_incarnation_mode](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html?highlight=new_Incarnation_mode#finetuning_scheduler.fts.FinetuningScheduler.params.new_incarnation_mode) to # ``True`` as above will re-intialize the checkpoint state with a new best checkpoint at the resumption depth. # %% [markdown] #
# -# **Note:** Currently, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) only supports the following ``StrategyType``s: +# **Note:** Currently, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) supports the following ``StrategyType``s: # # - ``DP`` # - ``DDP`` @@ -151,7 +151,7 @@ # # ## Scheduled Finetuning For SuperGLUE # -# The following example demonstrates the use of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. +# The following example demonstrates the use of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. # # %% @@ -183,7 +183,7 @@ def mock_register_module(key: str, require_fqn: bool = False) -> List: if key.lower() == "finetuningscheduler": - mod = import_module("pytorch_lightning.callbacks.finetuning_scheduler") + mod = import_module("finetuning_scheduler") MOCK_REGISTRY.register_classes(mod, pl.callbacks.Callback) else: raise MisconfigurationException(f"user module key '{key}' not found") @@ -457,7 +457,7 @@ def configure_callbacks(self): # %% # Let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it -# Please see the [FinetuningScheduler documentation](https://finetuning-scheduler.readthedocs.io/en/latest/advanced/finetuning_scheduler.html) for a full description of the schedule format +# Please see the [FinetuningScheduler documentation](https://finetuning-scheduler.readthedocs.io/en/latest/index.html) for a full description of the schedule format ft_schedule_yaml = """ @@ -539,8 +539,8 @@ def configure_callbacks(self): # %% [markdown] # ### Callback Configuration # -# The only callback required to invoke the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) is the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) callback itself. -# Default versions of [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSCheckpoint) and [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.html#pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSEarlyStopping) +# The only callback required to invoke the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback itself. +# Default versions of [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) and [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) # (if not specifying ``epoch_only_transitions``) will be included ([as discussed above](#basic_usage)) if not provided # in the callbacks list. For demonstration purposes I'm including example configurations of all three callbacks below. @@ -598,10 +598,10 @@ def train() -> None: # with the LightningCLI. This allows us to avoid managing imports and support more complex configuration separated from # code. # -# Note that we'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping [max_depth](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html?highlight=max_depth#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) for +# Note that we'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping [max_depth](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html?highlight=max_depth#finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) for # the implicit schedule will limit finetuning to just the last 4 parameters of the model, which is only a small fraction # of the parameters you'd want to tune for maximum performance. Since the implicit schedule is quite computationally -# intensive and most useful for exploring model behavior, leaving [max_depth](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html?highlight=max_depth#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) 1 allows us to demo implicit mode +# intensive and most useful for exploring model behavior, leaving [max_depth](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html?highlight=max_depth#finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) 1 allows us to demo implicit mode # behavior while keeping the computational cost and runtime of this notebook reasonable. To review how a full implicit # mode run compares to the ``nofts_baseline`` and ``fts_explicit`` scenarios, please see the the following # [tensorboard experiment summary](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/). @@ -631,7 +631,7 @@ def train() -> None: # ### Reviewing the Training Results # # See the [tensorboard experiment summaries](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/) to get a sense -# of the relative computational and performance tradeoffs associated with these [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) configurations. +# of the relative computational and performance tradeoffs associated with these [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) configurations. # The summary compares a full ``fts_implicit`` execution to ``fts_explicit`` and ``nofts_baseline`` scenarios using DDP # training with 2 GPUs. The full logs/schedules for all three scenarios are available # [here](https://drive.google.com/file/d/1LrUcisRLHeJgh_BDOOD_GUBPp5iHAkoR/view?usp=sharing) and the checkpoints @@ -644,8 +644,8 @@ def train() -> None: # Note there could be around ~1% variation in performance from the tensorboard summaries generated by this notebook # which uses DDP_SPAWN and 1 GPU. # -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) expands the space of possible finetuning schedules and the composition of more sophisticated schedules can -# yield marginal finetuning performance gains. That stated, it should be emphasized the primary utility of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) is to grant +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) expands the space of possible finetuning schedules and the composition of more sophisticated schedules can +# yield marginal finetuning performance gains. That stated, it should be emphasized the primary utility of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is to grant # greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTa-v3's implicit training # run, a critical tuning transition point is immediately apparent: # @@ -654,13 +654,13 @@ def train() -> None: # Our `val_loss` begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our # schedule, in phase 17 we're beginning tuning the attention parameters of our 10th encoder layer (of 11). Interesting! # Though beyond the scope of this tutorial, it might be worth investigating these dynamics further and -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) allows one to do just that quite easily. +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) allows one to do just that quite easily. # # %% [markdown] # # Note that though this example is intended to capture a common usage scenario, substantial variation is expected # among use cases and models. -# In summary, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.finetuning_scheduler.fts.html#pytorch_lightning.callbacks.finetuning_scheduler.fts.FinetuningScheduler) provides increased finetuning flexibility that can be useful in a variety of +# In summary, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) provides increased finetuning flexibility that can be useful in a variety of # contexts from exploring model tuning behavior to maximizing performance. # %% [markdown] # ## Footnotes From cdc332ad0e82cdfe355a19ca5afa1575df07a93f Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Fri, 18 Feb 2022 13:35:30 -0800 Subject: [PATCH 31/44] update finetuning-scheduler commit sha, simplify notebook dependency with finetuning-scheduler[examples] extra, accommodate acceleratorconnector refactor --- lightning_examples/finetuning-scheduler/.meta.yml | 6 +++--- .../finetuning-scheduler/finetuning-scheduler.py | 13 +++++++------ 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index f2814d3fa..e2287a647 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -11,9 +11,9 @@ description: | callback and demonstrates the use of it to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/) with iterative early-stopping defined according to a user-specified - schedule. It uses HuggingFace's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data - and foundational model weights. + schedule. It uses Hugging Face's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data + and foundational model weights. The required dependencies are installed via the finetuning-scheduler ``[examples]`` extra. requirements: - - git+https://github.com/speediedan/finetuning-scheduler.git@736ae0e481729ca2519fc7abf0d38b7f9ba425fe#egg=finetuning-scheduler[examples] + - git+https://github.com/speediedan/finetuning-scheduler.git@ff8c9f7176de4a10bb3d0d682fef5658b83d9462#egg=finetuning-scheduler[examples] accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 3e6aefb6c..4ff03b8cd 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,13 +1,13 @@ # %% [markdown] # ## Scheduled Finetuning # -# The [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback expedites and enhances foundational model experimentation with flexible finetuning schedules. +# The [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback accelerates and enhances model experimentation with flexible finetuning schedules. # -# Training with the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) confers a host of benefits: +# Training with the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is simple and confers a host of benefits: # -# - maximize finetuning flexibility -# - expedite and facilitate exploration of model tuning dynamics in research -# - allow marginal performance improvements of finetuned models +# - it dramatically increases finetuning flexibility +# - expedites and facilitates exploration of model tuning dynamics +# - enables marginal performance improvements of finetuned models # #
# @@ -574,6 +574,7 @@ def train() -> None: enable_progress_bar=enable_progress_bar, max_epochs=100, precision=16, + strategy="dp", accelerator="gpu", devices=1, callbacks=callbacks, @@ -642,7 +643,7 @@ def train() -> None: # [![nofts_baseline](nofts_baseline_accuracy.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6dHJ1ZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D) # # Note there could be around ~1% variation in performance from the tensorboard summaries generated by this notebook -# which uses DDP_SPAWN and 1 GPU. +# which uses DP and 1 GPU. # # [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) expands the space of possible finetuning schedules and the composition of more sophisticated schedules can # yield marginal finetuning performance gains. That stated, it should be emphasized the primary utility of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is to grant From 64ef2709e2282cf079f9deb8cb4fa1c12f985561 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Tue, 29 Mar 2022 14:10:40 -0700 Subject: [PATCH 32/44] sync finetuning_scheduler tutorial for pl 1.6.0rc1 --- lightning_examples/finetuning-scheduler/.meta.yml | 3 ++- .../finetuning-scheduler/finetuning-scheduler.py | 11 ++++++----- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index e2287a647..6ad58c5cc 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -14,6 +14,7 @@ description: | schedule. It uses Hugging Face's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data and foundational model weights. The required dependencies are installed via the finetuning-scheduler ``[examples]`` extra. requirements: - - git+https://github.com/speediedan/finetuning-scheduler.git@ff8c9f7176de4a10bb3d0d682fef5658b83d9462#egg=finetuning-scheduler[examples] + - git+https://github.com/speediedan/finetuning-scheduler.git@ce4f44acdbb741317370c23ed9c70b131d36540e#egg=finetuning-scheduler[examples] + - git+https://github.com/huggingface/transformers.git@2f4cea0899b0f2c2988f3da30dc89deaf6af7fcb#egg=transformers accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 4ff03b8cd..5e7274cdc 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -26,7 +26,7 @@ # criteria (a multi-phase extension of ``EarlyStopping``), user-specified epoch transitions or a composition of the two (the default mode). # A [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) training session completes when the # final phase of the schedule has its stopping criteria met. See -# the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/latest/extensions/generated/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. +# the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. # # ![FinetuningScheduler explicit loss animation](fts_explicit_loss_anim.gif) @@ -126,12 +126,13 @@ # By default ([FinetuningScheduler.restore_best](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html?highlight=restore_best#finetuning_scheduler.fts.FinetuningScheduler.params.restore_best) is ``True``), [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) will attempt to restore the best available checkpoint before finetuning depth transitions. # # ```python -# trainer = Trainer(callbacks=[FinetuningScheduler(new_incarnation_mode=True)]) +# trainer = Trainer(callbacks=[FinetuningScheduler()]) # trainer.fit(..., ckpt_path="some/path/to/my_kth_best_checkpoint.ckpt") # ``` # -# To handle the edge case wherein one is resuming scheduled finetuning from a non-best checkpoint and the previous best checkpoints may not be accessible, setting [FinetuningScheduler.new_incarnation_mode](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html?highlight=new_Incarnation_mode#finetuning_scheduler.fts.FinetuningScheduler.params.new_incarnation_mode) to -# ``True`` as above will re-intialize the checkpoint state with a new best checkpoint at the resumption depth. +# Note that similar to the behavior of [ModelCheckpoint](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.ModelCheckpoint.html), (specifically [this PR](https://github.com/PyTorchLightning/pytorch-lightning/pull/12045)), +# when resuming training with a different [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) ``dirpath`` from the provided +# checkpoint, the new training session's checkpoint state will be re-initialized at the resumption depth with the provided checkpoint being set as the best checkpoint. # %% [markdown] #
@@ -486,7 +487,7 @@ def configure_callbacks(self): f.write(ft_schedule_yaml) # %% -datasets.set_progress_bar_enabled(False) +datasets.logging.disable_progress_bar() pl.seed_everything(42) dm = RteBoolqDataModule(model_name_or_path="microsoft/deberta-v3-base", tokenizers_parallelism=True) From 7f6c7ffb79ffd99494928babbdc25ca49bca4f46 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Wed, 30 Mar 2022 12:15:32 -0700 Subject: [PATCH 33/44] update commit sha to point to fts with official pl 1.6.0 release --- lightning_examples/finetuning-scheduler/.meta.yml | 4 ++-- .../finetuning-scheduler/finetuning-scheduler.py | 12 ++++++++++++ 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 6ad58c5cc..64cbf9eea 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -1,7 +1,7 @@ title: Finetuning Scheduler author: "[Dan Dale](https://github.com/speediedan)" created: 2021-11-29 -updated: 2021-11-29 +updated: 2022-03-29 license: CC BY-SA build: 1 tags: @@ -14,7 +14,7 @@ description: | schedule. It uses Hugging Face's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data and foundational model weights. The required dependencies are installed via the finetuning-scheduler ``[examples]`` extra. requirements: - - git+https://github.com/speediedan/finetuning-scheduler.git@ce4f44acdbb741317370c23ed9c70b131d36540e#egg=finetuning-scheduler[examples] + - git+https://github.com/speediedan/finetuning-scheduler.git@10d4aa88aae437d9275ba4cea13836c350045900#egg=finetuning-scheduler[examples] - git+https://github.com/huggingface/transformers.git@2f4cea0899b0f2c2988f3da30dc89deaf6af7fcb#egg=transformers accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 5e7274cdc..173344eba 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,3 +1,15 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: -all +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.7 +# --- + # %% [markdown] # ## Scheduled Finetuning # From 835d15ee94467c02cb8e49991d02e428e9885c21 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Thu, 7 Apr 2022 17:54:39 -0700 Subject: [PATCH 34/44] require only finetuning-scheduler dependency now that it is available on pypi --- lightning_examples/finetuning-scheduler/.meta.yml | 3 +-- .../finetuning-scheduler/finetuning-scheduler.py | 12 ------------ 2 files changed, 1 insertion(+), 14 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 64cbf9eea..552d05fde 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -14,7 +14,6 @@ description: | schedule. It uses Hugging Face's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data and foundational model weights. The required dependencies are installed via the finetuning-scheduler ``[examples]`` extra. requirements: - - git+https://github.com/speediedan/finetuning-scheduler.git@10d4aa88aae437d9275ba4cea13836c350045900#egg=finetuning-scheduler[examples] - - git+https://github.com/huggingface/transformers.git@2f4cea0899b0f2c2988f3da30dc89deaf6af7fcb#egg=transformers + - finetuning-scheduler[examples] accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 173344eba..5e7274cdc 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,15 +1,3 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: -all -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.7 -# --- - # %% [markdown] # ## Scheduled Finetuning # From f6ec983db1729dec42dce9f0f2fb0d0ae081f527 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Mon, 18 Apr 2022 13:36:52 -0700 Subject: [PATCH 35/44] update fts documentation to point to stable version (0.1.1) --- .../finetuning-scheduler/.meta.yml | 2 +- .../finetuning-scheduler.py | 80 +++++++++++-------- 2 files changed, 47 insertions(+), 35 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 552d05fde..d4f6e477a 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -7,7 +7,7 @@ build: 1 tags: - finetuning description: | - This notebook introduces the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) + This notebook introduces the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback and demonstrates the use of it to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/) with iterative early-stopping defined according to a user-specified diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 5e7274cdc..cf4e6f959 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,9 +1,21 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: -all +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.8 +# --- + # %% [markdown] # ## Scheduled Finetuning # -# The [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback accelerates and enhances model experimentation with flexible finetuning schedules. +# The [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback accelerates and enhances model experimentation with flexible finetuning schedules. # -# Training with the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is simple and confers a host of benefits: +# Training with the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is simple and confers a host of benefits: # # - it dramatically increases finetuning flexibility # - expedites and facilitates exploration of model tuning dynamics @@ -11,7 +23,7 @@ # #
# -# Fundamentally, the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback enables scheduled, multi-phase, +# Fundamentally, the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback enables scheduled, multi-phase, # finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize # foundational model knowledge retention while allowing (typically upper layers of) the model to # optimally adapt to new tasks during transfer learning [1, 2, 3](#f1) @@ -19,14 +31,14 @@ # #
# -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) orchestrates the gradual unfreezing +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) orchestrates the gradual unfreezing # of models via a finetuning schedule that is either implicitly generated (the default) or explicitly provided by the user # (more computationally efficient). Finetuning phase transitions are driven by -# [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) +# [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) # criteria (a multi-phase extension of ``EarlyStopping``), user-specified epoch transitions or a composition of the two (the default mode). -# A [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) training session completes when the +# A [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) training session completes when the # final phase of the schedule has its stopping criteria met. See -# the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. +# the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. # # ![FinetuningScheduler explicit loss animation](fts_explicit_loss_anim.gif) @@ -36,9 +48,9 @@ # #
# -# If no finetuning schedule is provided by the user, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) will generate a +# If no finetuning schedule is provided by the user, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) will generate a # [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, -# using default [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) and [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) callbacks with ``monitor=val_loss``. +# using default [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) and [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) callbacks with ``monitor=val_loss``. # #
# @@ -51,9 +63,9 @@ # %% [markdown] # ## The Default Finetuning Schedule # -# Schedule definition is facilitated via the [gen_ft_schedule](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.SchedulingMixin.gen_ft_schedule) method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as +# Schedule definition is facilitated via the [gen_ft_schedule](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.SchedulingMixin.gen_ft_schedule) method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as # desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will likely be less computationally efficient than a user-defined finetuning schedule but is useful for exploring a model's finetuning behavior and can serve as a good baseline for subsquent explicit schedule refinement. -# While the current version of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning rates can be set as demonstrated in the next section. +# While the current version of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning rates can be set as demonstrated in the next section. # %% [markdown] # ## Specifying a Finetuning Schedule @@ -74,7 +86,7 @@ # ![side_by_side_yaml](side_by_side_yaml.png) # # 3. Once the finetuning schedule has been altered as desired, pass it to -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) to commence scheduled training: +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) to commence scheduled training: # # ```python # from pytorch_lightning import Trainer @@ -87,9 +99,9 @@ # ## Early-Stopping and Epoch-Driven Phase Transition Criteria # # -# By default, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) and epoch-driven -# transition criteria are composed. If a ``max_transition_epoch`` is specified for a given phase, the next finetuning phase will begin at that epoch unless [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) criteria are met first. -# If [FinetuningScheduler.epoch_transitions_only](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler.params.epoch_transitions_only) is ``True``, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) will not be used +# By default, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) and epoch-driven +# transition criteria are composed. If a ``max_transition_epoch`` is specified for a given phase, the next finetuning phase will begin at that epoch unless [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) criteria are met first. +# If [FinetuningScheduler.epoch_transitions_only](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler.params.epoch_transitions_only) is ``True``, [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) will not be used # and transitions will be exclusively epoch-driven. # # @@ -103,15 +115,15 @@ # # # -# The end-to-end example in this notebook ([Scheduled Finetuning For SuperGLUE](#superglue)) uses [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) in explicit mode to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). -# Please see the [official Finetuning Scheduler documentation](https://finetuning-scheduler.readthedocs.io/en/latest/index.html) if you are interested in a similar [CLI-based example](https://finetuning-scheduler.readthedocs.io/en/latest/index.html#scheduled-finetuning-superglue) using the LightningCLI. +# The end-to-end example in this notebook ([Scheduled Finetuning For SuperGLUE](#superglue)) uses [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) in explicit mode to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). +# Please see the [official Finetuning Scheduler documentation](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) if you are interested in a similar [CLI-based example](https://finetuning-scheduler.readthedocs.io/en/stable/index.html#scheduled-finetuning-superglue) using the LightningCLI. # %% [markdown] # ## Resuming Scheduled Finetuning Training Sessions # # Resumption of scheduled finetuning training is identical to the continuation of -# [other training sessions](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) with the caveat that the provided checkpoint must have been saved by a [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) session. -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) uses [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) (an extension of ``ModelCheckpoint``) to maintain schedule state with special metadata. +# [other training sessions](https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html) with the caveat that the provided checkpoint must have been saved by a [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) session. +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) uses [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) (an extension of ``ModelCheckpoint``) to maintain schedule state with special metadata. # # # ```python @@ -123,21 +135,21 @@ # # Training will resume at the depth/level of the provided checkpoint according the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions. # -# By default ([FinetuningScheduler.restore_best](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html?highlight=restore_best#finetuning_scheduler.fts.FinetuningScheduler.params.restore_best) is ``True``), [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) will attempt to restore the best available checkpoint before finetuning depth transitions. +# By default ([FinetuningScheduler.restore_best](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html?highlight=restore_best#finetuning_scheduler.fts.FinetuningScheduler.params.restore_best) is ``True``), [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) will attempt to restore the best available checkpoint before finetuning depth transitions. # # ```python # trainer = Trainer(callbacks=[FinetuningScheduler()]) # trainer.fit(..., ckpt_path="some/path/to/my_kth_best_checkpoint.ckpt") # ``` # -# Note that similar to the behavior of [ModelCheckpoint](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.ModelCheckpoint.html), (specifically [this PR](https://github.com/PyTorchLightning/pytorch-lightning/pull/12045)), -# when resuming training with a different [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) ``dirpath`` from the provided +# Note that similar to the behavior of [ModelCheckpoint](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.ModelCheckpoint.html), (specifically [this PR](https://github.com/PyTorchLightning/pytorch-lightning/pull/12045)), +# when resuming training with a different [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) ``dirpath`` from the provided # checkpoint, the new training session's checkpoint state will be re-initialized at the resumption depth with the provided checkpoint being set as the best checkpoint. # %% [markdown] #
# -# **Note:** Currently, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) supports the following ``StrategyType``s: +# **Note:** Currently, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) supports the following ``StrategyType``s: # # - ``DP`` # - ``DDP`` @@ -152,7 +164,7 @@ # # ## Scheduled Finetuning For SuperGLUE # -# The following example demonstrates the use of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. +# The following example demonstrates the use of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. # # %% @@ -458,7 +470,7 @@ def configure_callbacks(self): # %% # Let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it -# Please see the [FinetuningScheduler documentation](https://finetuning-scheduler.readthedocs.io/en/latest/index.html) for a full description of the schedule format +# Please see the [FinetuningScheduler documentation](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) for a full description of the schedule format ft_schedule_yaml = """ @@ -540,8 +552,8 @@ def configure_callbacks(self): # %% [markdown] # ### Callback Configuration # -# The only callback required to invoke the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback itself. -# Default versions of [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) and [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) +# The only callback required to invoke the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback itself. +# Default versions of [FTSCheckpoint](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSCheckpoint) and [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) # (if not specifying ``epoch_only_transitions``) will be included ([as discussed above](#basic_usage)) if not provided # in the callbacks list. For demonstration purposes I'm including example configurations of all three callbacks below. @@ -600,10 +612,10 @@ def train() -> None: # with the LightningCLI. This allows us to avoid managing imports and support more complex configuration separated from # code. # -# Note that we'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping [max_depth](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html?highlight=max_depth#finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) for +# Note that we'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping [max_depth](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html?highlight=max_depth#finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) for # the implicit schedule will limit finetuning to just the last 4 parameters of the model, which is only a small fraction # of the parameters you'd want to tune for maximum performance. Since the implicit schedule is quite computationally -# intensive and most useful for exploring model behavior, leaving [max_depth](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html?highlight=max_depth#finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) 1 allows us to demo implicit mode +# intensive and most useful for exploring model behavior, leaving [max_depth](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html?highlight=max_depth#finetuning_scheduler.fts.FinetuningScheduler.params.max_depth) 1 allows us to demo implicit mode # behavior while keeping the computational cost and runtime of this notebook reasonable. To review how a full implicit # mode run compares to the ``nofts_baseline`` and ``fts_explicit`` scenarios, please see the the following # [tensorboard experiment summary](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/). @@ -633,7 +645,7 @@ def train() -> None: # ### Reviewing the Training Results # # See the [tensorboard experiment summaries](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/) to get a sense -# of the relative computational and performance tradeoffs associated with these [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) configurations. +# of the relative computational and performance tradeoffs associated with these [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) configurations. # The summary compares a full ``fts_implicit`` execution to ``fts_explicit`` and ``nofts_baseline`` scenarios using DDP # training with 2 GPUs. The full logs/schedules for all three scenarios are available # [here](https://drive.google.com/file/d/1LrUcisRLHeJgh_BDOOD_GUBPp5iHAkoR/view?usp=sharing) and the checkpoints @@ -646,8 +658,8 @@ def train() -> None: # Note there could be around ~1% variation in performance from the tensorboard summaries generated by this notebook # which uses DP and 1 GPU. # -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) expands the space of possible finetuning schedules and the composition of more sophisticated schedules can -# yield marginal finetuning performance gains. That stated, it should be emphasized the primary utility of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is to grant +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) expands the space of possible finetuning schedules and the composition of more sophisticated schedules can +# yield marginal finetuning performance gains. That stated, it should be emphasized the primary utility of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is to grant # greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTa-v3's implicit training # run, a critical tuning transition point is immediately apparent: # @@ -656,13 +668,13 @@ def train() -> None: # Our `val_loss` begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our # schedule, in phase 17 we're beginning tuning the attention parameters of our 10th encoder layer (of 11). Interesting! # Though beyond the scope of this tutorial, it might be worth investigating these dynamics further and -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) allows one to do just that quite easily. +# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) allows one to do just that quite easily. # # %% [markdown] # # Note that though this example is intended to capture a common usage scenario, substantial variation is expected # among use cases and models. -# In summary, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/latest/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) provides increased finetuning flexibility that can be useful in a variety of +# In summary, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) provides increased finetuning flexibility that can be useful in a variety of # contexts from exploring model tuning behavior to maximizing performance. # %% [markdown] # ## Footnotes From e7c3838ec8099485ce6a9c8d8603fe496cf8e1a9 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Tue, 19 Apr 2022 14:01:01 -0700 Subject: [PATCH 36/44] implement workaround for cuda toolkit build difference of torch and torchvision in CI that was resulting in a segfault triggered by sentencepiece upon pytorch_lightning import --- lightning_examples/finetuning-scheduler/.meta.yml | 2 +- .../finetuning-scheduler/finetuning-scheduler.py | 13 +------------ 2 files changed, 2 insertions(+), 13 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index d4f6e477a..2461fdae0 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -3,7 +3,7 @@ author: "[Dan Dale](https://github.com/speediedan)" created: 2021-11-29 updated: 2022-03-29 license: CC BY-SA -build: 1 +build: 2 tags: - finetuning description: | diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index cf4e6f959..0148cbd67 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,15 +1,3 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: -all -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.8 -# --- - # %% [markdown] # ## Scheduled Finetuning # @@ -176,6 +164,7 @@ import datasets import pytorch_lightning as pl +import sentencepiece as sp # noqa:F401 import torch from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.loggers.tensorboard import TensorBoardLogger From 4e0715e8eea6a7918d46be55afddea4f8b36015b Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Tue, 19 Apr 2022 14:58:37 -0700 Subject: [PATCH 37/44] isort skip directive required to implement sentencepiece workaround --- .../finetuning-scheduler/finetuning-scheduler.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 0148cbd67..f137f106e 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -163,8 +163,9 @@ from typing import Any, Dict, List, Optional import datasets + +import sentencepiece as sp # noqa: F401 # isort: split import pytorch_lightning as pl -import sentencepiece as sp # noqa:F401 import torch from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.loggers.tensorboard import TensorBoardLogger From 31d3cb26e43bbdc4b36ba2df8d927994d555f31a Mon Sep 17 00:00:00 2001 From: Dan Dale Date: Thu, 21 Apr 2022 10:08:32 -0700 Subject: [PATCH 38/44] update broken gan/datamodules tutorial links (#164) * update both datamodules and basic-gan tutorials to reference stable doc version --- lightning_examples/basic-gan/gan.py | 2 +- lightning_examples/datamodules/.meta.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/lightning_examples/basic-gan/gan.py b/lightning_examples/basic-gan/gan.py index 251f68284..f0b984f30 100644 --- a/lightning_examples/basic-gan/gan.py +++ b/lightning_examples/basic-gan/gan.py @@ -20,7 +20,7 @@ # ### MNIST DataModule # # Below, we define a DataModule for the MNIST Dataset. To learn more about DataModules, check out our tutorial -# on them or see the [latest docs](https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html). +# on them or see the [latest docs](https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html). # %% diff --git a/lightning_examples/datamodules/.meta.yml b/lightning_examples/datamodules/.meta.yml index c53dcbcea..93a86ce8a 100644 --- a/lightning_examples/datamodules/.meta.yml +++ b/lightning_examples/datamodules/.meta.yml @@ -7,7 +7,7 @@ build: 3 description: This notebook will walk you through how to start using Datamodules. With the release of `pytorch-lightning` version 0.9.0, we have included a new class called `LightningDataModule` to help you decouple data related hooks from your `LightningModule`. - The most up to date documentation on datamodules can be found + The most up-to-date documentation on datamodules can be found [here](https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html). requirements: - torchvision From d4e36786fa1d2ceeb0cde2e69218ee218ea2dac3 Mon Sep 17 00:00:00 2001 From: Dan Dale Date: Tue, 10 May 2022 13:10:28 -0700 Subject: [PATCH 39/44] Apply suggestions from code review a few excellent suggestions/cleanup from review Co-authored-by: Rohit Gupta --- .../finetuning-scheduler.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index f137f106e..d2f10623a 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -52,7 +52,7 @@ # ## The Default Finetuning Schedule # # Schedule definition is facilitated via the [gen_ft_schedule](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.SchedulingMixin.gen_ft_schedule) method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as -# desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will likely be less computationally efficient than a user-defined finetuning schedule but is useful for exploring a model's finetuning behavior and can serve as a good baseline for subsquent explicit schedule refinement. +# desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will likely be less computationally efficient than a user-defined finetuning schedule but is useful for exploring a model's finetuning behavior and can serve as a good baseline for subsequent explicit schedule refinement. # While the current version of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning rates can be set as demonstrated in the next section. # %% [markdown] @@ -121,7 +121,7 @@ # trainer.fit(..., ckpt_path="some/path/to/my_checkpoint.ckpt") # ``` # -# Training will resume at the depth/level of the provided checkpoint according the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions. +# Training will resume at the depth/level of the provided checkpoint according to the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions. # # By default ([FinetuningScheduler.restore_best](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html?highlight=restore_best#finetuning_scheduler.fts.FinetuningScheduler.params.restore_best) is ``True``), [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) will attempt to restore the best available checkpoint before finetuning depth transitions. # @@ -205,7 +205,6 @@ def mock_register_module(key: str, require_fqn: bool = False) -> List: # Load the `FinetuningScheduler` PyTorch Lightning extension module we want to use. This will import all necessary callbacks. mock_register_module("finetuningscheduler") # set notebook-level variables -AVAIL_GPUS = torch.cuda.device_count() TASK_NUM_LABELS = {"boolq": 2, "rte": 2} DEFAULT_TASK = "rte" @@ -241,7 +240,7 @@ def __init__( tokenizers_parallelism: bool = True, **dataloader_kwargs: Any, ): - """Initialize this ``LightningDataModule`` designed for both the RTE or BoolQ SuperGLUE Hugging Face + """Initialize the ``LightningDataModule`` designed for both the RTE or BoolQ SuperGLUE Hugging Face datasets. Args: @@ -258,6 +257,7 @@ def __init__( train_batch_size (int, optional): Training batch size. Defaults to 16. eval_batch_size (int, optional): Batch size to use for validation and testing splits. Defaults to 16. tokenizers_parallelism (bool, optional): Whether to use parallelism in the tokenizer. Defaults to True. + \**dataloader_kwargs: Arguments passed when initializing the dataloader """ super().__init__() self.model_name_or_path = model_name_or_path @@ -355,7 +355,7 @@ def __init__( self.task_name = task_name else: self.task_name = DEFAULT_TASK - rank_zero_warn(f"Invalid task_name '{task_name}'. Proceeding with the default task: '{DEFAULT_TASK}'") + rank_zero_warn(f"Invalid task_name {task_name!r}. Proceeding with the default task: {DEFAULT_TASK!r}") self.num_labels = TASK_NUM_LABELS[self.task_name] self.experiment_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{experiment_tag}" self.model_cfg = model_cfg or {} @@ -381,7 +381,7 @@ def forward(self, **inputs): def training_step(self, batch, batch_idx): outputs = self(**batch) loss = outputs[0] - self.log("train_loss", loss, prog_bar=True) + self.log("train_loss", loss) return loss def training_epoch_end(self, outputs: List[Any]) -> None: @@ -433,7 +433,7 @@ def configure_optimizers(self): # not applied to the bias parameter (for completeness, in this case it won't make much # performance difference) optimizer = AdamW(params=self._init_param_groups(), **self.optimizer_init) - scheduler = {"scheduler": CosineAnnealingWarmRestarts(optimizer, **self.lr_scheduler_init)} + scheduler = {"scheduler": CosineAnnealingWarmRestarts(optimizer, **self.lr_scheduler_init), "interval": "step"} return [optimizer], [scheduler] def configure_callbacks(self): @@ -577,9 +577,8 @@ def train() -> None: enable_progress_bar=enable_progress_bar, max_epochs=100, precision=16, - strategy="dp", - accelerator="gpu", - devices=1, + accelerator="auto", + devices=1 if torch.cuda.is_available() else None callbacks=callbacks, logger=logger, ) From be5810f62280df8ba1b1e840092d571164f487e7 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 10 May 2022 20:10:46 +0000 Subject: [PATCH 40/44] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- lightning_examples/finetuning-scheduler/finetuning-scheduler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index d2f10623a..c2c6019c7 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -240,7 +240,7 @@ def __init__( tokenizers_parallelism: bool = True, **dataloader_kwargs: Any, ): - """Initialize the ``LightningDataModule`` designed for both the RTE or BoolQ SuperGLUE Hugging Face + r"""Initialize the ``LightningDataModule`` designed for both the RTE or BoolQ SuperGLUE Hugging Face datasets. Args: From d1505880f4c4d8b315cfdb9bef233dda24b5bae2 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Tue, 10 May 2022 15:05:36 -0700 Subject: [PATCH 41/44] additional clarification of documentation and minor cleanup pre-publish --- .../finetuning-scheduler.py | 111 +++++++++--------- .../finetuning-scheduler/logo_fts.png | Bin 0 -> 7798 bytes 2 files changed, 57 insertions(+), 54 deletions(-) create mode 100644 lightning_examples/finetuning-scheduler/logo_fts.png diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index c2c6019c7..3e829b451 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,29 +1,37 @@ # %% [markdown] -# ## Scheduled Finetuning +# ## Scheduled Finetuning with the Finetuning Scheduler Extension # -# The [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback accelerates and enhances model experimentation with flexible finetuning schedules. +# ![Finetuning Scheduler logo](logo_fts.png) # -# Training with the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) is simple and confers a host of benefits: +# The [Finetuning Scheduler](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) extension accelerates and enhances model experimentation with flexible finetuning schedules. +# +# Training with the extension is simple and confers a host of benefits: # # - it dramatically increases finetuning flexibility # - expedites and facilitates exploration of model tuning dynamics # - enables marginal performance improvements of finetuned models # +# Setup is straightforward, just install from PyPI! Since this notebook-based example requires a few additional packages (e.g. +# ``transformers``, ``sentencepiece``), we installed the ``finetuning-scheduler`` package with the ``[examples]`` extra above. +# Once the ``finetuning-scheduler`` package is installed, the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback is available for use with PyTorch Lightning. +# For additional installation options, please see the Finetuning Scheduler [README](https://github.com/speediedan/finetuning-scheduler/blob/main/README.md). +# +# +# #
# -# Fundamentally, the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback enables scheduled, multi-phase, -# finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize +# Fundamentally, [Finetuning Scheduler](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) enables +# scheduled, multi-phase, finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize # foundational model knowledge retention while allowing (typically upper layers of) the model to # optimally adapt to new tasks during transfer learning [1, 2, 3](#f1) # -# #
# -# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) orchestrates the gradual unfreezing +# The [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) callback orchestrates the gradual unfreezing # of models via a finetuning schedule that is either implicitly generated (the default) or explicitly provided by the user # (more computationally efficient). Finetuning phase transitions are driven by # [FTSEarlyStopping](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.FTSEarlyStopping) -# criteria (a multi-phase extension of ``EarlyStopping``), user-specified epoch transitions or a composition of the two (the default mode). +# criteria (a multi-phase extension of ``EarlyStopping`` packaged with FinetuningScheduler), user-specified epoch transitions or a composition of the two (the default mode). # A [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) training session completes when the # final phase of the schedule has its stopping criteria met. See # the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. @@ -260,30 +268,27 @@ def __init__( \**dataloader_kwargs: Arguments passed when initializing the dataloader """ super().__init__() - self.model_name_or_path = model_name_or_path - self.task_name = task_name if task_name in TASK_NUM_LABELS.keys() else DEFAULT_TASK - self.max_seq_length = max_seq_length - self.train_batch_size = train_batch_size - self.eval_batch_size = eval_batch_size - self.tokenizers_parallelism = tokenizers_parallelism + task_name = task_name if task_name in TASK_NUM_LABELS.keys() else DEFAULT_TASK + self.text_fields = self.TASK_TEXT_FIELD_MAP[task_name] self.dataloader_kwargs = { "num_workers": dataloader_kwargs.get("num_workers", 0), "pin_memory": dataloader_kwargs.get("pin_memory", False), } - self.text_fields = self.TASK_TEXT_FIELD_MAP[self.task_name] - self.num_labels = TASK_NUM_LABELS[self.task_name] - os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.tokenizers_parallelism else "false" - self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True, local_files_only=False) + self.save_hyperparameters() + os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.hparams.tokenizers_parallelism else "false" + self.tokenizer = AutoTokenizer.from_pretrained( + self.hparams.model_name_or_path, use_fast=True, local_files_only=False + ) def prepare_data(self): """Load the SuperGLUE dataset.""" # N.B. PL calls prepare_data from a single process (rank 0) so do not use it to assign # state (e.g. self.x=y) - datasets.load_dataset("super_glue", self.task_name) + datasets.load_dataset("super_glue", self.hparams.task_name) def setup(self, stage): - """Setup our dataset splits for training/validation/testing.""" - self.dataset = datasets.load_dataset("super_glue", self.task_name) + """Setup our dataset splits for training/validation.""" + self.dataset = datasets.load_dataset("super_glue", self.hparams.task_name) for split in self.dataset.keys(): self.dataset[split] = self.dataset[split].map( self._convert_to_features, batched=True, remove_columns=["label"] @@ -294,15 +299,12 @@ def setup(self, stage): self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] def train_dataloader(self): - return DataLoader(self.dataset["train"], batch_size=self.train_batch_size, **self.dataloader_kwargs) + return DataLoader(self.dataset["train"], batch_size=self.hparams.train_batch_size, **self.dataloader_kwargs) def val_dataloader(self): - return DataLoader(self.dataset["validation"], batch_size=self.eval_batch_size, **self.dataloader_kwargs) - - def test_dataloader(self): - return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size, **self.dataloader_kwargs) + return DataLoader(self.dataset["validation"], batch_size=self.hparams.eval_batch_size, **self.dataloader_kwargs) - def _convert_to_features(self, example_batch) -> BatchEncoding: + def _convert_to_features(self, example_batch: datasets.arrow_dataset.Batch) -> BatchEncoding: """Convert raw text examples to a :class:`~transformers.tokenization_utils_base.BatchEncoding` container (derived from python dict) of features that includes helpful methods for translating between word/character space and token space. @@ -316,7 +318,7 @@ def _convert_to_features(self, example_batch) -> BatchEncoding: text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) # Tokenize the text/text pairs features = self.tokenizer.batch_encode_plus( - text_pairs, max_length=self.max_seq_length, padding="longest", truncation=True + text_pairs, max_length=self.hparams.max_seq_length, padding="longest", truncation=True ) # Rename label to labels to make it easier to pass to model forward features["labels"] = example_batch["label"] @@ -349,31 +351,32 @@ def __init__( "default". """ super().__init__() - self.optimizer_init = optimizer_init - self.lr_scheduler_init = lr_scheduler_init - if task_name in TASK_NUM_LABELS.keys(): - self.task_name = task_name - else: - self.task_name = DEFAULT_TASK + if task_name not in TASK_NUM_LABELS.keys(): rank_zero_warn(f"Invalid task_name {task_name!r}. Proceeding with the default task: {DEFAULT_TASK!r}") - self.num_labels = TASK_NUM_LABELS[self.task_name] - self.experiment_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{experiment_tag}" + task_name = DEFAULT_TASK + self.num_labels = TASK_NUM_LABELS[task_name] self.model_cfg = model_cfg or {} conf = AutoConfig.from_pretrained(model_name_or_path, num_labels=self.num_labels, local_files_only=False) self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=conf) self.model.config.update(self.model_cfg) # apply model config overrides self.init_hparams = { - "optimizer": self.optimizer_init, - "lr_scheduler": self.lr_scheduler_init, + "optimizer_init": optimizer_init, + "lr_scheduler_init": lr_scheduler_init, "model_config": self.model.config, "model_name_or_path": model_name_or_path, - "task_name": self.task_name, - "experiment_id": self.experiment_id, + "task_name": task_name, + "experiment_id": f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{experiment_tag}", } self.save_hyperparameters(self.init_hparams) - self.metric = datasets.load_metric("super_glue", self.task_name, experiment_id=self.experiment_id) + self.metric = datasets.load_metric( + "super_glue", self.hparams.task_name, experiment_id=self.hparams.experiment_id + ) self.no_decay = ["bias", "LayerNorm.weight"] - self.finetuningscheduler_callback = None + + @property + def finetuningscheduler_callback(self) -> FinetuningScheduler: # type: ignore # noqa + fts = [c for c in self.trainer.callbacks if isinstance(c, FinetuningScheduler)] # type: ignore # noqa + return fts[0] if fts else None def forward(self, **inputs): return self.model(**inputs) @@ -414,7 +417,7 @@ def _init_param_groups(self) -> List[Dict]: for n, p in self.model.named_parameters() if not any(nd in n for nd in self.no_decay) and p.requires_grad ], - "weight_decay": self.optimizer_init["weight_decay"], + "weight_decay": self.hparams.optimizer_init["weight_decay"], }, { "params": [ @@ -432,16 +435,13 @@ def configure_optimizers(self): # but in this case we pass a list of parameter groups to ensure weight_decay is # not applied to the bias parameter (for completeness, in this case it won't make much # performance difference) - optimizer = AdamW(params=self._init_param_groups(), **self.optimizer_init) - scheduler = {"scheduler": CosineAnnealingWarmRestarts(optimizer, **self.lr_scheduler_init), "interval": "step"} + optimizer = AdamW(params=self._init_param_groups(), **self.hparams.optimizer_init) + scheduler = { + "scheduler": CosineAnnealingWarmRestarts(optimizer, **self.hparams.lr_scheduler_init), + "interval": "step", + } return [optimizer], [scheduler] - def configure_callbacks(self): - found_fts = [c for c in self.trainer.callbacks if isinstance(c, FinetuningScheduler)] # type: ignore # noqa - if found_fts: - self.finetuningscheduler_callback = found_fts[0] - return super().configure_callbacks() - # %% [markdown] # ### Our Training Sessions @@ -578,14 +578,14 @@ def train() -> None: max_epochs=100, precision=16, accelerator="auto", - devices=1 if torch.cuda.is_available() else None + devices=1 if torch.cuda.is_available() else None, callbacks=callbacks, logger=logger, ) trainer.fit(model, datamodule=dm) -if AVAIL_GPUS > 0: +if torch.cuda.is_available(): train() else: print("Given the multiple phases of finetuning demonstrated, this notebook is best used with a GPU") @@ -620,7 +620,7 @@ def train() -> None: scenario_callbacks = {"nofts_baseline": nofts_callbacks, "fts_implicit": fts_implicit_callbacks} # %% -if AVAIL_GPUS > 0: +if torch.cuda.is_available(): for scenario_name, scenario_callbacks in scenario_callbacks.items(): model = RteBoolqModule(**lightning_module_kwargs, experiment_tag=scenario_name) logger = TensorBoardLogger("lightning_logs", name=scenario_name) @@ -709,3 +709,6 @@ def train() -> None: # # # + +# %% [markdown] +# diff --git 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z@N(Dd3#taZc3%W~OZ4+oRniUsWmbaCHK3-Ox*N}gQB?85%>#rIzJjhbmM0uF~+yFhk=>0etSHqG1yVvp025ju`(tANu%&jfXZS@mv( z4CwEJx){92plSfL?2Xt_nvr+QsHeRLqR&0!eOV20B(nP38nG`81GPl#AbOGU()=SG z{k9`XYa8@4zg>Mu$Hrs+aWdD>0KAAS(lg!zP7qH%H4)isCU>8fgL)R!Q}L1PDryW( zfy%9R+mc+7lV*SpM-M{phphO90gE7Y7Su&d5IX7)F_VMYz`myNL(oMSkULBL!H|8% zDr^M?7M_$i9OXr4_c(AkHWs&#PeFfrvH$_`xJy4wFn#`KjDskZx@W<&;}NPr-4t2f z;V5IA5OI_X{@}O>*WWmlZ%+vB>#D{SruyWt%<@i#c~N`mI2>h*6C#ds!SCUAL%erV z4qYqG170qZtg$U@EG}OJDO(3!2`fs8I(ImFJkBQKC=+xF)TPG2KM=_ibU)(BwhS={ z)*@Tejz?0TY>A|CzaEBMSKKzNUE1a`pk4*f-_Nqpe;8aGk^Ii#C_kJKag-Tz`@z~H zfCG?}Dnk+f_*%rD zh&UYGO8t3|y*}p4^}r>_?t%_SS&|fq>2NqYg&t~x$0D})1yg<$OlX1y#iS^AIC>n; zu{a*|G1xaB(cat{H;phba4_y$4r0=FAHUIzs07*qo IM6N<$f@H Date: Tue, 10 May 2022 15:45:43 -0700 Subject: [PATCH 42/44] metadata and aesthetic image improvements --- .../finetuning-scheduler/.meta.yml | 8 +++---- .../finetuning-scheduler.py | 24 +++++++++++-------- 2 files changed, 18 insertions(+), 14 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 2461fdae0..3cae7999e 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -1,14 +1,14 @@ title: Finetuning Scheduler author: "[Dan Dale](https://github.com/speediedan)" created: 2021-11-29 -updated: 2022-03-29 +updated: 2022-05-10 license: CC BY-SA -build: 2 +build: 3 tags: - finetuning description: | - This notebook introduces the [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) - callback and demonstrates the use of it to finetune a small foundational model on the + This notebook introduces the [Finetuning Scheduler](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) extension + and demonstrates the use of it to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/) with iterative early-stopping defined according to a user-specified schedule. It uses Hugging Face's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 3e829b451..0c0fb5b81 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,7 +1,7 @@ # %% [markdown] # ## Scheduled Finetuning with the Finetuning Scheduler Extension # -# ![Finetuning Scheduler logo](logo_fts.png) +# ![Finetuning Scheduler logo](logo_fts.png){height="58px" width="401px"} # # The [Finetuning Scheduler](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) extension accelerates and enhances model experimentation with flexible finetuning schedules. # @@ -36,7 +36,7 @@ # final phase of the schedule has its stopping criteria met. See # the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. # -# ![FinetuningScheduler explicit loss animation](fts_explicit_loss_anim.gif) +# ![FinetuningScheduler explicit loss animation](fts_explicit_loss_anim.gif){height="272px" width="376px"} # %% [markdown] # @@ -79,7 +79,7 @@ # # 2. Alter the schedule as desired. # -# ![side_by_side_yaml](side_by_side_yaml.png) +# ![side_by_side_yaml](side_by_side_yaml.png){height="327px" width="800px"} # # 3. Once the finetuning schedule has been altered as desired, pass it to # [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) to commence scheduled training: @@ -105,7 +105,7 @@ # # **Tip:** Use of regex expressions can be convenient for specifying more complex schedules. Also, a per-phase base maximum lr can be specified: # -# ![emphasized_yaml](emphasized_yaml.png) +# ![emphasized_yaml](emphasized_yaml.png){height="380px" width="800px"} # #
# @@ -145,7 +145,7 @@ # %% [markdown] #
# -# **Note:** Currently, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) supports the following ``StrategyType``s: +# **Note:** Currently, [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) supports the following strategy types: # # - ``DP`` # - ``DDP`` @@ -588,7 +588,9 @@ def train() -> None: if torch.cuda.is_available(): train() else: - print("Given the multiple phases of finetuning demonstrated, this notebook is best used with a GPU") + print( + "Given the computation associated w/ the multiple phases of finetuning demonstrated, this notebook is best used with a GPU" + ) # %% [markdown] # ### Running the Baseline and Implicit Finetuning Scenarios @@ -628,7 +630,9 @@ def train() -> None: print(f"Beginning training the '{scenario_name}' scenario") train() else: - print("Given the multiple phases of finetuning demonstrated, this notebook is best used with a GPU") + print( + "Given the computation associated w/ the multiple phases of finetuning demonstrated, this notebook is best used with a GPU" + ) # %% [markdown] # ### Reviewing the Training Results @@ -641,8 +645,8 @@ def train() -> None: # produced in the scenarios [here](https://drive.google.com/file/d/1t7myBgcqcZ9ax_IT9QVk-vFH_l_o5UXB/view?usp=sharing) # (caution, ~3.5GB). # -# [![fts_explicit_accuracy](fts_explicit_accuracy.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOnRydWUsIm5vZnRzX2Jhc2VsaW5lIjpmYWxzZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D) -# [![nofts_baseline](nofts_baseline_accuracy.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6dHJ1ZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D) +# [![fts_explicit_accuracy](fts_explicit_accuracy.png){height="315px" width="492px"}](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOnRydWUsIm5vZnRzX2Jhc2VsaW5lIjpmYWxzZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D) +# [![nofts_baseline](nofts_baseline_accuracy.png){height="316px" width="505px"}](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6dHJ1ZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D) # # Note there could be around ~1% variation in performance from the tensorboard summaries generated by this notebook # which uses DP and 1 GPU. @@ -652,7 +656,7 @@ def train() -> None: # greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTa-v3's implicit training # run, a critical tuning transition point is immediately apparent: # -# [![implicit_training_transition](implicit_training_transition.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6ZmFsc2UsImZ0c19pbXBsaWNpdCI6dHJ1ZX0%3D) +# [![implicit_training_transition](implicit_training_transition.png){height="272px" width="494px"}](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6ZmFsc2UsImZ0c19pbXBsaWNpdCI6dHJ1ZX0%3D) # # Our `val_loss` begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our # schedule, in phase 17 we're beginning tuning the attention parameters of our 10th encoder layer (of 11). Interesting! From 3cecc757d7dc40b7a364a21fa73aaaf204fff9a2 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Wed, 11 May 2022 09:53:43 -0700 Subject: [PATCH 43/44] make notebook accelerator agnostic --- .../finetuning-scheduler.py | 27 +++++++------------ 1 file changed, 10 insertions(+), 17 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 0c0fb5b81..9d6316332 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -585,12 +585,10 @@ def train() -> None: trainer.fit(model, datamodule=dm) -if torch.cuda.is_available(): - train() -else: - print( - "Given the computation associated w/ the multiple phases of finetuning demonstrated, this notebook is best used with a GPU" - ) +print( + "Note given the computation associated w/ the multiple phases of finetuning demonstrated, this notebook is best used with an accelerator" +) +train() # %% [markdown] # ### Running the Baseline and Implicit Finetuning Scenarios @@ -622,17 +620,12 @@ def train() -> None: scenario_callbacks = {"nofts_baseline": nofts_callbacks, "fts_implicit": fts_implicit_callbacks} # %% -if torch.cuda.is_available(): - for scenario_name, scenario_callbacks in scenario_callbacks.items(): - model = RteBoolqModule(**lightning_module_kwargs, experiment_tag=scenario_name) - logger = TensorBoardLogger("lightning_logs", name=scenario_name) - callbacks = scenario_callbacks - print(f"Beginning training the '{scenario_name}' scenario") - train() -else: - print( - "Given the computation associated w/ the multiple phases of finetuning demonstrated, this notebook is best used with a GPU" - ) +for scenario_name, scenario_callbacks in scenario_callbacks.items(): + model = RteBoolqModule(**lightning_module_kwargs, experiment_tag=scenario_name) + logger = TensorBoardLogger("lightning_logs", name=scenario_name) + callbacks = scenario_callbacks + print(f"Beginning training the '{scenario_name}' scenario") + train() # %% [markdown] # ### Reviewing the Training Results From e73f44361cfd5c0c326ad0d2cb659178597dd98e Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Wed, 11 May 2022 11:28:52 -0700 Subject: [PATCH 44/44] reset lr_scheduler interval to epoch --- lightning_examples/finetuning-scheduler/finetuning-scheduler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 9d6316332..e276fe677 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -438,7 +438,7 @@ def configure_optimizers(self): optimizer = AdamW(params=self._init_param_groups(), **self.hparams.optimizer_init) scheduler = { "scheduler": CosineAnnealingWarmRestarts(optimizer, **self.hparams.lr_scheduler_init), - "interval": "step", + "interval": "epoch", } return [optimizer], [scheduler]

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# # **Note:** Currently, _FinetuningScheduler_ only supports the following ``StrategyType``s: +# # - ``DP`` # - ``DDP`` # - ``DDP_SPAWN`` @@ -600,6 +601,8 @@ def configure_callbacks(self): enable_progress_bar = False # %% + + def train() -> None: trainer = pl.Trainer( enable_progress_bar=enable_progress_bar, @@ -622,13 +625,15 @@ def train() -> None: # ### Running the Baseline and Implicit Finetuning Scenarios # # Let's now compare our ``nofts_baseline`` and ``fts_implicit`` scenarios with the ``fts_explicit`` one we just ran. +# # We'll need to update our callbacks list, using the core PL ``EarlyStopping`` and ``ModelCheckpoint`` callbacks for the # ``nofts_baseline`` (which operate identically to their FTS analogs apart from the recursive training support). # For both core PyTorch Lightning and user-registered callbacks, we can define our callbacks using a dictionary as we do # with the LightningCLI. This allows us to avoid managing imports and support more complex configuration separated from # code. -# We'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping ``max_depth`` for the -# implicit schedule will limit finetuning to just the last 4 parameters of the model, which is only a small fraction +# +# Note that we'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping ``max_depth`` for +# the implicit schedule will limit finetuning to just the last 4 parameters of the model, which is only a small fraction # of the parameters you'd want to tune for maximum performance. Since the implicit schedule is quite computationally # intensive and most useful for exploring model behavior, leaving ``max_depth`` 1 allows us to demo implicit mode # behavior while keeping the computational cost and runtime of this notebook reasonable. To review how a full implicit @@ -686,8 +691,8 @@ def train() -> None: # which uses DDP_SPAWN and 1 GPU. # # ``FinetuningScheduler`` expands the space of possible finetuning schedules and the composition of more sophisticated schedules can -# yield mariginal finetuning performance gains. That stated, it should be emphasized the primary utility of ``FinetuningScheduler`` is to grant -# greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTav3's implicit training +# yield marginal finetuning performance gains. That stated, it should be emphasized the primary utility of ``FinetuningScheduler`` is to grant +# greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTa-v3's implicit training # run, a critical tuning transition point is immediately apparent: # # [![implicit_training_transition](implicit_training_transition.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6ZmFsc2UsImZ0c19pbXBsaWNpdCI6dHJ1ZX0%3D) From c99e169566535a64144fa830b778206c2cac55e6 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Thu, 18 Nov 2021 11:11:42 -0800 Subject: [PATCH 11/44] several recommended fixes for lightning_examples/finetuning-scheduler/finetuning-scheduler.py Co-authored-by: thomas chaton --- .../finetuning-scheduler/.meta.yml | 20 ++ .../finetuning-scheduler.py | 326 ++++++++++++++++++ .../text-transformers/text-transformers.py | 2 +- 3 files changed, 347 insertions(+), 1 deletion(-) create mode 100644 lightning_examples/finetuning-scheduler/.meta.yml create mode 100644 lightning_examples/finetuning-scheduler/finetuning-scheduler.py diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml new file mode 100644 index 000000000..6a2a38ce8 --- /dev/null +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -0,0 +1,20 @@ +title: Finetuning Scheduler +author: PL team +created: 2021-01-31 +updated: 2021-06-21 +license: CC BY-SA +build: 1 +tags: + - Text +description: | + This notebook will use HuggingFace's `datasets` library to get data, which will be wrapped in a `LightningDataModule`. + Then, we write a class to perform text classification on any dataset from the [GLUE Benchmark](https://gluebenchmark.com/). + (We just show CoLA and MRPC due to constraint on compute/disk) +requirements: + - transformers + - datasets + - scipy + - scikit-learn +accelerator: + - CPU + - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py new file mode 100644 index 000000000..b97a1012e --- /dev/null +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -0,0 +1,326 @@ +# %% +from datetime import datetime +from typing import Optional + +import datasets +import torch +from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything +from torch.utils.data import DataLoader +from transformers import ( + AdamW, + AutoConfig, + AutoModelForSequenceClassification, + AutoTokenizer, + get_linear_schedule_with_warmup, +) + +AVAIL_GPUS = min(1, torch.cuda.device_count()) + +# %% [markdown] +# ## Training BERT with Lightning + +# %% [markdown] +# ### Lightning DataModule for GLUE + + +# %% +class GLUEDataModule(LightningDataModule): + + task_text_field_map = { + "cola": ["sentence"], + "sst2": ["sentence"], + "mrpc": ["sentence1", "sentence2"], + "qqp": ["question1", "question2"], + "stsb": ["sentence1", "sentence2"], + "mnli": ["premise", "hypothesis"], + "qnli": ["question", "sentence"], + "rte": ["sentence1", "sentence2"], + "wnli": ["sentence1", "sentence2"], + "ax": ["premise", "hypothesis"], + } + + glue_task_num_labels = { + "cola": 2, + "sst2": 2, + "mrpc": 2, + "qqp": 2, + "stsb": 1, + "mnli": 3, + "qnli": 2, + "rte": 2, + "wnli": 2, + "ax": 3, + } + + loader_columns = [ + "datasets_idx", + "input_ids", + "token_type_ids", + "attention_mask", + "start_positions", + "end_positions", + "labels", + ] + + def __init__( + self, + model_name_or_path: str, + task_name: str = "mrpc", + max_seq_length: int = 128, + train_batch_size: int = 32, + eval_batch_size: int = 32, + **kwargs, + ): + super().__init__() + self.model_name_or_path = model_name_or_path + self.task_name = task_name + self.max_seq_length = max_seq_length + self.train_batch_size = train_batch_size + self.eval_batch_size = eval_batch_size + + self.text_fields = self.task_text_field_map[task_name] + self.num_labels = self.glue_task_num_labels[task_name] + self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + + def setup(self, stage: str): + self.dataset = datasets.load_dataset("glue", self.task_name) + + for split in self.dataset.keys(): + self.dataset[split] = self.dataset[split].map( + self.convert_to_features, + batched=True, + remove_columns=["label"], + ) + self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] + self.dataset[split].set_format(type="torch", columns=self.columns) + + self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] + + def prepare_data(self): + datasets.load_dataset("glue", self.task_name) + AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + + def train_dataloader(self): + return DataLoader(self.dataset["train"], batch_size=self.train_batch_size) + + def val_dataloader(self): + if len(self.eval_splits) == 1: + return DataLoader(self.dataset["validation"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits] + + def test_dataloader(self): + if len(self.eval_splits) == 1: + return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size) + elif len(self.eval_splits) > 1: + return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits] + + def convert_to_features(self, example_batch, indices=None): + + # Either encode single sentence or sentence pairs + if len(self.text_fields) > 1: + texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) + else: + texts_or_text_pairs = example_batch[self.text_fields[0]] + + # Tokenize the text/text pairs + features = self.tokenizer.batch_encode_plus( + texts_or_text_pairs, max_length=self.max_seq_length, pad_to_max_length=True, truncation=True + ) + + # Rename label to labels to make it easier to pass to model forward + features["labels"] = example_batch["label"] + + return features + + +# %% [markdown] +# **You could use this datamodule with standalone PyTorch if you wanted...** + +# %% +dm = GLUEDataModule("distilbert-base-uncased") +dm.prepare_data() +dm.setup("fit") +next(iter(dm.train_dataloader())) + +# %% [markdown] +# ### Transformer LightningModule + + +# %% +class GLUETransformer(LightningModule): + def __init__( + self, + model_name_or_path: str, + num_labels: int, + task_name: str, + learning_rate: float = 2e-5, + adam_epsilon: float = 1e-8, + warmup_steps: int = 0, + weight_decay: float = 0.0, + train_batch_size: int = 32, + eval_batch_size: int = 32, + eval_splits: Optional[list] = None, + **kwargs, + ): + super().__init__() + + self.save_hyperparameters() + + self.config = AutoConfig.from_pretrained(model_name_or_path, num_labels=num_labels) + self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=self.config) + self.metric = datasets.load_metric( + "glue", self.hparams.task_name, experiment_id=datetime.now().strftime("%d-%m-%Y_%H-%M-%S") + ) + + def forward(self, **inputs): + return self.model(**inputs) + + def training_step(self, batch, batch_idx): + outputs = self(**batch) + loss = outputs[0] + return loss + + def validation_step(self, batch, batch_idx, dataloader_idx=0): + outputs = self(**batch) + val_loss, logits = outputs[:2] + + if self.hparams.num_labels >= 1: + preds = torch.argmax(logits, axis=1) + elif self.hparams.num_labels == 1: + preds = logits.squeeze() + + labels = batch["labels"] + + return {"loss": val_loss, "preds": preds, "labels": labels} + + def validation_epoch_end(self, outputs): + if self.hparams.task_name == "mnli": + for i, output in enumerate(outputs): + # matched or mismatched + split = self.hparams.eval_splits[i].split("_")[-1] + preds = torch.cat([x["preds"] for x in output]).detach().cpu().numpy() + labels = torch.cat([x["labels"] for x in output]).detach().cpu().numpy() + loss = torch.stack([x["loss"] for x in output]).mean() + self.log(f"val_loss_{split}", loss, prog_bar=True) + split_metrics = { + f"{k}_{split}": v for k, v in self.metric.compute(predictions=preds, references=labels).items() + } + self.log_dict(split_metrics, prog_bar=True) + return loss + + preds = torch.cat([x["preds"] for x in outputs]).detach().cpu().numpy() + labels = torch.cat([x["labels"] for x in outputs]).detach().cpu().numpy() + loss = torch.stack([x["loss"] for x in outputs]).mean() + self.log("val_loss", loss, prog_bar=True) + self.log_dict(self.metric.compute(predictions=preds, references=labels), prog_bar=True) + return loss + + def setup(self, stage=None) -> None: + if stage != "fit": + return + # Get dataloader by calling it - train_dataloader() is called after setup() by default + # train_loader = self.train_dataloader() + train_loader = self.trainer.datamodule.train_dataloader() + # Calculate total steps + tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus) + ab_size = self.trainer.accumulate_grad_batches * float(self.trainer.max_epochs) + self.total_steps = (len(train_loader.dataset) // tb_size) // ab_size + + def configure_optimizers(self): + """Prepare optimizer and schedule (linear warmup and decay)""" + model = self.model + no_decay = ["bias", "LayerNorm.weight"] + optimizer_grouped_parameters = [ + { + "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], + "weight_decay": self.hparams.weight_decay, + }, + { + "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], + "weight_decay": 0.0, + }, + ] + optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon) + + scheduler = get_linear_schedule_with_warmup( + optimizer, + num_warmup_steps=self.hparams.warmup_steps, + num_training_steps=self.total_steps, + ) + scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1} + return [optimizer], [scheduler] + + +# %% [markdown] +# ## Training + +# %% [markdown] +# ### CoLA +# +# See an interactive view of the +# CoLA dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=cola) + +# %% +seed_everything(42) + +dm = GLUEDataModule(model_name_or_path="albert-base-v2", task_name="cola") +dm.setup("fit") +model = GLUETransformer( + model_name_or_path="albert-base-v2", + num_labels=dm.num_labels, + eval_splits=dm.eval_splits, + task_name=dm.task_name, +) + +trainer = Trainer(max_epochs=1, gpus=AVAIL_GPUS) +trainer.fit(model, dm) + +# %% [markdown] +# ### MRPC +# +# See an interactive view of the +# MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mrpc) + +# %% +seed_everything(42) + +dm = GLUEDataModule( + model_name_or_path="distilbert-base-cased", + task_name="mrpc", +) +dm.setup("fit") +model = GLUETransformer( + model_name_or_path="distilbert-base-cased", + num_labels=dm.num_labels, + eval_splits=dm.eval_splits, + task_name=dm.task_name, +) + +trainer = Trainer(max_epochs=3, gpus=AVAIL_GPUS) +trainer.fit(model, dm) + +# %% [markdown] +# ### MNLI +# +# - The MNLI dataset is huge, so we aren't going to bother trying to train on it here. +# - We will skip over training and go straight to validation. +# +# See an interactive view of the +# MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mnli) + +# %% +dm = GLUEDataModule( + model_name_or_path="distilbert-base-cased", + task_name="mnli", +) +dm.setup("fit") +model = GLUETransformer( + model_name_or_path="distilbert-base-cased", + num_labels=dm.num_labels, + eval_splits=dm.eval_splits, + task_name=dm.task_name, +) + +trainer = Trainer(gpus=AVAIL_GPUS, progress_bar_refresh_rate=20) +trainer.validate(model, dm.val_dataloader()) diff --git a/lightning_examples/text-transformers/text-transformers.py b/lightning_examples/text-transformers/text-transformers.py index 57786f010..d14a2e397 100644 --- a/lightning_examples/text-transformers/text-transformers.py +++ b/lightning_examples/text-transformers/text-transformers.py @@ -220,7 +220,7 @@ def setup(self, stage=None) -> None: if stage != "fit": return # Get dataloader by calling it - train_dataloader() is called after setup() by default - train_loader = self.trainer.datamodule.train_dataloader() + train_loader = self.train_dataloader() # Calculate total steps tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus) From b89c5d03d18b4ab8e93c76514ddaef5104cc3983 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Mon, 29 Nov 2021 17:22:25 -0800 Subject: [PATCH 12/44] notebook-based fts tutorial and example --- .../finetuning-scheduler/.meta.yml | 19 +- .../finetuning-scheduler/emphasized_yaml.svg | 1176 +++++++++++++++++ .../finetuning-scheduler.py | 704 +++++++--- .../fts_explicit_loss_anim.gif | Bin 0 -> 302052 bytes .../side_by_side_yaml.svg | 754 +++++++++++ 5 files changed, 2437 insertions(+), 216 deletions(-) create mode 100644 lightning_examples/finetuning-scheduler/emphasized_yaml.svg create mode 100644 lightning_examples/finetuning-scheduler/fts_explicit_loss_anim.gif create mode 100644 lightning_examples/finetuning-scheduler/side_by_side_yaml.svg diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 6a2a38ce8..33296fba5 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -1,20 +1,19 @@ title: Finetuning Scheduler -author: PL team -created: 2021-01-31 -updated: 2021-06-21 +author: Dan Dale +created: 2021-11-26 +updated: 2021-11-26 license: CC BY-SA build: 1 tags: - - Text + - finetuning description: | - This notebook will use HuggingFace's `datasets` library to get data, which will be wrapped in a `LightningDataModule`. - Then, we write a class to perform text classification on any dataset from the [GLUE Benchmark](https://gluebenchmark.com/). - (We just show CoLA and MRPC due to constraint on compute/disk) + This notebook introduces the FinetuningScheduler callback and demonstrates the use of FinetuningScheduler to finetune + a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task + of [SuperGLUE](https://super.gluebenchmark.com/) with iterative earlystopping defined according to a user-specified + schedule. It uses HuggingFace's `datasets` and `transformers`` libraries to retrieve the relevant benchmark data and + foundational model weights. requirements: - transformers - datasets - - scipy - - scikit-learn accelerator: - - CPU - GPU diff --git a/lightning_examples/finetuning-scheduler/emphasized_yaml.svg b/lightning_examples/finetuning-scheduler/emphasized_yaml.svg new file mode 100644 index 000000000..eda7e7891 --- /dev/null +++ b/lightning_examples/finetuning-scheduler/emphasized_yaml.svg @@ -0,0 +1,1176 @@ + + + + + + + + image/svg+xml + + + + + + + + + diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index b97a1012e..9669cb632 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,57 +1,274 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.1 +# kernelspec: +# display_name: 'Python 3.7.11 64-bit (''pldev_tutorials'': conda)' +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Scheduled Finetuning +# +#
+# +# The ``FinetuningScheduler`` callback enables multi-phase, +# scheduled finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize +# foundational model knowledge retention while allowing (typically upper layers of) the model to +# optimally adapt to new tasks during transfer learning [1, 2, 3](#f1) +# +#
+# +# ``FinetuningScheduler`` orchestrates the gradual unfreezing +# of models via a finetuning schedule that is either implicitly generated (the default) or explicitly provided by the user +# (more computationally efficient). Finetuning phase transitions are driven by +# ``FTSEarlyStopping`` criteria (a multi-phase +# extension of ``EarlyStopping``), user-specified epoch transitions or a composition of the two (the default mode). +# A ``FinetuningScheduler`` training session completes when the +# final phase of the schedule has its stopping criteria met. See +# the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/latest/extensions/generated/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. +# +# + +# %% [markdown] +# ## Basic Usage +# +# If no finetuning schedule is user-provided, ``FinetuningScheduler`` will generate a +# [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, using default ``FTSEarlyStopping`` and ``FTSCheckpoint`` callbacks with ``monitor=val_loss``. +# +# ```python +# from pytorch_lightning import Trainer +# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# trainer = Trainer(callbacks=[FinetuningScheduler()]) +# ``` + +# %% [markdown] +# ## The Default Finetuning Schedule +# +# Schedule definition is facilitated via the ``gen_ft_schedule`` method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as +# desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will often be less computationally efficient than a user-defined finetuning schedule but can often serve as a good baseline for subsquent explicit schedule refinement and will marginally outperform many explicit schedules. + +# %% [markdown] +# ## Specifying a Finetuning Schedule +# +# To specify a finetuning schedule, it's convenient to first generate the default schedule and then alter the thawed/unfrozen parameter groups associated with each finetuning phase as desired. Finetuning phases are zero-indexed and executed in ascending order. +# +# 1. Generate the default schedule to ``Trainer.log_dir`` with the name +# (``lightning_module.__class__.__name__``)_ft_schedule.yaml +# +# ```python +# from pytorch_lightning import Trainer +# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# trainer = Trainer(callbacks=[FinetuningScheduler(gen_ft_sched_only=True)]) +# ``` +# +# 2. Alter the schedule as desired. +# +# +# +# 3. Once the finetuning schedule has been altered as desired, pass it to +# ``FinetuningScheduler`` to commence scheduled training: +# +# ```python +# from pytorch_lightning import Trainer +# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# +# trainer = Trainer(callbacks=[FinetuningScheduler(ft_schedule="/path/to/my/schedule/my_schedule.yaml")]) +# ``` + +# %% [markdown] +# ## EarlyStopping and Epoch-Driven Phase Transition Criteria +# +# +# By default, ``FTSEarlyStopping`` and epoch-driven +# transition criteria are composed. If a ``max_transition_epoch`` is specified for a given phase, the next finetuning phase will begin at that epoch unless ``FTSEarlyStopping`` criteria are met first. +# If ``FinetuningScheduler.epoch_transitions_only`` is ``True``, ``FTSEarlyStopping`` will not be used +# and transitions will be exclusively epoch-driven. +# +# +#
+# +# **Tip:** Use of regex expressions can be convenient for specifying more complex schedules: +# +# +# +#
+# +# +# +# The end-to-end example in this notebook ([Scheduled Finetuning For SuperGLUE](#superglue)) uses ``FinetuningScheduler`` in explicit mode to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). + +# %% [markdown] +# ## Resuming Scheduled Finetuning Training Sessions +# +# Resumption of scheduled finetuning training is identical to the continuation of +# [other training sessions](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html) with the caveat that the provided checkpoint must have been saved by a ``FinetuningScheduler`` session. +# ``FinetuningScheduler`` uses ``FTSCheckpoint`` (an extension of ``ModelCheckpoint``) to maintain schedule state with special metadata. +# +# +# ```python +# from pytorch_lightning import Trainer +# from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler +# trainer = Trainer(callbacks=[FinetuningScheduler()], resume_from_checkpoint="some/path/to/my_checkpoint.ckpt") +# ``` +# +# Training will resume at the depth/level of the provided checkpoint according the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions. +# +# By default (``FinetuningScheduler.restore_best`` is ``True``), ``FinetuningScheduler`` will attempt to restore the best available checkpoint before finetuning depth transitions. +# +# ```python +# trainer = Trainer( +# callbacks=[FinetuningScheduler(new_incarnation_mode=True)], +# resume_from_checkpoint="some/path/to/my_kth_best_checkpoint.ckpt", +# ) +# ``` +# +# To handle the edge case wherein one is resuming scheduled finetuning from a non-best checkpoint and the previous best checkpoints may not be accessible, setting ``FinetuningScheduler.new_incarnation_mode`` to +# ``True`` as above will re-intialize the checkpoint state with a new best checkpoint at the resumption depth. + +# %% [markdown] +#
+# +# **Note:** Currently, _FinetuningScheduler_ only supports the following _TrainingTypePlugins_: +# +# - ``DDPPlugin`` +# - ``DDPShardedPlugin`` +# - ``DDPSpawnPlugin`` +# - ``DDPSpawnShardedPlugin`` +# - ``DataParallelPlugin`` +# - ``SingleDevicePlugin`` +# +#
+ +# %% [markdown] +#
+# +# # Scheduled Finetuning For SuperGLUE +# +# The following example demonstrates the use of ``FinetuningScheduler`` to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. +# +# ``FinetuningScheduler`` can be used to achieve non-trivial model performance improvements in both implicit and explicit scheduling contexts at an also non-trivial computational cost. + # %% +import os +import warnings from datetime import datetime -from typing import Optional +from typing import Any, Dict, List, Optional, Tuple, Union +from importlib import import_module +import logging -import datasets import torch -from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything from torch.utils.data import DataLoader -from transformers import ( - AdamW, - AutoConfig, - AutoModelForSequenceClassification, - AutoTokenizer, - get_linear_schedule_with_warmup, -) -AVAIL_GPUS = min(1, torch.cuda.device_count()) +import pytorch_lightning as pl +from pytorch_lightning.utilities import rank_zero_warn +from pytorch_lightning.utilities.cli import CALLBACK_REGISTRY, _Registry +from pytorch_lightning.utilities.exceptions import MisconfigurationException +from pytorch_lightning.loggers.tensorboard import TensorBoardLogger -# %% [markdown] -# ## Training BERT with Lightning +import datasets +from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer -# %% [markdown] -# ### Lightning DataModule for GLUE + +# %% +# a couple helper functions to prepare code to work with the forthcoming hub and user module registry +MOCK_HUB_REGISTRY = _Registry() + + +def module_hub_mock(key: str, require_fqn: bool = False) -> List: + if key.lower() == "finetuningscheduler": + mod = import_module("pytorch_lightning.callbacks.finetuning_scheduler") + MOCK_HUB_REGISTRY.register_classes(mod, pl.callbacks.Callback) + else: + raise MisconfigurationException(f"user module key '{key}' not found") + registered_list = [] + # make registered class available by unqualified class name by default + if not require_fqn: + for n, c in MOCK_HUB_REGISTRY.items(): + globals()[f"{n}"] = c + registered_list = ", ".join([n for n in MOCK_HUB_REGISTRY.names]) + else: + registered_list = ", ".join( + [c.__module__ + "." + c.__name__ for c in MOCK_HUB_REGISTRY.classes] + ) + print(f"Imported and registered the following callbacks: {registered_list}") + + +def instantiate_registered_class( + init: Dict[str, Any], args: Optional[Union[Any, Tuple[Any, ...]]] = None +) -> Any: + """Instantiates a class with the given args and init. Accepts class definitions in the form + of a "class_path" or "callback_key" associated with a _Registry + + Args: + init: Dict of the form {"class_path":... or "callback_key":..., "init_args":...}. + args: Positional arguments required for instantiation. + + Returns: + The instantiated class object. + """ + kwargs = init.get("init_args", {}) + if args and not isinstance(args, tuple): + args = (args,) + shortcircuit_local = False if "." in init["class_path"] else True + class_module, class_name, args_class = None, None, None + if init.get("class_path", None): + if not shortcircuit_local: + class_module, class_name = init["class_path"].rsplit(".", 1) + else: # class is expected to be locally defined + args_class = globals()[init["class_path"]] + elif init.get("callback_key", None): + callback_path = CALLBACK_REGISTRY.get( + init["callback_key"], None + ) or MOCK_HUB_REGISTRY.get(init["callback_key"], None) + assert callback_path, MisconfigurationException( + f'specified callback_key {init["callback_key"]} has not been registered' + ) + class_module, class_name = callback_path.__module__, callback_path.__name__ + else: + raise MisconfigurationException( + "Neither a class_path nor callback_key were included in a configuration that" + "requires one" + ) + if not shortcircuit_local: + module = __import__(class_module, fromlist=[class_name]) + args_class = getattr(module, class_name) + return args_class(**kwargs) if not args else args_class(*args, **kwargs) + + +# %% +# load the pl extension module we want to use. This will import all necessary callbacks. +module_hub_mock("finetuningscheduler") +# set notebook-level variables +AVAIL_GPUS = min(1, torch.cuda.device_count()) +TASK_NUM_LABELS = {"boolq": 2, "rte": 2} +DEFAULT_TASK = "rte" + +# narrow our logging to adapt it for a notebook environment +for l_key in logging.Logger.manager.loggerDict.keys(): + if "pytorch_lightning" in l_key: + logging.getLogger(l_key).setLevel("INFO") + else: + logging.getLogger(l_key).setLevel("CRITICAL") +pl_logger = logging.getLogger("pytorch_lightning") +pl_logger.removeHandler(pl_logger.handlers[0]) +rz_logger = logging.getLogger("pytorch_lightning.utilities.distributed") +rz_logger.addHandler(logging.StreamHandler()) +rz_logger.handlers[0].setLevel("INFO") # %% -class GLUEDataModule(LightningDataModule): - - task_text_field_map = { - "cola": ["sentence"], - "sst2": ["sentence"], - "mrpc": ["sentence1", "sentence2"], - "qqp": ["question1", "question2"], - "stsb": ["sentence1", "sentence2"], - "mnli": ["premise", "hypothesis"], - "qnli": ["question", "sentence"], - "rte": ["sentence1", "sentence2"], - "wnli": ["sentence1", "sentence2"], - "ax": ["premise", "hypothesis"], - } - - glue_task_num_labels = { - "cola": 2, - "sst2": 2, - "mrpc": 2, - "qqp": 2, - "stsb": 1, - "mnli": 3, - "qnli": 2, - "rte": 2, - "wnli": 2, - "ax": 3, - } +class RteBoolqDataModule(pl.LightningDataModule): + """A ``LightningDataModule`` for using either the RTE or BoolQ SuperGLUE Hugging Face datasets""" + task_text_field_map = {"rte": ["premise", "hypothesis"], "boolq": ["question", "passage"]} loader_columns = [ "datasets_idx", "input_ids", @@ -61,117 +278,167 @@ class GLUEDataModule(LightningDataModule): "end_positions", "labels", ] + # ignore warnings related tokenizers_parallelism/DataLoader parallelism tradeoff and + # expected logging behavior + for warnf in [".*does not have many workers*", ".*The number of training samples.*"]: + warnings.filterwarnings("ignore", warnf) def __init__( self, model_name_or_path: str, - task_name: str = "mrpc", + task_name: str = DEFAULT_TASK, + prep_on_init: bool = False, max_seq_length: int = 128, train_batch_size: int = 32, eval_batch_size: int = 32, - **kwargs, + pin_memory: bool = False, + tokenizers_parallelism: bool = True, + num_workers: int = 0, ): super().__init__() self.model_name_or_path = model_name_or_path - self.task_name = task_name + self.task_name = task_name if task_name in TASK_NUM_LABELS.keys() else DEFAULT_TASK self.max_seq_length = max_seq_length self.train_batch_size = train_batch_size self.eval_batch_size = eval_batch_size - - self.text_fields = self.task_text_field_map[task_name] - self.num_labels = self.glue_task_num_labels[task_name] - self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) - - def setup(self, stage: str): - self.dataset = datasets.load_dataset("glue", self.task_name) - + self.tokenizers_parallelism = tokenizers_parallelism + self.dataloader_kwargs = {"num_workers": num_workers, "pin_memory": pin_memory} + self.text_fields = self.task_text_field_map[self.task_name] + self.num_labels = TASK_NUM_LABELS[self.task_name] + os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.tokenizers_parallelism else "false" + self.tokenizer = AutoTokenizer.from_pretrained( + self.model_name_or_path, use_fast=True, local_files_only=False + ) + if ( + prep_on_init + ): # useful if one wants to load datasets as soon as the ``LightningDataModule`` is + # instantiated + self.prepare_data() + self.setup("fit") + + def setup(self, stage): + self.dataset = datasets.load_dataset("super_glue", self.task_name) for split in self.dataset.keys(): self.dataset[split] = self.dataset[split].map( - self.convert_to_features, - batched=True, - remove_columns=["label"], + self.convert_to_features, batched=True, remove_columns=["label"] ) - self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] + self.columns = [ + c for c in self.dataset[split].column_names if c in self.loader_columns + ] self.dataset[split].set_format(type="torch", columns=self.columns) self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] def prepare_data(self): - datasets.load_dataset("glue", self.task_name) - AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True) + # N.B. PL calls prepare_data from a single process (rank 0) so do not use it to assign + # state (e.g. self.x=y) + datasets.load_dataset("super_glue", self.task_name) def train_dataloader(self): - return DataLoader(self.dataset["train"], batch_size=self.train_batch_size) + return DataLoader( + self.dataset["train"], batch_size=self.train_batch_size, **self.dataloader_kwargs + ) def val_dataloader(self): if len(self.eval_splits) == 1: - return DataLoader(self.dataset["validation"], batch_size=self.eval_batch_size) + return DataLoader( + self.dataset["validation"], + batch_size=self.eval_batch_size, + **self.dataloader_kwargs, + ) elif len(self.eval_splits) > 1: - return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits] + return [ + DataLoader( + self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs + ) + for x in self.eval_splits + ] def test_dataloader(self): if len(self.eval_splits) == 1: - return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size) + return DataLoader( + self.dataset["test"], batch_size=self.eval_batch_size, **self.dataloader_kwargs + ) elif len(self.eval_splits) > 1: - return [DataLoader(self.dataset[x], batch_size=self.eval_batch_size) for x in self.eval_splits] - - def convert_to_features(self, example_batch, indices=None): - - # Either encode single sentence or sentence pairs - if len(self.text_fields) > 1: - texts_or_text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) - else: - texts_or_text_pairs = example_batch[self.text_fields[0]] - + return [ + DataLoader( + self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs + ) + for x in self.eval_splits + ] + + def convert_to_features(self, example_batch): + text_pairs = list( + zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]) + ) # Tokenize the text/text pairs features = self.tokenizer.batch_encode_plus( - texts_or_text_pairs, max_length=self.max_seq_length, pad_to_max_length=True, truncation=True + text_pairs, max_length=self.max_seq_length, padding="longest", truncation=True ) - # Rename label to labels to make it easier to pass to model forward features["labels"] = example_batch["label"] - return features -# %% [markdown] -# **You could use this datamodule with standalone PyTorch if you wanted...** - # %% -dm = GLUEDataModule("distilbert-base-uncased") -dm.prepare_data() -dm.setup("fit") -next(iter(dm.train_dataloader())) - -# %% [markdown] -# ### Transformer LightningModule - +class RteBoolqModule(pl.LightningModule): + """A ``LightningModule`` that can be used to finetune a foundational + model on either the RTE or BoolQ SuperGLUE tasks using Hugging Face + implementations of a given model and the `SuperGLUE Hugging Face dataset. + """ -# %% -class GLUETransformer(LightningModule): def __init__( self, model_name_or_path: str, - num_labels: int, - task_name: str, - learning_rate: float = 2e-5, - adam_epsilon: float = 1e-8, - warmup_steps: int = 0, - weight_decay: float = 0.0, - train_batch_size: int = 32, - eval_batch_size: int = 32, - eval_splits: Optional[list] = None, - **kwargs, + optimizer_init: Dict[str, Any], + lr_scheduler_init: Dict[str, Any], + pl_lrs_cfg: Optional[Dict[str, Any]] = None, + model_cfg: Optional[Dict[str, Any]] = None, + task_name: str = DEFAULT_TASK, + experiment_tag: str = "default", + plot_liveloss: bool = False, ): + """ + Args: + model_name_or_path (str): Path to pretrained model or identifier from https://huggingface.co/models + optimizer_init (Dict[str, Any]): The desired optimizer configuration. + lr_scheduler_init (Dict[str, Any]): The desired learning rate scheduler config + pl_lrs_cfg (Optional[Dict[str, Any]]): Defines custom overrides of pytorch lightning lr_scheduler defaults + defined in ``_get_default_scheduler_config`` + model_cfg (Optional[Dict[str, Any]], optional): Defines overrides of the default model config. Defaults to + ``None``. + task_name (str, optional): The SuperGLUE task to execute, one of ``'rte'``, ``'boolq'``. Defaults to "rte". + experiment_tag (str, optional): The tag to use for the experiment and tensorboard logs. Defaults to + "default". + """ super().__init__() - + self.optimizer_init = optimizer_init + self.lr_scheduler_init = lr_scheduler_init + self.plot_liveloss = plot_liveloss + self.pl_lrs_cfg = pl_lrs_cfg or {} + if task_name in TASK_NUM_LABELS.keys(): + self.task_name = task_name + else: + self.task_name = DEFAULT_TASK + rank_zero_warn( + f"Invalid task_name '{task_name}'. Proceeding with the default task: '{DEFAULT_TASK}'" + ) + self.num_labels = TASK_NUM_LABELS[self.task_name] self.save_hyperparameters() - - self.config = AutoConfig.from_pretrained(model_name_or_path, num_labels=num_labels) - self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=self.config) + self.experiment_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{experiment_tag}" + self.model_cfg = model_cfg or {} + conf = AutoConfig.from_pretrained( + model_name_or_path, num_labels=self.num_labels, local_files_only=False + ) + self.model = AutoModelForSequenceClassification.from_pretrained( + model_name_or_path, config=conf + ) + self.model.config.update(self.model_cfg) # apply model config overrides self.metric = datasets.load_metric( - "glue", self.hparams.task_name, experiment_id=datetime.now().strftime("%d-%m-%Y_%H-%M-%S") + "super_glue", self.task_name, experiment_id=self.experiment_id ) + self.no_decay = ["bias", "LayerNorm.weight"] + self.finetuningscheduler_callback = None def forward(self, **inputs): return self.model(**inputs) @@ -181,146 +448,171 @@ def training_step(self, batch, batch_idx): loss = outputs[0] return loss + def training_epoch_end(self, outputs: List[Any]) -> None: + loss = torch.stack([x["loss"] for x in outputs]).mean() + self.log("train_loss", loss, prog_bar=True, sync_dist=True) + if self.finetuningscheduler_callback: + self.log("finetuning_schedule_depth", self.finetuningscheduler_callback.curr_depth) + def validation_step(self, batch, batch_idx, dataloader_idx=0): outputs = self(**batch) val_loss, logits = outputs[:2] - if self.hparams.num_labels >= 1: + if self.num_labels >= 1: preds = torch.argmax(logits, axis=1) - elif self.hparams.num_labels == 1: + elif self.num_labels == 1: preds = logits.squeeze() labels = batch["labels"] - return {"loss": val_loss, "preds": preds, "labels": labels} def validation_epoch_end(self, outputs): - if self.hparams.task_name == "mnli": - for i, output in enumerate(outputs): - # matched or mismatched - split = self.hparams.eval_splits[i].split("_")[-1] - preds = torch.cat([x["preds"] for x in output]).detach().cpu().numpy() - labels = torch.cat([x["labels"] for x in output]).detach().cpu().numpy() - loss = torch.stack([x["loss"] for x in output]).mean() - self.log(f"val_loss_{split}", loss, prog_bar=True) - split_metrics = { - f"{k}_{split}": v for k, v in self.metric.compute(predictions=preds, references=labels).items() - } - self.log_dict(split_metrics, prog_bar=True) - return loss - preds = torch.cat([x["preds"] for x in outputs]).detach().cpu().numpy() labels = torch.cat([x["labels"] for x in outputs]).detach().cpu().numpy() loss = torch.stack([x["loss"] for x in outputs]).mean() - self.log("val_loss", loss, prog_bar=True) - self.log_dict(self.metric.compute(predictions=preds, references=labels), prog_bar=True) + self.log("val_loss", loss, prog_bar=True, sync_dist=True) + metric_dict = self.metric.compute(predictions=preds, references=labels) + self.log_dict(metric_dict, prog_bar=True, sync_dist=True) return loss - def setup(self, stage=None) -> None: - if stage != "fit": - return - # Get dataloader by calling it - train_dataloader() is called after setup() by default - # train_loader = self.train_dataloader() - train_loader = self.trainer.datamodule.train_dataloader() - # Calculate total steps - tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus) - ab_size = self.trainer.accumulate_grad_batches * float(self.trainer.max_epochs) - self.total_steps = (len(train_loader.dataset) // tb_size) // ab_size + def init_pgs(self) -> List[Dict]: + """Initialize the parameter groups. Used to ensure weight_decay is not applied + to our specified bias parameters when we initialize the optimizer. - def configure_optimizers(self): - """Prepare optimizer and schedule (linear warmup and decay)""" - model = self.model - no_decay = ["bias", "LayerNorm.weight"] - optimizer_grouped_parameters = [ + Returns: + List[Dict]: A list of parameter group dictionaries. + """ + pgs = [ { - "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], - "weight_decay": self.hparams.weight_decay, + "params": [ + p + for n, p in self.model.named_parameters() + if not any(nd in n for nd in self.no_decay) and p.requires_grad + ], + "weight_decay": self.optimizer_init["init_args"]["weight_decay"], }, { - "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], + "params": [ + p + for n, p in self.model.named_parameters() + if any(nd in n for nd in self.no_decay) and p.requires_grad + ], "weight_decay": 0.0, }, ] - optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon) + return pgs - scheduler = get_linear_schedule_with_warmup( - optimizer, - num_warmup_steps=self.hparams.warmup_steps, - num_training_steps=self.total_steps, - ) - scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1} + def configure_optimizers(self): + # the phase 0 parameters will have been set to require gradients during setup + # you can initialize the optimizer with a simple requires.grad filter as is often done, + # but in this case we pass a list of parameter groups to ensure weight_decay is + # not applied to the bias parameter (for completeness, in this case it won't make much + # performance difference) + optimizer = instantiate_registered_class(args=self.init_pgs(), init=self.optimizer_init) + scheduler = { + "scheduler": instantiate_registered_class( + args=optimizer, init=self.lr_scheduler_init + ), + **self.pl_lrs_cfg, + } return [optimizer], [scheduler] + def configure_callbacks(self): + found_fts = [c for c in self.trainer.callbacks if isinstance(c, FinetuningScheduler)] # type: ignore # noqa + if found_fts: + self.finetuningscheduler_callback = found_fts[0] + return super().configure_callbacks() -# %% [markdown] -# ## Training - -# %% [markdown] -# ### CoLA -# -# See an interactive view of the -# CoLA dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=cola) # %% -seed_everything(42) +# let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it +# Please see the documentation for a full description of the schedule format +ft_schedule_yaml = """ +0: + params: # the parameters for each phase definition can be fully specified + - model.classifier.bias + - model.classifier.weight +1: + params: # or specified via a regex + - model.albert.pooler.* +2: + params: + - model.albert.encoder.*.ffn_output.* +""" +ft_schedule_name = "RteBoolqModule_ft_schedule_albert_base.yaml" +with open(ft_schedule_name, "w") as f: + f.write(ft_schedule_yaml) -dm = GLUEDataModule(model_name_or_path="albert-base-v2", task_name="cola") +# %% +datasets.set_progress_bar_enabled(False) +pl.seed_everything(42) +dm = RteBoolqDataModule(model_name_or_path="albert-base-v2", tokenizers_parallelism=False) dm.setup("fit") -model = GLUETransformer( +optimizer_init = { + "class_path": "torch.optim.AdamW", + "init_args": {"weight_decay": 1e-05, "eps": 1e-07, "lr": 1e-05}, +} +lr_scheduler_init = { + "class_path": "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts", + "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, +} +pl_lrs_cfg = {"interval": "epoch", "frequency": 1, "name": "CosineAnnealingWarmRestarts"} + +model = RteBoolqModule( model_name_or_path="albert-base-v2", - num_labels=dm.num_labels, - eval_splits=dm.eval_splits, - task_name=dm.task_name, + optimizer_init=optimizer_init, + lr_scheduler_init=lr_scheduler_init, + pl_lrs_cfg=pl_lrs_cfg, + experiment_tag="fts_explicit", ) - -trainer = Trainer(max_epochs=1, gpus=AVAIL_GPUS) -trainer.fit(model, dm) - -# %% [markdown] -# ### MRPC -# -# See an interactive view of the -# MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mrpc) +callbacks = [ + FinetuningScheduler(ft_schedule=ft_schedule_name, max_depth=2), # type: ignore # noqa + FTSEarlyStopping(monitor="val_loss", min_delta=0.001, patience=2), # type: ignore # noqa + FTSCheckpoint(monitor="val_loss", save_top_k=5), # type: ignore # noqa +] +example_logdir = "lightning_logs" +logger = TensorBoardLogger(example_logdir, name="fts_explicit") # %% -seed_everything(42) +# optionally start tensorboard and monitor progress graphically while viewing multi-phase finetuning specific training +# logs in the cell output below by uncommenting the next 3 lines +# # !mkdir -p $example_logdir +# # %load_ext tensorboard +# # %tensorboard --logdir example_logdir +# disable progress bar by default to focus on multi-phase training logs. Set to True to re-enable if desired +show_progress = False -dm = GLUEDataModule( - model_name_or_path="distilbert-base-cased", - task_name="mrpc", -) -dm.setup("fit") -model = GLUETransformer( - model_name_or_path="distilbert-base-cased", - num_labels=dm.num_labels, - eval_splits=dm.eval_splits, - task_name=dm.task_name, +# %% +trainer = pl.Trainer( + enable_progress_bar=show_progress, + precision=16, + gpus=AVAIL_GPUS, + callbacks=callbacks, + logger=logger, ) - -trainer = Trainer(max_epochs=3, gpus=AVAIL_GPUS) -trainer.fit(model, dm) +trainer.fit(model, datamodule=dm) # %% [markdown] -# ### MNLI +# ## Footnotes # -# - The MNLI dataset is huge, so we aren't going to bother trying to train on it here. -# - We will skip over training and go straight to validation. +#
    +#
  1. # -# See an interactive view of the -# MRPC dataset in [NLP Viewer](https://huggingface.co/nlp/viewer/?dataset=glue&config=mnli) - -# %% -dm = GLUEDataModule( - model_name_or_path="distilbert-base-cased", - task_name="mnli", -) -dm.setup("fit") -model = GLUETransformer( - model_name_or_path="distilbert-base-cased", - num_labels=dm.num_labels, - eval_splits=dm.eval_splits, - task_name=dm.task_name, -) - -trainer = Trainer(gpus=AVAIL_GPUS, progress_bar_refresh_rate=20) -trainer.validate(model, dm.val_dataloader()) +# [Howard, J., & Ruder, S. (2018)](https://arxiv.org/pdf/1801.06146.pdf). Fine-tuned Language +# Models for Text Classification. ArXiv, abs/1801.06146. [↩](#a1) +# +#
    2. +#
  2. +# +# [Chronopoulou, A., Baziotis, C., & Potamianos, A. (2019)](https://arxiv.org/pdf/1902.10547.pdf). +# An embarrassingly simple approach for transfer learning from pretrained language models. arXiv +# preprint arXiv:1902.10547. [↩](#a1) +# +#
    3. +#
  3. +# +# [Peters, M. E., Ruder, S., & Smith, N. A. (2019)](https://arxiv.org/pdf/1903.05987.pdf). To tune or not to +# tune? adapting pretrained representations to diverse tasks. arXiv preprint arXiv:1903.05987. [↩](#a1) +# +#
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zq_{Zp^CKxPri`xP;>MX{uF(wa{FKxdJD7r>;q<&{r&a)25dqrbWA@P!TdcGbAB{GT zxuzwx#WF^(NSw%AHF+95qb|8H9iC})GVm=GfzoA!wEh9_Kbhyn%h7-)+d z!$zgHxK?zo?E+&+l;10SB(cR@R)hm+i!HuHgSOZo4~Vam&VaahQ55NV<8ogih>I^* z%;LG*;`(H+N^r5I6c-P<)2(9(zs3?PNMTo@r%SCKNpLalp2(h;s1D*{Zt^-VE{f7Y znFkqr;`NsBu?jb`M8H)d!r$UU`Q1CN7mvb&L0deTEBOpBEip@h+*7$QN;00Ctf3#Q z&Hs(oI8TKu05_KJyIxGIS*G#jCNB_SdU-C8OF0$5&TYTv|5jbVOIBx=xcbY*t3y>6 zqv*J=6=`3ysb8zIfvl_0yV(E>zpRmKl|c;J1lr=+&^23(8ly%+d_h}0abFu2l9_Fl zRxV4M{zJTmiybb$!t3GE<*(+F;@U?+T-Jeke|Bc7+Rxi)u^g`|=5I9G7AJI}f))=g!zyPS{#(*1}yX^CsM{nff#+ zHu@rdp|K*l4J-Cl#4)ZCn_mgO5LXxPtu7U=E?2C6eYN_nXLV(4Rs3ajReIswg`3q| zQ@{V`!rMsE2QR##67}o(IAUVX@Xea~m0oxe__(K~(6&gr@OEzM zBcAD1KGR1#R;s!!maTw + + + + + + + image/svg+xml + + + + + + + + + From 3a42523e9f3a1fc03c1f4586cfa9b8aeea510abf Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Mon, 29 Nov 2021 19:48:08 -0800 Subject: [PATCH 13/44] tweak md format to H2 and update meta info --- lightning_examples/finetuning-scheduler/.meta.yml | 6 +++--- .../finetuning-scheduler/finetuning-scheduler.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 33296fba5..d1cfeb47d 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -1,7 +1,7 @@ title: Finetuning Scheduler author: Dan Dale -created: 2021-11-26 -updated: 2021-11-26 +created: 2021-11-29 +updated: 2021-11-29 license: CC BY-SA build: 1 tags: @@ -10,7 +10,7 @@ description: | This notebook introduces the FinetuningScheduler callback and demonstrates the use of FinetuningScheduler to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/) with iterative earlystopping defined according to a user-specified - schedule. It uses HuggingFace's `datasets` and `transformers`` libraries to retrieve the relevant benchmark data and + schedule. It uses HuggingFace's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data and foundational model weights. requirements: - transformers diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 9669cb632..4a38f96c4 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -15,7 +15,7 @@ # --- # %% [markdown] -# # Scheduled Finetuning +# ## Scheduled Finetuning # #
    # @@ -150,7 +150,7 @@ # %% [markdown] #
    # -# # Scheduled Finetuning For SuperGLUE +# ## Scheduled Finetuning For SuperGLUE # # The following example demonstrates the use of ``FinetuningScheduler`` to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. # From 7ddbcd4a6a47478f607f046fbf4b22986e84f690 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Tue, 30 Nov 2021 10:57:11 -0800 Subject: [PATCH 14/44] update tutorial reqs --- docs/requirements.txt | 3 + .../finetuning-scheduler/.meta.yml | 11 +- .../finetuning-scheduler.py | 108 ++++++------------ .../fts_explicit_loss_anim.gif | Bin 302052 -> 172945 bytes 4 files changed, 44 insertions(+), 78 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index f721f461a..cd93ce2ee 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -6,6 +6,9 @@ docutils>=0.16 sphinx-paramlinks>=0.4.0 ipython[notebook] +# temporarily included until hub available to evaluate finetuning_scheduler +git+git://github.com/speediedan/pytorch-lightning.git@24d3e43568814ec381ac5be91627629808d62081#egg=pytorch-lightning + https://github.com/PyTorchLightning/lightning_sphinx_theme/archive/master.zip#egg=pt-lightning-sphinx-theme -r ../.actions/requirements.txt diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index d1cfeb47d..3b63b73e5 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -7,13 +7,14 @@ build: 1 tags: - finetuning description: | - This notebook introduces the FinetuningScheduler callback and demonstrates the use of FinetuningScheduler to finetune - a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task - of [SuperGLUE](https://super.gluebenchmark.com/) with iterative earlystopping defined according to a user-specified - schedule. It uses HuggingFace's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data and - foundational model weights. + This notebook introduces the FinetuningScheduler callback and demonstrates the use of FinetuningScheduler to finetune + a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task + of [SuperGLUE](https://super.gluebenchmark.com/) with iterative early-stopping defined according to a user-specified + schedule. It uses HuggingFace's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data + and foundational model weights. requirements: - transformers - datasets + - scikit-learn accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 4a38f96c4..5aab8f6df 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- # --- # jupyter: # jupytext: @@ -7,7 +6,7 @@ # extension: .py # format_name: percent # format_version: '1.3' -# jupytext_version: 1.13.1 +# jupytext_version: 1.13.2 # kernelspec: # display_name: 'Python 3.7.11 64-bit (''pldev_tutorials'': conda)' # language: python @@ -84,7 +83,7 @@ # ``` # %% [markdown] -# ## EarlyStopping and Epoch-Driven Phase Transition Criteria +# ## Early-Stopping and Epoch-Driven Phase Transition Criteria # # # By default, ``FTSEarlyStopping`` and epoch-driven @@ -155,28 +154,26 @@ # The following example demonstrates the use of ``FinetuningScheduler`` to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. # # ``FinetuningScheduler`` can be used to achieve non-trivial model performance improvements in both implicit and explicit scheduling contexts at an also non-trivial computational cost. +# # %% +import logging import os import warnings from datetime import datetime -from typing import Any, Dict, List, Optional, Tuple, Union from importlib import import_module -import logging - -import torch -from torch.utils.data import DataLoader +from typing import Any, Dict, List, Optional, Tuple, Union +import datasets import pytorch_lightning as pl +import torch +from pytorch_lightning.loggers.tensorboard import TensorBoardLogger from pytorch_lightning.utilities import rank_zero_warn from pytorch_lightning.utilities.cli import CALLBACK_REGISTRY, _Registry from pytorch_lightning.utilities.exceptions import MisconfigurationException -from pytorch_lightning.loggers.tensorboard import TensorBoardLogger - -import datasets +from torch.utils.data import DataLoader from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer - # %% # a couple helper functions to prepare code to work with the forthcoming hub and user module registry MOCK_HUB_REGISTRY = _Registry() @@ -195,17 +192,13 @@ def module_hub_mock(key: str, require_fqn: bool = False) -> List: globals()[f"{n}"] = c registered_list = ", ".join([n for n in MOCK_HUB_REGISTRY.names]) else: - registered_list = ", ".join( - [c.__module__ + "." + c.__name__ for c in MOCK_HUB_REGISTRY.classes] - ) + registered_list = ", ".join([c.__module__ + "." + c.__name__ for c in MOCK_HUB_REGISTRY.classes]) print(f"Imported and registered the following callbacks: {registered_list}") -def instantiate_registered_class( - init: Dict[str, Any], args: Optional[Union[Any, Tuple[Any, ...]]] = None -) -> Any: - """Instantiates a class with the given args and init. Accepts class definitions in the form - of a "class_path" or "callback_key" associated with a _Registry +def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, Tuple[Any, ...]]] = None) -> Any: + """Instantiates a class with the given args and init. Accepts class definitions in the form of a "class_path" + or "callback_key" associated with a _Registry. Args: init: Dict of the form {"class_path":... or "callback_key":..., "init_args":...}. @@ -225,17 +218,16 @@ def instantiate_registered_class( else: # class is expected to be locally defined args_class = globals()[init["class_path"]] elif init.get("callback_key", None): - callback_path = CALLBACK_REGISTRY.get( + callback_path = CALLBACK_REGISTRY.get(init["callback_key"], None) or MOCK_HUB_REGISTRY.get( init["callback_key"], None - ) or MOCK_HUB_REGISTRY.get(init["callback_key"], None) + ) assert callback_path, MisconfigurationException( f'specified callback_key {init["callback_key"]} has not been registered' ) class_module, class_name = callback_path.__module__, callback_path.__name__ else: raise MisconfigurationException( - "Neither a class_path nor callback_key were included in a configuration that" - "requires one" + "Neither a class_path nor callback_key were included in a configuration that" "requires one" ) if not shortcircuit_local: module = __import__(class_module, fromlist=[class_name]) @@ -266,7 +258,7 @@ def instantiate_registered_class( # %% class RteBoolqDataModule(pl.LightningDataModule): - """A ``LightningDataModule`` for using either the RTE or BoolQ SuperGLUE Hugging Face datasets""" + """A ``LightningDataModule`` for using either the RTE or BoolQ SuperGLUE Hugging Face datasets.""" task_text_field_map = {"rte": ["premise", "hypothesis"], "boolq": ["question", "passage"]} loader_columns = [ @@ -306,12 +298,8 @@ def __init__( self.text_fields = self.task_text_field_map[self.task_name] self.num_labels = TASK_NUM_LABELS[self.task_name] os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.tokenizers_parallelism else "false" - self.tokenizer = AutoTokenizer.from_pretrained( - self.model_name_or_path, use_fast=True, local_files_only=False - ) - if ( - prep_on_init - ): # useful if one wants to load datasets as soon as the ``LightningDataModule`` is + self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True, local_files_only=False) + if prep_on_init: # useful if one wants to load datasets as soon as the ``LightningDataModule`` is # instantiated self.prepare_data() self.setup("fit") @@ -322,9 +310,7 @@ def setup(self, stage): self.dataset[split] = self.dataset[split].map( self.convert_to_features, batched=True, remove_columns=["label"] ) - self.columns = [ - c for c in self.dataset[split].column_names if c in self.loader_columns - ] + self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] self.dataset[split].set_format(type="torch", columns=self.columns) self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] @@ -335,9 +321,7 @@ def prepare_data(self): datasets.load_dataset("super_glue", self.task_name) def train_dataloader(self): - return DataLoader( - self.dataset["train"], batch_size=self.train_batch_size, **self.dataloader_kwargs - ) + return DataLoader(self.dataset["train"], batch_size=self.train_batch_size, **self.dataloader_kwargs) def val_dataloader(self): if len(self.eval_splits) == 1: @@ -348,29 +332,21 @@ def val_dataloader(self): ) elif len(self.eval_splits) > 1: return [ - DataLoader( - self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs - ) + DataLoader(self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs) for x in self.eval_splits ] def test_dataloader(self): if len(self.eval_splits) == 1: - return DataLoader( - self.dataset["test"], batch_size=self.eval_batch_size, **self.dataloader_kwargs - ) + return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size, **self.dataloader_kwargs) elif len(self.eval_splits) > 1: return [ - DataLoader( - self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs - ) + DataLoader(self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs) for x in self.eval_splits ] def convert_to_features(self, example_batch): - text_pairs = list( - zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]) - ) + text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) # Tokenize the text/text pairs features = self.tokenizer.batch_encode_plus( text_pairs, max_length=self.max_seq_length, padding="longest", truncation=True @@ -382,10 +358,8 @@ def convert_to_features(self, example_batch): # %% class RteBoolqModule(pl.LightningModule): - """A ``LightningModule`` that can be used to finetune a foundational - model on either the RTE or BoolQ SuperGLUE tasks using Hugging Face - implementations of a given model and the `SuperGLUE Hugging Face dataset. - """ + """A ``LightningModule`` that can be used to finetune a foundational model on either the RTE or BoolQ SuperGLUE + tasks using Hugging Face implementations of a given model and the `SuperGLUE Hugging Face dataset.""" def __init__( self, @@ -396,7 +370,6 @@ def __init__( model_cfg: Optional[Dict[str, Any]] = None, task_name: str = DEFAULT_TASK, experiment_tag: str = "default", - plot_liveloss: bool = False, ): """ Args: @@ -414,29 +387,20 @@ def __init__( super().__init__() self.optimizer_init = optimizer_init self.lr_scheduler_init = lr_scheduler_init - self.plot_liveloss = plot_liveloss self.pl_lrs_cfg = pl_lrs_cfg or {} if task_name in TASK_NUM_LABELS.keys(): self.task_name = task_name else: self.task_name = DEFAULT_TASK - rank_zero_warn( - f"Invalid task_name '{task_name}'. Proceeding with the default task: '{DEFAULT_TASK}'" - ) + rank_zero_warn(f"Invalid task_name '{task_name}'. Proceeding with the default task: '{DEFAULT_TASK}'") self.num_labels = TASK_NUM_LABELS[self.task_name] self.save_hyperparameters() self.experiment_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{experiment_tag}" self.model_cfg = model_cfg or {} - conf = AutoConfig.from_pretrained( - model_name_or_path, num_labels=self.num_labels, local_files_only=False - ) - self.model = AutoModelForSequenceClassification.from_pretrained( - model_name_or_path, config=conf - ) + conf = AutoConfig.from_pretrained(model_name_or_path, num_labels=self.num_labels, local_files_only=False) + self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=conf) self.model.config.update(self.model_cfg) # apply model config overrides - self.metric = datasets.load_metric( - "super_glue", self.task_name, experiment_id=self.experiment_id - ) + self.metric = datasets.load_metric("super_glue", self.task_name, experiment_id=self.experiment_id) self.no_decay = ["bias", "LayerNorm.weight"] self.finetuningscheduler_callback = None @@ -476,8 +440,8 @@ def validation_epoch_end(self, outputs): return loss def init_pgs(self) -> List[Dict]: - """Initialize the parameter groups. Used to ensure weight_decay is not applied - to our specified bias parameters when we initialize the optimizer. + """Initialize the parameter groups. Used to ensure weight_decay is not applied to our specified bias + parameters when we initialize the optimizer. Returns: List[Dict]: A list of parameter group dictionaries. @@ -510,9 +474,7 @@ def configure_optimizers(self): # performance difference) optimizer = instantiate_registered_class(args=self.init_pgs(), init=self.optimizer_init) scheduler = { - "scheduler": instantiate_registered_class( - args=optimizer, init=self.lr_scheduler_init - ), + "scheduler": instantiate_registered_class(args=optimizer, init=self.lr_scheduler_init), **self.pl_lrs_cfg, } return [optimizer], [scheduler] @@ -568,7 +530,7 @@ def configure_callbacks(self): callbacks = [ FinetuningScheduler(ft_schedule=ft_schedule_name, max_depth=2), # type: ignore # noqa FTSEarlyStopping(monitor="val_loss", min_delta=0.001, patience=2), # type: ignore # noqa - FTSCheckpoint(monitor="val_loss", save_top_k=5), # type: ignore # noqa + FTSCheckpoint(monitor="val_loss", save_top_k=5), # type: ignore # noqa ] example_logdir = "lightning_logs" logger = TensorBoardLogger(example_logdir, name="fts_explicit") diff --git 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ipython[notebook] -# temporarily included until hub available to evaluate finetuning_scheduler -git+git://github.com/speediedan/pytorch-lightning.git@24d3e43568814ec381ac5be91627629808d62081#egg=pytorch-lightning - https://github.com/PyTorchLightning/lightning_sphinx_theme/archive/master.zip#egg=pt-lightning-sphinx-theme -r ../.actions/requirements.txt diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 5aab8f6df..027275b92 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,18 +1,3 @@ -# --- -# jupyter: -# jupytext: -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.2 -# kernelspec: -# display_name: 'Python 3.7.11 64-bit (''pldev_tutorials'': conda)' -# language: python -# name: python3 -# --- - # %% [markdown] # ## Scheduled Finetuning # @@ -34,7 +19,7 @@ # final phase of the schedule has its stopping criteria met. See # the [early stopping documentation](https://pytorch-lightning.readthedocs.io/en/latest/extensions/generated/pytorch_lightning.callbacks.EarlyStopping.html) for more details on that callback's configuration. # -# +# ![FinetuningScheduler explicit loss animation](fts_explicit_loss_anim.gif) # %% [markdown] # ## Basic Usage @@ -416,7 +401,7 @@ def training_epoch_end(self, outputs: List[Any]) -> None: loss = torch.stack([x["loss"] for x in outputs]).mean() self.log("train_loss", loss, prog_bar=True, sync_dist=True) if self.finetuningscheduler_callback: - self.log("finetuning_schedule_depth", self.finetuningscheduler_callback.curr_depth) + self.log("finetuning_schedule_depth", float(self.finetuningscheduler_callback.curr_depth)) def validation_step(self, batch, batch_idx, dataloader_idx=0): outputs = self(**batch) From b250845e601a43db7adad699b49cac8f941cbc65 Mon Sep 17 00:00:00 2001 From: Dan Dale Date: Thu, 2 Dec 2021 09:06:05 -0800 Subject: [PATCH 16/44] Update lightning_examples/finetuning-scheduler/finetuning-scheduler.py Co-authored-by: thomas chaton --- .../finetuning-scheduler.py | 21 +++++++++---------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 027275b92..9cc473742 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -24,7 +24,7 @@ # %% [markdown] # ## Basic Usage # -# If no finetuning schedule is user-provided, ``FinetuningScheduler`` will generate a +# If no finetuning schedule is provided by the user, ``FinetuningScheduler`` will generate a # [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, using default ``FTSEarlyStopping`` and ``FTSCheckpoint`` callbacks with ``monitor=val_loss``. # # ```python @@ -268,9 +268,8 @@ def __init__( max_seq_length: int = 128, train_batch_size: int = 32, eval_batch_size: int = 32, - pin_memory: bool = False, tokenizers_parallelism: bool = True, - num_workers: int = 0, + **dataloader_kwargs: Any, ): super().__init__() self.model_name_or_path = model_name_or_path @@ -399,7 +398,7 @@ def training_step(self, batch, batch_idx): def training_epoch_end(self, outputs: List[Any]) -> None: loss = torch.stack([x["loss"] for x in outputs]).mean() - self.log("train_loss", loss, prog_bar=True, sync_dist=True) + self.log("train_loss", loss, prog_bar=True) if self.finetuningscheduler_callback: self.log("finetuning_schedule_depth", float(self.finetuningscheduler_callback.curr_depth)) @@ -424,14 +423,14 @@ def validation_epoch_end(self, outputs): self.log_dict(metric_dict, prog_bar=True, sync_dist=True) return loss - def init_pgs(self) -> List[Dict]: + def _init_param_groups(self) -> List[Dict]: """Initialize the parameter groups. Used to ensure weight_decay is not applied to our specified bias parameters when we initialize the optimizer. Returns: List[Dict]: A list of parameter group dictionaries. """ - pgs = [ + return [ { "params": [ p @@ -449,7 +448,6 @@ def init_pgs(self) -> List[Dict]: "weight_decay": 0.0, }, ] - return pgs def configure_optimizers(self): # the phase 0 parameters will have been set to require gradients during setup @@ -472,7 +470,7 @@ def configure_callbacks(self): # %% -# let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it +# Let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it # Please see the documentation for a full description of the schedule format ft_schedule_yaml = """ 0: @@ -527,13 +525,14 @@ def configure_callbacks(self): # # %load_ext tensorboard # # %tensorboard --logdir example_logdir # disable progress bar by default to focus on multi-phase training logs. Set to True to re-enable if desired -show_progress = False +enable_progress_bar = False # %% trainer = pl.Trainer( - enable_progress_bar=show_progress, + enable_progress_bar=enable_progress_bar, precision=16, - gpus=AVAIL_GPUS, + accelerator="auto", + devices="auto", callbacks=callbacks, logger=logger, ) From 8077909c3193d68d06cfa22efe372421d99a21d4 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Thu, 2 Dec 2021 14:34:09 -0800 Subject: [PATCH 17/44] additional tuning literature references and several additional recommended improvements for finetuning_scheduler tutorial --- .../finetuning-scheduler.py | 182 +++++++++++------- 1 file changed, 115 insertions(+), 67 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 9cc473742..52a10fb23 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -100,7 +100,8 @@ # ```python # from pytorch_lightning import Trainer # from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler -# trainer = Trainer(callbacks=[FinetuningScheduler()], resume_from_checkpoint="some/path/to/my_checkpoint.ckpt") +# trainer = Trainer(callbacks=[FinetuningScheduler()]) +# trainer.fit(..., ckpt_path="some/path/to/my_checkpoint.ckpt") # ``` # # Training will resume at the depth/level of the provided checkpoint according the specified schedule. Schedules can be altered between training sessions but schedule compatibility is left to the user for maximal flexibility. If executing a user-defined schedule, typically the same schedule should be provided for the original and resumed training sessions. @@ -108,10 +109,8 @@ # By default (``FinetuningScheduler.restore_best`` is ``True``), ``FinetuningScheduler`` will attempt to restore the best available checkpoint before finetuning depth transitions. # # ```python -# trainer = Trainer( -# callbacks=[FinetuningScheduler(new_incarnation_mode=True)], -# resume_from_checkpoint="some/path/to/my_kth_best_checkpoint.ckpt", -# ) +# trainer = Trainer(callbacks=[FinetuningScheduler(new_incarnation_mode=True)]) +# trainer.fit(..., ckpt_path="some/path/to/my_kth_best_checkpoint.ckpt") # ``` # # To handle the edge case wherein one is resuming scheduled finetuning from a non-best checkpoint and the previous best checkpoints may not be accessible, setting ``FinetuningScheduler.new_incarnation_mode`` to @@ -120,14 +119,12 @@ # %% [markdown] #

    # -# **Note:** Currently, _FinetuningScheduler_ only supports the following _TrainingTypePlugins_: -# -# - ``DDPPlugin`` -# - ``DDPShardedPlugin`` -# - ``DDPSpawnPlugin`` -# - ``DDPSpawnShardedPlugin`` -# - ``DataParallelPlugin`` -# - ``SingleDevicePlugin`` +# **Note:** Currently, _FinetuningScheduler_ only supports the following ``StrategyType``s: +# - ``DP`` +# - ``DDP`` +# - ``DDP_SPAWN`` +# - ``DDP_SHARDED`` +# - ``DDP_SHARDED_SPAWN`` # #
    @@ -142,7 +139,6 @@ # # %% -import logging import os import warnings from datetime import datetime @@ -160,24 +156,24 @@ from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer # %% -# a couple helper functions to prepare code to work with the forthcoming hub and user module registry -MOCK_HUB_REGISTRY = _Registry() +# a couple helper functions to prepare code to work with a user module registry +MOCK_REGISTRY = _Registry() -def module_hub_mock(key: str, require_fqn: bool = False) -> List: +def mock_register_module(key: str, require_fqn: bool = False) -> List: if key.lower() == "finetuningscheduler": mod = import_module("pytorch_lightning.callbacks.finetuning_scheduler") - MOCK_HUB_REGISTRY.register_classes(mod, pl.callbacks.Callback) + MOCK_REGISTRY.register_classes(mod, pl.callbacks.Callback) else: raise MisconfigurationException(f"user module key '{key}' not found") registered_list = [] # make registered class available by unqualified class name by default if not require_fqn: - for n, c in MOCK_HUB_REGISTRY.items(): + for n, c in MOCK_REGISTRY.items(): globals()[f"{n}"] = c - registered_list = ", ".join([n for n in MOCK_HUB_REGISTRY.names]) + registered_list = ", ".join([n for n in MOCK_REGISTRY.names]) else: - registered_list = ", ".join([c.__module__ + "." + c.__name__ for c in MOCK_HUB_REGISTRY.classes]) + registered_list = ", ".join([c.__module__ + "." + c.__name__ for c in MOCK_REGISTRY.classes]) print(f"Imported and registered the following callbacks: {registered_list}") @@ -203,7 +199,7 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, else: # class is expected to be locally defined args_class = globals()[init["class_path"]] elif init.get("callback_key", None): - callback_path = CALLBACK_REGISTRY.get(init["callback_key"], None) or MOCK_HUB_REGISTRY.get( + callback_path = CALLBACK_REGISTRY.get(init["callback_key"], None) or MOCK_REGISTRY.get( init["callback_key"], None ) assert callback_path, MisconfigurationException( @@ -222,31 +218,24 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, # %% # load the pl extension module we want to use. This will import all necessary callbacks. -module_hub_mock("finetuningscheduler") +mock_register_module("finetuningscheduler") # set notebook-level variables -AVAIL_GPUS = min(1, torch.cuda.device_count()) +AVAIL_GPUS = torch.cuda.device_count() TASK_NUM_LABELS = {"boolq": 2, "rte": 2} DEFAULT_TASK = "rte" -# narrow our logging to adapt it for a notebook environment -for l_key in logging.Logger.manager.loggerDict.keys(): - if "pytorch_lightning" in l_key: - logging.getLogger(l_key).setLevel("INFO") - else: - logging.getLogger(l_key).setLevel("CRITICAL") -pl_logger = logging.getLogger("pytorch_lightning") -pl_logger.removeHandler(pl_logger.handlers[0]) -rz_logger = logging.getLogger("pytorch_lightning.utilities.distributed") -rz_logger.addHandler(logging.StreamHandler()) -rz_logger.handlers[0].setLevel("INFO") +# ignore warnings related tokenizers_parallelism/DataLoader parallelism tradeoff and +# expected logging behavior +for warnf in [".*does not have many workers*", ".*The number of training samples.*"]: + warnings.filterwarnings("ignore", warnf) # %% class RteBoolqDataModule(pl.LightningDataModule): """A ``LightningDataModule`` for using either the RTE or BoolQ SuperGLUE Hugging Face datasets.""" - task_text_field_map = {"rte": ["premise", "hypothesis"], "boolq": ["question", "passage"]} - loader_columns = [ + TASK_TEXT_FIELD_MAP = {"rte": ("premise", "hypothesis"), "boolq": ("question", "passage")} + LOADER_COLUMNS = ( "datasets_idx", "input_ids", "token_type_ids", @@ -254,17 +243,12 @@ class RteBoolqDataModule(pl.LightningDataModule): "start_positions", "end_positions", "labels", - ] - # ignore warnings related tokenizers_parallelism/DataLoader parallelism tradeoff and - # expected logging behavior - for warnf in [".*does not have many workers*", ".*The number of training samples.*"]: - warnings.filterwarnings("ignore", warnf) + ) def __init__( self, model_name_or_path: str, task_name: str = DEFAULT_TASK, - prep_on_init: bool = False, max_seq_length: int = 128, train_batch_size: int = 32, eval_batch_size: int = 32, @@ -278,23 +262,22 @@ def __init__( self.train_batch_size = train_batch_size self.eval_batch_size = eval_batch_size self.tokenizers_parallelism = tokenizers_parallelism - self.dataloader_kwargs = {"num_workers": num_workers, "pin_memory": pin_memory} - self.text_fields = self.task_text_field_map[self.task_name] + self.dataloader_kwargs = { + "num_workers": dataloader_kwargs.get("num_workers", 0), + "pin_memory": dataloader_kwargs.get("pin_memory", False), + } + self.text_fields = self.TASK_TEXT_FIELD_MAP[self.task_name] self.num_labels = TASK_NUM_LABELS[self.task_name] os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.tokenizers_parallelism else "false" self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True, local_files_only=False) - if prep_on_init: # useful if one wants to load datasets as soon as the ``LightningDataModule`` is - # instantiated - self.prepare_data() - self.setup("fit") def setup(self, stage): self.dataset = datasets.load_dataset("super_glue", self.task_name) for split in self.dataset.keys(): self.dataset[split] = self.dataset[split].map( - self.convert_to_features, batched=True, remove_columns=["label"] + self._convert_to_features, batched=True, remove_columns=["label"] ) - self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] + self.columns = [c for c in self.dataset[split].column_names if c in self.LOADER_COLUMNS] self.dataset[split].set_format(type="torch", columns=self.columns) self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] @@ -329,7 +312,7 @@ def test_dataloader(self): for x in self.eval_splits ] - def convert_to_features(self, example_batch): + def _convert_to_features(self, example_batch): text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) # Tokenize the text/text pairs features = self.tokenizer.batch_encode_plus( @@ -412,15 +395,15 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): preds = logits.squeeze() labels = batch["labels"] + self.log("val_loss", val_loss, prog_bar=True) return {"loss": val_loss, "preds": preds, "labels": labels} def validation_epoch_end(self, outputs): preds = torch.cat([x["preds"] for x in outputs]).detach().cpu().numpy() labels = torch.cat([x["labels"] for x in outputs]).detach().cpu().numpy() loss = torch.stack([x["loss"] for x in outputs]).mean() - self.log("val_loss", loss, prog_bar=True, sync_dist=True) metric_dict = self.metric.compute(predictions=preds, references=labels) - self.log_dict(metric_dict, prog_bar=True, sync_dist=True) + self.log_dict(metric_dict, prog_bar=True) return loss def _init_param_groups(self) -> List[Dict]: @@ -455,7 +438,7 @@ def configure_optimizers(self): # but in this case we pass a list of parameter groups to ensure weight_decay is # not applied to the bias parameter (for completeness, in this case it won't make much # performance difference) - optimizer = instantiate_registered_class(args=self.init_pgs(), init=self.optimizer_init) + optimizer = instantiate_registered_class(args=self._init_param_groups(), init=self.optimizer_init) scheduler = { "scheduler": instantiate_registered_class(args=optimizer, init=self.lr_scheduler_init), **self.pl_lrs_cfg, @@ -485,6 +468,8 @@ def configure_callbacks(self): - model.albert.encoder.*.ffn_output.* """ ft_schedule_name = "RteBoolqModule_ft_schedule_albert_base.yaml" +# Let's write the schedule to a file so we can simulate loading an explicitly defined finetuning +# schedule. with open(ft_schedule_name, "w") as f: f.write(ft_schedule_yaml) @@ -492,17 +477,52 @@ def configure_callbacks(self): datasets.set_progress_bar_enabled(False) pl.seed_everything(42) dm = RteBoolqDataModule(model_name_or_path="albert-base-v2", tokenizers_parallelism=False) -dm.setup("fit") + +# %% [markdown] +# ### Optimizer Configuration +# +#
    +# +# Though other optimizers can arguably yield some marginal advantage contingent on the context, +# the Adam optimizer (and the [AdamW version](https://pytorch.org/docs/stable/_modules/torch/optim/adamw.html#AdamW) which +# implements decoupled weight decay) remains robust to hyperparameter choices and is commonly used for finetuning +# foundational language models. See [(Sivaprasad et al., 2020)](#f2) and [(Mosbach, Andriushchenko & Klakow, 2020)](#f3) for theoretical and systematic empirical justifications of Adam and its use in finetuning +# large transformer-based language models. The values used here have some justification +# in the referenced literature but have been largely empirically determined and while a good +# starting point could be could be further tuned. +# +#
    + +# %% optimizer_init = { "class_path": "torch.optim.AdamW", "init_args": {"weight_decay": 1e-05, "eps": 1e-07, "lr": 1e-05}, } + +# %% [markdown] +# ### LR Scheduler Configuration +# +#
    +# +# The [CosineAnnealingWarmRestarts scheduler](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html?highlight=cosineannealingwarm#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts) nicely fits with our iterative finetuning since it does not depend upon a global max_epoch +# value. The importance of initial warmup is reduced due to the innate warmup effect of Adam bias correction [[5]](#f3) +# and the gradual thawing we are performing. Note that commonly used LR schedulers that depend on providing +# max_iterations/epochs (e.g. the +# [CosineWarmupScheduler](https://github.com/PyTorchLightning/lightning-tutorials/blob/0c325829101d5a6ebf32ed99bbf5b09badf04a59/course_UvA-DL/05-transformers-and-MH-attention/Transformers_MHAttention.py#L688) +# used in other pytorch-lightning tutorials) also work with FinetuningScheduler. Though the LR scheduler is theoretically +# justified [(Loshchilov & Hutter, 2016)](#f4), the particular values provided here are primarily empircally driven. +# +#
    + + +# %% lr_scheduler_init = { "class_path": "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts", "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, } pl_lrs_cfg = {"interval": "epoch", "frequency": 1, "name": "CosineAnnealingWarmRestarts"} +# %% model = RteBoolqModule( model_name_or_path="albert-base-v2", optimizer_init=optimizer_init, @@ -528,15 +548,23 @@ def configure_callbacks(self): enable_progress_bar = False # %% -trainer = pl.Trainer( - enable_progress_bar=enable_progress_bar, - precision=16, - accelerator="auto", - devices="auto", - callbacks=callbacks, - logger=logger, -) -trainer.fit(model, datamodule=dm) +def train() -> None: + trainer = pl.Trainer( + enable_progress_bar=enable_progress_bar, + precision=16, + gpus=1, + # accelerator="auto", + # devices="auto", + callbacks=callbacks, + logger=logger, + ) + trainer.fit(model, datamodule=dm) + + +if AVAIL_GPUS > 0: + train() +else: + print("Given the multiple phases of finetuning demonstrated, this notebook is best used with a GPU") # %% [markdown] # ## Footnotes @@ -560,5 +588,25 @@ def configure_callbacks(self): # [Peters, M. E., Ruder, S., & Smith, N. A. (2019)](https://arxiv.org/pdf/1903.05987.pdf). To tune or not to # tune? adapting pretrained representations to diverse tasks. arXiv preprint arXiv:1903.05987. [↩](#a1) # -# -# +# +#
  4. +# +# [Sivaprasad, P. T., Mai, F., Vogels, T., Jaggi, M., & Fleuret, F. (2020)](https://arxiv.org/pdf/1910.11758.pdf). +# Optimizer benchmarking needs to account for hyperparameter tuning. In International Conference on Machine Learning +# (pp. 9036-9045). PMLR. [↩](#a2) +# +#
    5. +#
  5. +# +# [Mosbach, M., Andriushchenko, M., & Klakow, D. (2020)](https://arxiv.org/pdf/2006.04884.pdf). On the stability of +# fine-tuning bert: Misconceptions, explanations, and strong baselines. arXiv preprint arXiv:2006.04884. [↩](#a2) +# +#
    6. +#
  6. +# +# [Loshchilov, I., & Hutter, F. (2016)](https://arxiv.org/pdf/1608.03983.pdf). Sgdr: Stochastic gradient descent with +# warm restarts. arXiv preprint arXiv:1608.03983. [↩](#a3) +# +#
    7. +# +#
From d1429fa103c48f85bf2b111d222c73342401cb2d Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Sat, 4 Dec 2021 14:34:37 -0800 Subject: [PATCH 18/44] misc tutorial enhancements --- .../finetuning-scheduler.py | 22 +++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 52a10fb23..37729e295 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,3 +1,15 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: -all +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.13.2 +# --- + # %% [markdown] # ## Scheduled Finetuning # @@ -396,6 +408,7 @@ def validation_step(self, batch, batch_idx, dataloader_idx=0): labels = batch["labels"] self.log("val_loss", val_loss, prog_bar=True) + return {"loss": val_loss, "preds": preds, "labels": labels} def validation_epoch_end(self, outputs): @@ -518,7 +531,8 @@ def configure_callbacks(self): # %% lr_scheduler_init = { "class_path": "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts", - "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, + # "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, + "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 0}, } pl_lrs_cfg = {"interval": "epoch", "frequency": 1, "name": "CosineAnnealingWarmRestarts"} @@ -551,10 +565,10 @@ def configure_callbacks(self): def train() -> None: trainer = pl.Trainer( enable_progress_bar=enable_progress_bar, + # max_epochs=1, precision=16, - gpus=1, - # accelerator="auto", - # devices="auto", + accelerator="gpu", + devices=1, callbacks=callbacks, logger=logger, ) From 3fc3887b990c509ac0e4a082eb8cd53c19fbac7d Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Wed, 8 Dec 2021 12:33:03 -0800 Subject: [PATCH 19/44] numerous expository enhancements and switch to DeBERTav3 --- .../finetuning-scheduler/.meta.yml | 1 + ...eBoolqModule_ft_schedule_deberta_base.yaml | 18 ++ .../finetuning-scheduler.py | 233 +++++++++++++----- .../fts_explicit_accuracy.png | Bin 0 -> 24409 bytes .../implicit_training_transition.png | Bin 0 -> 22919 bytes .../nofts_baseline_accuracy.png | Bin 0 -> 24750 bytes 6 files changed, 197 insertions(+), 55 deletions(-) create mode 100644 lightning_examples/finetuning-scheduler/RteBoolqModule_ft_schedule_deberta_base.yaml create mode 100644 lightning_examples/finetuning-scheduler/fts_explicit_accuracy.png create mode 100644 lightning_examples/finetuning-scheduler/implicit_training_transition.png create mode 100644 lightning_examples/finetuning-scheduler/nofts_baseline_accuracy.png diff --git a/lightning_examples/finetuning-scheduler/.meta.yml b/lightning_examples/finetuning-scheduler/.meta.yml index 3b63b73e5..6954f680c 100644 --- a/lightning_examples/finetuning-scheduler/.meta.yml +++ b/lightning_examples/finetuning-scheduler/.meta.yml @@ -16,5 +16,6 @@ requirements: - transformers - datasets - scikit-learn + - sentencepiece accelerator: - GPU diff --git a/lightning_examples/finetuning-scheduler/RteBoolqModule_ft_schedule_deberta_base.yaml b/lightning_examples/finetuning-scheduler/RteBoolqModule_ft_schedule_deberta_base.yaml new file mode 100644 index 000000000..62bdbae28 --- /dev/null +++ b/lightning_examples/finetuning-scheduler/RteBoolqModule_ft_schedule_deberta_base.yaml @@ -0,0 +1,18 @@ + +0: + params: + - model.classifier.bias + - model.classifier.weight + - model.pooler.dense.bias + - model.pooler.dense.weight + - model.deberta.encoder.LayerNorm.bias + - model.deberta.encoder.LayerNorm.weight + - model.deberta.encoder.rel_embeddings.weight + - model.deberta.encoder.layer.{0,11}.(output|attention|intermediate).* +1: + params: + - model.deberta.embeddings.LayerNorm.bias + - model.deberta.embeddings.LayerNorm.weight +2: + params: + - model.deberta.embeddings.word_embeddings.weight diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 37729e295..968395d0c 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -1,25 +1,21 @@ -# --- -# jupyter: -# jupytext: -# cell_metadata_filter: -all -# formats: ipynb,py:percent -# text_representation: -# extension: .py -# format_name: percent -# format_version: '1.3' -# jupytext_version: 1.13.2 -# --- - # %% [markdown] # ## Scheduled Finetuning # +# Training with the ``FinetuningScheduler`` callback confers a host of benefits. It was created to meet the following +# objectives in roughly descending order of priority: +# +# - maximize finetuning flexibility +# - expedite and facilitate exploration of model tuning dynamics in research +# - allow marginal performance improvements of finetuned models +# #
# -# The ``FinetuningScheduler`` callback enables multi-phase, -# scheduled finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize +# Fundamentally, the ``FinetuningScheduler`` callback enables scheduled, multi-phase, +# finetuning of foundational models. Gradual unfreezing (i.e. thawing) can help maximize # foundational model knowledge retention while allowing (typically upper layers of) the model to # optimally adapt to new tasks during transfer learning [1, 2, 3](#f1) # +# #
# # ``FinetuningScheduler`` orchestrates the gradual unfreezing @@ -34,11 +30,16 @@ # ![FinetuningScheduler explicit loss animation](fts_explicit_loss_anim.gif) # %% [markdown] +# # ## Basic Usage # +#
+# # If no finetuning schedule is provided by the user, ``FinetuningScheduler`` will generate a # [default schedule](#The-Default-Finetuning-Schedule) and proceed to finetune according to the generated schedule, using default ``FTSEarlyStopping`` and ``FTSCheckpoint`` callbacks with ``monitor=val_loss``. # +#
+# # ```python # from pytorch_lightning import Trainer # from pytorch_lightning.callbacks.finetuning_scheduler import FinetuningScheduler @@ -49,7 +50,7 @@ # ## The Default Finetuning Schedule # # Schedule definition is facilitated via the ``gen_ft_schedule`` method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as -# desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will often be less computationally efficient than a user-defined finetuning schedule but can often serve as a good baseline for subsquent explicit schedule refinement and will marginally outperform many explicit schedules. +# desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will likely be less computationally efficient than a user-defined finetuning schedule but is useful for exploring a model's finetuning behavior and can serve as a good baseline for subsquent explicit schedule refinement. # %% [markdown] # ## Specifying a Finetuning Schedule @@ -147,8 +148,6 @@ # # The following example demonstrates the use of ``FinetuningScheduler`` to finetune a small foundational model on the [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of [SuperGLUE](https://super.gluebenchmark.com/). Iterative early-stopping will be applied according to a user-specified schedule. # -# ``FinetuningScheduler`` can be used to achieve non-trivial model performance improvements in both implicit and explicit scheduling contexts at an also non-trivial computational cost. -# # %% import os @@ -166,6 +165,7 @@ from pytorch_lightning.utilities.exceptions import MisconfigurationException from torch.utils.data import DataLoader from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer +from transformers import logging as transformers_logging # %% # a couple helper functions to prepare code to work with a user module registry @@ -200,12 +200,14 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, Returns: The instantiated class object. """ + class_module, class_name, args_class = None, None, None + shortcircuit_local = False kwargs = init.get("init_args", {}) + class_path = init.get("class_path", None) if args and not isinstance(args, tuple): args = (args,) - shortcircuit_local = False if "." in init["class_path"] else True - class_module, class_name, args_class = None, None, None - if init.get("class_path", None): + if class_path: + shortcircuit_local = False if "." in class_path else True if not shortcircuit_local: class_module, class_name = init["class_path"].rsplit(".", 1) else: # class is expected to be locally defined @@ -236,6 +238,7 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, TASK_NUM_LABELS = {"boolq": 2, "rte": 2} DEFAULT_TASK = "rte" +transformers_logging.set_verbosity_error() # ignore warnings related tokenizers_parallelism/DataLoader parallelism tradeoff and # expected logging behavior for warnf in [".*does not have many workers*", ".*The number of training samples.*"]: @@ -262,8 +265,8 @@ def __init__( model_name_or_path: str, task_name: str = DEFAULT_TASK, max_seq_length: int = 128, - train_batch_size: int = 32, - eval_batch_size: int = 32, + train_batch_size: int = 16, + eval_batch_size: int = 16, tokenizers_parallelism: bool = True, **dataloader_kwargs: Any, ): @@ -373,12 +376,21 @@ def __init__( self.task_name = DEFAULT_TASK rank_zero_warn(f"Invalid task_name '{task_name}'. Proceeding with the default task: '{DEFAULT_TASK}'") self.num_labels = TASK_NUM_LABELS[self.task_name] - self.save_hyperparameters() self.experiment_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{experiment_tag}" self.model_cfg = model_cfg or {} conf = AutoConfig.from_pretrained(model_name_or_path, num_labels=self.num_labels, local_files_only=False) self.model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, config=conf) self.model.config.update(self.model_cfg) # apply model config overrides + self.init_hparams = { + "optimizer": self.optimizer_init, + "lr_scheduler": self.lr_scheduler_init, + "pl_lrs_cfg": self.pl_lrs_cfg, + "model_config": self.model.config, + "model_name_or_path": model_name_or_path, + "task_name": self.task_name, + "experiment_id": self.experiment_id, + } + self.save_hyperparameters(self.init_hparams) self.metric = datasets.load_metric("super_glue", self.task_name, experiment_id=self.experiment_id) self.no_decay = ["bias", "LayerNorm.weight"] self.finetuningscheduler_callback = None @@ -400,24 +412,14 @@ def training_epoch_end(self, outputs: List[Any]) -> None: def validation_step(self, batch, batch_idx, dataloader_idx=0): outputs = self(**batch) val_loss, logits = outputs[:2] - if self.num_labels >= 1: preds = torch.argmax(logits, axis=1) elif self.num_labels == 1: preds = logits.squeeze() - labels = batch["labels"] self.log("val_loss", val_loss, prog_bar=True) - - return {"loss": val_loss, "preds": preds, "labels": labels} - - def validation_epoch_end(self, outputs): - preds = torch.cat([x["preds"] for x in outputs]).detach().cpu().numpy() - labels = torch.cat([x["labels"] for x in outputs]).detach().cpu().numpy() - loss = torch.stack([x["loss"] for x in outputs]).mean() metric_dict = self.metric.compute(predictions=preds, references=labels) self.log_dict(metric_dict, prog_bar=True) - return loss def _init_param_groups(self) -> List[Dict]: """Initialize the parameter groups. Used to ensure weight_decay is not applied to our specified bias @@ -465,22 +467,46 @@ def configure_callbacks(self): return super().configure_callbacks() +# %% [markdown] +# ### Our Training Sessions +# +# We'll be comparing three different finetuning training configurations. Every configuration in this example depends +# upon a shared set of defaults, only differing in their respective finetuning schedules. +# +# | Experiment Tag | Training Scenario Description | +# |:-----------------:| ---------------------------------------------------------------------- | +# | ``fts_explicit`` | Training with a finetuning schedule explicitly provided by the user | +# | ``nofts_baseline``| A baseline finetuning training session (without scheduled finetuning) | +# | ``fts_implicit`` | Training with an implicitly generated finetuning schedule (the default)| +# +# Let's begin by configuring the ``fts_explicit`` scenario. We'll subsequently run the other two scenarios for +# comparison. + # %% # Let's create a finetuning schedule for our model and run an explicitly scheduled finetuning training scenario with it -# Please see the documentation for a full description of the schedule format +# Please see the ``FinetuningScheduler`` documentation for a full description of the schedule format + + ft_schedule_yaml = """ 0: - params: # the parameters for each phase definition can be fully specified + params: - model.classifier.bias - model.classifier.weight + - model.pooler.dense.bias + - model.pooler.dense.weight + - model.deberta.encoder.LayerNorm.bias + - model.deberta.encoder.LayerNorm.weight + - model.deberta.encoder.rel_embeddings.weight + - model.deberta.encoder.layer.{0,11}.(output|attention|intermediate).* 1: - params: # or specified via a regex - - model.albert.pooler.* + params: + - model.deberta.embeddings.LayerNorm.bias + - model.deberta.embeddings.LayerNorm.weight 2: params: - - model.albert.encoder.*.ffn_output.* + - model.deberta.embeddings.word_embeddings.weight """ -ft_schedule_name = "RteBoolqModule_ft_schedule_albert_base.yaml" +ft_schedule_name = "RteBoolqModule_ft_schedule_deberta_base.yaml" # Let's write the schedule to a file so we can simulate loading an explicitly defined finetuning # schedule. with open(ft_schedule_name, "w") as f: @@ -489,7 +515,7 @@ def configure_callbacks(self): # %% datasets.set_progress_bar_enabled(False) pl.seed_everything(42) -dm = RteBoolqDataModule(model_name_or_path="albert-base-v2", tokenizers_parallelism=False) +dm = RteBoolqDataModule(model_name_or_path="microsoft/deberta-v3-base", tokenizers_parallelism=False) # %% [markdown] # ### Optimizer Configuration @@ -531,28 +557,40 @@ def configure_callbacks(self): # %% lr_scheduler_init = { "class_path": "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts", - # "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, - "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 0}, + "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, } pl_lrs_cfg = {"interval": "epoch", "frequency": 1, "name": "CosineAnnealingWarmRestarts"} # %% -model = RteBoolqModule( - model_name_or_path="albert-base-v2", - optimizer_init=optimizer_init, - lr_scheduler_init=lr_scheduler_init, - pl_lrs_cfg=pl_lrs_cfg, - experiment_tag="fts_explicit", -) +# Load our lightning module... +lightning_module_kwargs = { + "model_name_or_path": "microsoft/deberta-v3-base", + "optimizer_init": optimizer_init, + "lr_scheduler_init": lr_scheduler_init, + "pl_lrs_cfg": pl_lrs_cfg, +} +model = RteBoolqModule(**lightning_module_kwargs, experiment_tag="fts_explicit") + +# %% [markdown] +# ### Callback Configuration +# +# The only callback required to invoke the ``FinetuningScheduler`` is the ``FinetuningScheduler`` callback itself. +# Default versions of ``FTSCheckpoint`` and ``FTSEarlyStopping`` (if not specifying ``epoch_only_transitions``) will be +# included ([as discussed above](#basic_usage)) if not provided in the callbacks list. For demonstration purposes I'm +# including example configurations of all three callbacks below. + +# %% +earlystopping_kwargs = {"monitor": "val_loss", "min_delta": 0.001, "patience": 2} +checkpoint_kwargs = {"monitor": "val_loss", "save_top_k": 1} +fts_kwargs = {"max_depth": 1} callbacks = [ - FinetuningScheduler(ft_schedule=ft_schedule_name, max_depth=2), # type: ignore # noqa - FTSEarlyStopping(monitor="val_loss", min_delta=0.001, patience=2), # type: ignore # noqa - FTSCheckpoint(monitor="val_loss", save_top_k=5), # type: ignore # noqa + FinetuningScheduler(ft_schedule=ft_schedule_name, **fts_kwargs), # type: ignore # noqa + FTSEarlyStopping(**earlystopping_kwargs), # type: ignore # noqa + FTSCheckpoint(**checkpoint_kwargs), # type: ignore # noqa ] +# %% example_logdir = "lightning_logs" logger = TensorBoardLogger(example_logdir, name="fts_explicit") - -# %% # optionally start tensorboard and monitor progress graphically while viewing multi-phase finetuning specific training # logs in the cell output below by uncommenting the next 3 lines # # !mkdir -p $example_logdir @@ -565,7 +603,7 @@ def configure_callbacks(self): def train() -> None: trainer = pl.Trainer( enable_progress_bar=enable_progress_bar, - # max_epochs=1, + max_epochs=100, precision=16, accelerator="gpu", devices=1, @@ -580,6 +618,91 @@ def train() -> None: else: print("Given the multiple phases of finetuning demonstrated, this notebook is best used with a GPU") +# %% [markdown] +# ### Running the Baseline and Implicit Finetuning Scenarios +# +# Let's now compare our ``nofts_baseline`` and ``fts_implicit`` scenarios with the ``fts_explicit`` one we just ran. +# We'll need to update our callbacks list, using the core PL ``EarlyStopping`` and ``ModelCheckpoint`` callbacks for the +# ``nofts_baseline`` (which operate identically to their FTS analogs apart from the recursive training support). +# For both core PyTorch Lightning and user-registered callbacks, we can define our callbacks using a dictionary as we do +# with the LightningCLI. This allows us to avoid managing imports and support more complex configuration separated from +# code. +# We'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping ``max_depth`` for the +# implicit schedule will limit finetuning to just the last 4 parameters of the model, which is only a small fraction +# of the parameters you'd want to tune for maximum performance. Since the implicit schedule is quite computationally +# intensive and most useful for exploring model behavior, leaving ``max_depth`` 1 allows us to demo implicit mode +# behavior while keeping the computational cost and runtime of this notebook reasonable. To review how a full implicit +# mode run compares to the ``nofts_baseline`` and ``fts_explicit`` scenarios, please see the the following +# [tensorboard experiment summary](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/). + + +# %% + +# reference the core callbacks using the relevant callback_key +nofts_callback_cfg = [ + {"callback_key": "EarlyStopping", "init_args": earlystopping_kwargs}, + {"callback_key": "ModelCheckpoint", "init_args": checkpoint_kwargs}, +] +# we can use class_paths, class names or fully qualified names for referencing user-registered callbacks as well +fts_implicit_cfg = [ + {"callback_key": "FinetuningScheduler", "init_args": fts_kwargs}, # use the callback_key + {"class_path": "FTSEarlyStopping", "init_args": earlystopping_kwargs}, # just the class_name + # or the fully qualified name + { + "class_path": "pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSCheckpoint", + "init_args": checkpoint_kwargs, + }, +] +nofts_callbacks = [instantiate_registered_class(c) for c in nofts_callback_cfg] +fts_implicit_callbacks = [instantiate_registered_class(c) for c in fts_implicit_cfg] +scenario_callbacks = {"nofts_baseline": nofts_callbacks, "fts_implicit": fts_implicit_callbacks} + +# %% +if AVAIL_GPUS > 0: + for scenario_name, scenario_callbacks in scenario_callbacks.items(): + model = RteBoolqModule(**lightning_module_kwargs, experiment_tag=scenario_name) + logger = TensorBoardLogger(example_logdir, name=scenario_name) + callbacks = scenario_callbacks + print(f"Beginning training the '{scenario_name}' scenario") + train() +else: + print("Given the multiple phases of finetuning demonstrated, this notebook is best used with a GPU") + +# %% [markdown] +# ### Reviewing the Training Results +# +# See the [tensorboard experiment summaries](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/) to get a sense +# of the relative computational and performance tradeoffs associated with these ``FinetuningScheduler`` configurations. +# The summary compares a full ``fts_implicit`` execution to ``fts_explicit`` and ``nofts_baseline`` scenarios using DDP +# training with 2 GPUs. The full logs/schedules for all three scenarios are available +# [here](https://drive.google.com/file/d/1LrUcisRLHeJgh_BDOOD_GUBPp5iHAkoR/view?usp=sharing) and the checkpoints +# produced in the scenarios [here](https://drive.google.com/file/d/1t7myBgcqcZ9ax_IT9QVk-vFH_l_o5UXB/view?usp=sharing) +# (caution, ~3.5GB). +# +# [![fts_explicit_accuracy](fts_explicit_accuracy.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOnRydWUsIm5vZnRzX2Jhc2VsaW5lIjpmYWxzZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D) +# [![nofts_baseline](nofts_baseline_accuracy.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6dHJ1ZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D) +# +# Note there could be around ~1% variation in performance from the tensorboard summaries generated by this notebook +# which uses DDP_SPAWN and 1 GPU. +# +# ``FinetuningScheduler`` expands the space of possible finetuning schedules and the composition of more sophisticated schedules can +# yield mariginal finetuning performance gains. That stated, it should be emphasized the primary utility of ``FinetuningScheduler`` is to grant +# greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTav3's implicit training +# run, a critical tuning transition point is immediately apparent: +# +# [![implicit_training_transition](implicit_training_transition.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6ZmFsc2UsImZ0c19pbXBsaWNpdCI6dHJ1ZX0%3D) +# +# Our val_loss begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our +# schedule, in phase 17 we're beginning tuning the attention parameters of our 10th encoder layer (of 11). Interesting! +# Though beyond the scope of this tutorial, it might be worth investigating these dynamics further and +# ``FinetuningScheduler`` allows one to do just that quite easily. +# +# %% [markdown] +# +# Note that though this example is intended to capture a common usage scenario, substantial variation is expected +# among use cases and models. +# In summary, ``FinetuningScheduler`` provides increased finetuning flexibility that can be useful in a variety of +# contexts from exploring model tuning behavior to maximizing performance. # %% [markdown] # ## Footnotes # diff --git a/lightning_examples/finetuning-scheduler/fts_explicit_accuracy.png b/lightning_examples/finetuning-scheduler/fts_explicit_accuracy.png new file mode 100644 index 0000000000000000000000000000000000000000..b5d8f554a7f7b9b87ed28c016ecfa8d0b1c36deb GIT binary patch literal 24409 zcmb4r1yogS*X<^x1Sthn8VONC1f)|$5U>!XLmH%!lui*45fA|Z5mD*xZjo+~?oJ7* zyEgCp{r|mp+;Q)?e)^4 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ziiwHI9^4l@hq4IZgKx0mMiGQNkvSci7P6>>gqwxTVfgzeV5krjfBy7I&Dhwf*Mh}h zXjIiY61Q#Pqqp^lNnJfU*>uF)8+!)ztHnP!Zz259+KC?$f`8KNH^>*StRe{QK?Pcs z>~m~pW7|Q@<3I0+q<%MlUSzX3CO2&<$jjT}SKEp7MQ-R&9qf6G_2Q2+n{ literal 0 HcmV?d00001 From 8d32874b6b7df39dd9d223c484d776b33b7733cb Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Sat, 11 Dec 2021 13:13:22 -0800 Subject: [PATCH 20/44] minor spelling and stylistic updates --- .../finetuning-scheduler/finetuning-scheduler.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 968395d0c..cb8dc2913 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -133,6 +133,7 @@ #
# # **Note:** Currently, _FinetuningScheduler_ only supports the following ``StrategyType``s: +# # - ``DP`` # - ``DDP`` # - ``DDP_SPAWN`` @@ -600,6 +601,8 @@ def configure_callbacks(self): enable_progress_bar = False # %% + + def train() -> None: trainer = pl.Trainer( enable_progress_bar=enable_progress_bar, @@ -622,13 +625,15 @@ def train() -> None: # ### Running the Baseline and Implicit Finetuning Scenarios # # Let's now compare our ``nofts_baseline`` and ``fts_implicit`` scenarios with the ``fts_explicit`` one we just ran. +# # We'll need to update our callbacks list, using the core PL ``EarlyStopping`` and ``ModelCheckpoint`` callbacks for the # ``nofts_baseline`` (which operate identically to their FTS analogs apart from the recursive training support). # For both core PyTorch Lightning and user-registered callbacks, we can define our callbacks using a dictionary as we do # with the LightningCLI. This allows us to avoid managing imports and support more complex configuration separated from # code. -# We'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping ``max_depth`` for the -# implicit schedule will limit finetuning to just the last 4 parameters of the model, which is only a small fraction +# +# Note that we'll be using identical callback configurations to the ``fts_explicit`` scenario. Keeping ``max_depth`` for +# the implicit schedule will limit finetuning to just the last 4 parameters of the model, which is only a small fraction # of the parameters you'd want to tune for maximum performance. Since the implicit schedule is quite computationally # intensive and most useful for exploring model behavior, leaving ``max_depth`` 1 allows us to demo implicit mode # behavior while keeping the computational cost and runtime of this notebook reasonable. To review how a full implicit @@ -686,8 +691,8 @@ def train() -> None: # which uses DDP_SPAWN and 1 GPU. # # ``FinetuningScheduler`` expands the space of possible finetuning schedules and the composition of more sophisticated schedules can -# yield mariginal finetuning performance gains. That stated, it should be emphasized the primary utility of ``FinetuningScheduler`` is to grant -# greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTav3's implicit training +# yield marginal finetuning performance gains. That stated, it should be emphasized the primary utility of ``FinetuningScheduler`` is to grant +# greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTa-v3's implicit training # run, a critical tuning transition point is immediately apparent: # # [![implicit_training_transition](implicit_training_transition.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6ZmFsc2UsImZ0c19pbXBsaWNpdCI6dHJ1ZX0%3D) From 9722e22dcc688fa8e5bbd1de842df897f0ac98f3 Mon Sep 17 00:00:00 2001 From: Dan Dale Date: Fri, 17 Dec 2021 09:21:22 -0500 Subject: [PATCH 21/44] Apply suggestions from code review a number of good code/documentation cleanup suggestions Co-authored-by: thomas chaton --- .../finetuning-scheduler.py | 39 ++++++++----------- 1 file changed, 17 insertions(+), 22 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index cb8dc2913..46c4c2016 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -232,7 +232,7 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, # %% -# load the pl extension module we want to use. This will import all necessary callbacks. +# Load the `FinetuningScheduler` PyTorch Lightning extension module we want to use. This will import all necessary callbacks. mock_register_module("finetuningscheduler") # set notebook-level variables AVAIL_GPUS = torch.cuda.device_count() @@ -240,15 +240,15 @@ def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, DEFAULT_TASK = "rte" transformers_logging.set_verbosity_error() -# ignore warnings related tokenizers_parallelism/DataLoader parallelism tradeoff and -# expected logging behavior +# ignore warnings related tokenizers_parallelism/DataLoader parallelism trade-off and +# expected logging behavior for warnf in [".*does not have many workers*", ".*The number of training samples.*"]: warnings.filterwarnings("ignore", warnf) # %% class RteBoolqDataModule(pl.LightningDataModule): - """A ``LightningDataModule`` for using either the RTE or BoolQ SuperGLUE Hugging Face datasets.""" + """A ``LightningDataModule`` designed for both the RTE or BoolQ SuperGLUE Hugging Face datasets.""" TASK_TEXT_FIELD_MAP = {"rte": ("premise", "hypothesis"), "boolq": ("question", "passage")} LOADER_COLUMNS = ( @@ -287,6 +287,11 @@ def __init__( os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.tokenizers_parallelism else "false" self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True, local_files_only=False) + def prepare_data(self): + # N.B. PL calls prepare_data from a single process (rank 0) so do not use it to assign + # state (e.g. self.x=y) + datasets.load_dataset("super_glue", self.task_name) + def setup(self, stage): self.dataset = datasets.load_dataset("super_glue", self.task_name) for split in self.dataset.keys(): @@ -298,11 +303,6 @@ def setup(self, stage): self.eval_splits = [x for x in self.dataset.keys() if "validation" in x] - def prepare_data(self): - # N.B. PL calls prepare_data from a single process (rank 0) so do not use it to assign - # state (e.g. self.x=y) - datasets.load_dataset("super_glue", self.task_name) - def train_dataloader(self): return DataLoader(self.dataset["train"], batch_size=self.train_batch_size, **self.dataloader_kwargs) @@ -402,11 +402,10 @@ def forward(self, **inputs): def training_step(self, batch, batch_idx): outputs = self(**batch) loss = outputs[0] + self.log("train_loss", loss, prog_bar=True) return loss def training_epoch_end(self, outputs: List[Any]) -> None: - loss = torch.stack([x["loss"] for x in outputs]).mean() - self.log("train_loss", loss, prog_bar=True) if self.finetuningscheduler_callback: self.log("finetuning_schedule_depth", float(self.finetuningscheduler_callback.curr_depth)) @@ -516,7 +515,7 @@ def configure_callbacks(self): # %% datasets.set_progress_bar_enabled(False) pl.seed_everything(42) -dm = RteBoolqDataModule(model_name_or_path="microsoft/deberta-v3-base", tokenizers_parallelism=False) +dm = RteBoolqDataModule(model_name_or_path="microsoft/deberta-v3-base", tokenizers_parallelism=True) # %% [markdown] # ### Optimizer Configuration @@ -581,22 +580,18 @@ def configure_callbacks(self): # including example configurations of all three callbacks below. # %% -earlystopping_kwargs = {"monitor": "val_loss", "min_delta": 0.001, "patience": 2} -checkpoint_kwargs = {"monitor": "val_loss", "save_top_k": 1} -fts_kwargs = {"max_depth": 1} callbacks = [ - FinetuningScheduler(ft_schedule=ft_schedule_name, **fts_kwargs), # type: ignore # noqa - FTSEarlyStopping(**earlystopping_kwargs), # type: ignore # noqa - FTSCheckpoint(**checkpoint_kwargs), # type: ignore # noqa + FinetuningScheduler(ft_schedule=ft_schedule_name, max_depth=1), # type: ignore # noqa + FTSEarlyStopping(monitor="val_loss", min_delta=0.001, patience=2), # type: ignore # noqa + FTSCheckpoint(monitor="val_loss", save_top_k=1), # type: ignore # noqa ] # %% -example_logdir = "lightning_logs" -logger = TensorBoardLogger(example_logdir, name="fts_explicit") +logger = TensorBoardLogger("lightning_logs", name="fts_explicit") # optionally start tensorboard and monitor progress graphically while viewing multi-phase finetuning specific training # logs in the cell output below by uncommenting the next 3 lines # # !mkdir -p $example_logdir # # %load_ext tensorboard -# # %tensorboard --logdir example_logdir +# # %tensorboard --logdir lightning_logs # disable progress bar by default to focus on multi-phase training logs. Set to True to re-enable if desired enable_progress_bar = False @@ -697,7 +692,7 @@ def train() -> None: # # [![implicit_training_transition](implicit_training_transition.png)](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6ZmFsc2UsImZ0c19pbXBsaWNpdCI6dHJ1ZX0%3D) # -# Our val_loss begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our +# Our `val_loss` begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our # schedule, in phase 17 we're beginning tuning the attention parameters of our 10th encoder layer (of 11). Interesting! # Though beyond the scope of this tutorial, it might be worth investigating these dynamics further and # ``FinetuningScheduler`` allows one to do just that quite easily. From 7dbae67925113aec5976a012c698176f18b523cc Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 17 Dec 2021 14:21:42 +0000 Subject: [PATCH 22/44] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- lightning_examples/finetuning-scheduler/finetuning-scheduler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 46c4c2016..342532c29 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -291,7 +291,7 @@ def prepare_data(self): # N.B. PL calls prepare_data from a single process (rank 0) so do not use it to assign # state (e.g. self.x=y) datasets.load_dataset("super_glue", self.task_name) - + def setup(self, stage): self.dataset = datasets.load_dataset("super_glue", self.task_name) for split in self.dataset.keys(): From 2367ebaf3b2a2957ca48d97b1e830c344e6886ed Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Fri, 17 Dec 2021 09:13:20 -0800 Subject: [PATCH 23/44] removed instantiate_registered_class to simplify example, improved documentation, simplified datamodule loader logic --- .../finetuning-scheduler.py | 169 +++++++----------- .../text-transformers/text-transformers.py | 2 +- 2 files changed, 63 insertions(+), 108 deletions(-) diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index 342532c29..ee8ae15c5 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -155,18 +155,22 @@ import warnings from datetime import datetime from importlib import import_module -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional import datasets import pytorch_lightning as pl import torch +from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.loggers.tensorboard import TensorBoardLogger from pytorch_lightning.utilities import rank_zero_warn -from pytorch_lightning.utilities.cli import CALLBACK_REGISTRY, _Registry +from pytorch_lightning.utilities.cli import _Registry from pytorch_lightning.utilities.exceptions import MisconfigurationException +from torch.optim.adamw import AdamW +from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts from torch.utils.data import DataLoader from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer from transformers import logging as transformers_logging +from transformers.tokenization_utils_base import BatchEncoding # %% # a couple helper functions to prepare code to work with a user module registry @@ -190,47 +194,6 @@ def mock_register_module(key: str, require_fqn: bool = False) -> List: print(f"Imported and registered the following callbacks: {registered_list}") -def instantiate_registered_class(init: Dict[str, Any], args: Optional[Union[Any, Tuple[Any, ...]]] = None) -> Any: - """Instantiates a class with the given args and init. Accepts class definitions in the form of a "class_path" - or "callback_key" associated with a _Registry. - - Args: - init: Dict of the form {"class_path":... or "callback_key":..., "init_args":...}. - args: Positional arguments required for instantiation. - - Returns: - The instantiated class object. - """ - class_module, class_name, args_class = None, None, None - shortcircuit_local = False - kwargs = init.get("init_args", {}) - class_path = init.get("class_path", None) - if args and not isinstance(args, tuple): - args = (args,) - if class_path: - shortcircuit_local = False if "." in class_path else True - if not shortcircuit_local: - class_module, class_name = init["class_path"].rsplit(".", 1) - else: # class is expected to be locally defined - args_class = globals()[init["class_path"]] - elif init.get("callback_key", None): - callback_path = CALLBACK_REGISTRY.get(init["callback_key"], None) or MOCK_REGISTRY.get( - init["callback_key"], None - ) - assert callback_path, MisconfigurationException( - f'specified callback_key {init["callback_key"]} has not been registered' - ) - class_module, class_name = callback_path.__module__, callback_path.__name__ - else: - raise MisconfigurationException( - "Neither a class_path nor callback_key were included in a configuration that" "requires one" - ) - if not shortcircuit_local: - module = __import__(class_module, fromlist=[class_name]) - args_class = getattr(module, class_name) - return args_class(**kwargs) if not args else args_class(*args, **kwargs) - - # %% # Load the `FinetuningScheduler` PyTorch Lightning extension module we want to use. This will import all necessary callbacks. mock_register_module("finetuningscheduler") @@ -271,6 +234,24 @@ def __init__( tokenizers_parallelism: bool = True, **dataloader_kwargs: Any, ): + """Initialize this ``LightningDataModule`` designed for both the RTE or BoolQ SuperGLUE Hugging Face + datasets. + + Args: + model_name_or_path (str): + Can be either: + - A string, the ``model id`` of a pretrained model hosted inside a model repo on huggingface.co. + Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under + a user or organization name, like ``dbmdz/bert-base-german-cased``. + - A path to a ``directory`` containing model weights saved using + :meth:`~transformers.PreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``. + task_name (str, optional): Name of the SuperGLUE task to execute. This module supports 'rte' or 'boolq'. + Defaults to DEFAULT_TASK which is 'rte'. + max_seq_length (int, optional): Length to which we will pad sequences or truncate input. Defaults to 128. + train_batch_size (int, optional): Training batch size. Defaults to 16. + eval_batch_size (int, optional): Batch size to use for validation and testing splits. Defaults to 16. + tokenizers_parallelism (bool, optional): Whether to use parallelism in the tokenizer. Defaults to True. + """ super().__init__() self.model_name_or_path = model_name_or_path self.task_name = task_name if task_name in TASK_NUM_LABELS.keys() else DEFAULT_TASK @@ -288,11 +269,13 @@ def __init__( self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True, local_files_only=False) def prepare_data(self): + """Load the SuperGLUE dataset.""" # N.B. PL calls prepare_data from a single process (rank 0) so do not use it to assign # state (e.g. self.x=y) datasets.load_dataset("super_glue", self.task_name) def setup(self, stage): + """Setup our dataset splits for training/validation/testing.""" self.dataset = datasets.load_dataset("super_glue", self.task_name) for split in self.dataset.keys(): self.dataset[split] = self.dataset[split].map( @@ -307,28 +290,22 @@ def train_dataloader(self): return DataLoader(self.dataset["train"], batch_size=self.train_batch_size, **self.dataloader_kwargs) def val_dataloader(self): - if len(self.eval_splits) == 1: - return DataLoader( - self.dataset["validation"], - batch_size=self.eval_batch_size, - **self.dataloader_kwargs, - ) - elif len(self.eval_splits) > 1: - return [ - DataLoader(self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs) - for x in self.eval_splits - ] + return DataLoader(self.dataset["validation"], batch_size=self.eval_batch_size, **self.dataloader_kwargs) def test_dataloader(self): - if len(self.eval_splits) == 1: - return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size, **self.dataloader_kwargs) - elif len(self.eval_splits) > 1: - return [ - DataLoader(self.dataset[x], batch_size=self.eval_batch_size, **self.dataloader_kwargs) - for x in self.eval_splits - ] - - def _convert_to_features(self, example_batch): + return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size, **self.dataloader_kwargs) + + def _convert_to_features(self, example_batch) -> BatchEncoding: + """Convert raw text examples to a :class:`~transformers.tokenization_utils_base.BatchEncoding` container + (derived from python dict) of features that includes helpful methods for translating between word/character + space and token space. + + Args: + example_batch ([type]): The set of examples to convert to token space. + + Returns: + ``BatchEncoding``: A batch of encoded examples (note default tokenizer batch_size=1000) + """ text_pairs = list(zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]])) # Tokenize the text/text pairs features = self.tokenizer.batch_encode_plus( @@ -349,7 +326,6 @@ def __init__( model_name_or_path: str, optimizer_init: Dict[str, Any], lr_scheduler_init: Dict[str, Any], - pl_lrs_cfg: Optional[Dict[str, Any]] = None, model_cfg: Optional[Dict[str, Any]] = None, task_name: str = DEFAULT_TASK, experiment_tag: str = "default", @@ -359,8 +335,6 @@ def __init__( model_name_or_path (str): Path to pretrained model or identifier from https://huggingface.co/models optimizer_init (Dict[str, Any]): The desired optimizer configuration. lr_scheduler_init (Dict[str, Any]): The desired learning rate scheduler config - pl_lrs_cfg (Optional[Dict[str, Any]]): Defines custom overrides of pytorch lightning lr_scheduler defaults - defined in ``_get_default_scheduler_config`` model_cfg (Optional[Dict[str, Any]], optional): Defines overrides of the default model config. Defaults to ``None``. task_name (str, optional): The SuperGLUE task to execute, one of ``'rte'``, ``'boolq'``. Defaults to "rte". @@ -370,7 +344,6 @@ def __init__( super().__init__() self.optimizer_init = optimizer_init self.lr_scheduler_init = lr_scheduler_init - self.pl_lrs_cfg = pl_lrs_cfg or {} if task_name in TASK_NUM_LABELS.keys(): self.task_name = task_name else: @@ -385,7 +358,6 @@ def __init__( self.init_hparams = { "optimizer": self.optimizer_init, "lr_scheduler": self.lr_scheduler_init, - "pl_lrs_cfg": self.pl_lrs_cfg, "model_config": self.model.config, "model_name_or_path": model_name_or_path, "task_name": self.task_name, @@ -435,7 +407,7 @@ def _init_param_groups(self) -> List[Dict]: for n, p in self.model.named_parameters() if not any(nd in n for nd in self.no_decay) and p.requires_grad ], - "weight_decay": self.optimizer_init["init_args"]["weight_decay"], + "weight_decay": self.optimizer_init["weight_decay"], }, { "params": [ @@ -453,11 +425,8 @@ def configure_optimizers(self): # but in this case we pass a list of parameter groups to ensure weight_decay is # not applied to the bias parameter (for completeness, in this case it won't make much # performance difference) - optimizer = instantiate_registered_class(args=self._init_param_groups(), init=self.optimizer_init) - scheduler = { - "scheduler": instantiate_registered_class(args=optimizer, init=self.lr_scheduler_init), - **self.pl_lrs_cfg, - } + optimizer = AdamW(params=self._init_param_groups(), **self.optimizer_init) + scheduler = {"scheduler": CosineAnnealingWarmRestarts(optimizer, **self.lr_scheduler_init)} return [optimizer], [scheduler] def configure_callbacks(self): @@ -533,10 +502,7 @@ def configure_callbacks(self): #
# %% -optimizer_init = { - "class_path": "torch.optim.AdamW", - "init_args": {"weight_decay": 1e-05, "eps": 1e-07, "lr": 1e-05}, -} +optimizer_init = {"weight_decay": 1e-05, "eps": 1e-07, "lr": 1e-05} # %% [markdown] # ### LR Scheduler Configuration @@ -555,11 +521,7 @@ def configure_callbacks(self): # %% -lr_scheduler_init = { - "class_path": "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts", - "init_args": {"T_0": 1, "T_mult": 2, "eta_min": 1e-07}, -} -pl_lrs_cfg = {"interval": "epoch", "frequency": 1, "name": "CosineAnnealingWarmRestarts"} +lr_scheduler_init = {"T_0": 1, "T_mult": 2, "eta_min": 1e-07} # %% # Load our lightning module... @@ -567,7 +529,6 @@ def configure_callbacks(self): "model_name_or_path": "microsoft/deberta-v3-base", "optimizer_init": optimizer_init, "lr_scheduler_init": lr_scheduler_init, - "pl_lrs_cfg": pl_lrs_cfg, } model = RteBoolqModule(**lightning_module_kwargs, experiment_tag="fts_explicit") @@ -580,16 +541,22 @@ def configure_callbacks(self): # including example configurations of all three callbacks below. # %% +# let's save our callback configurations for the explicit scenario since we'll be reusing the same +# configurations for the implicit and nofts_baseline scenarios (except the config for the +# FinetuningScheduler callback itself of course in the case of nofts_baseline) +earlystopping_kwargs = {"monitor": "val_loss", "min_delta": 0.001, "patience": 2} +checkpoint_kwargs = {"monitor": "val_loss", "save_top_k": 1} +fts_kwargs = {"max_depth": 1} callbacks = [ - FinetuningScheduler(ft_schedule=ft_schedule_name, max_depth=1), # type: ignore # noqa - FTSEarlyStopping(monitor="val_loss", min_delta=0.001, patience=2), # type: ignore # noqa - FTSCheckpoint(monitor="val_loss", save_top_k=1), # type: ignore # noqa + FinetuningScheduler(ft_schedule=ft_schedule_name, **fts_kwargs), # type: ignore # noqa + FTSEarlyStopping(**earlystopping_kwargs), # type: ignore # noqa + FTSCheckpoint(**checkpoint_kwargs), # type: ignore # noqa ] + # %% logger = TensorBoardLogger("lightning_logs", name="fts_explicit") # optionally start tensorboard and monitor progress graphically while viewing multi-phase finetuning specific training -# logs in the cell output below by uncommenting the next 3 lines -# # !mkdir -p $example_logdir +# logs in the cell output below by uncommenting the next 2 lines # # %load_ext tensorboard # # %tensorboard --logdir lightning_logs # disable progress bar by default to focus on multi-phase training logs. Set to True to re-enable if desired @@ -637,31 +604,19 @@ def train() -> None: # %% - -# reference the core callbacks using the relevant callback_key -nofts_callback_cfg = [ - {"callback_key": "EarlyStopping", "init_args": earlystopping_kwargs}, - {"callback_key": "ModelCheckpoint", "init_args": checkpoint_kwargs}, -] -# we can use class_paths, class names or fully qualified names for referencing user-registered callbacks as well -fts_implicit_cfg = [ - {"callback_key": "FinetuningScheduler", "init_args": fts_kwargs}, # use the callback_key - {"class_path": "FTSEarlyStopping", "init_args": earlystopping_kwargs}, # just the class_name - # or the fully qualified name - { - "class_path": "pytorch_lightning.callbacks.finetuning_scheduler.fts_supporters.FTSCheckpoint", - "init_args": checkpoint_kwargs, - }, +nofts_callbacks = [EarlyStopping(**earlystopping_kwargs), ModelCheckpoint(**checkpoint_kwargs)] +fts_implicit_callbacks = [ + FinetuningScheduler(**fts_kwargs), # type: ignore # noqa + FTSEarlyStopping(**earlystopping_kwargs), # type: ignore # noqa + FTSCheckpoint(**checkpoint_kwargs), # type: ignore # noqa ] -nofts_callbacks = [instantiate_registered_class(c) for c in nofts_callback_cfg] -fts_implicit_callbacks = [instantiate_registered_class(c) for c in fts_implicit_cfg] scenario_callbacks = {"nofts_baseline": nofts_callbacks, "fts_implicit": fts_implicit_callbacks} # %% if AVAIL_GPUS > 0: for scenario_name, scenario_callbacks in scenario_callbacks.items(): model = RteBoolqModule(**lightning_module_kwargs, experiment_tag=scenario_name) - logger = TensorBoardLogger(example_logdir, name=scenario_name) + logger = TensorBoardLogger("lightning_logs", name=scenario_name) callbacks = scenario_callbacks print(f"Beginning training the '{scenario_name}' scenario") train() diff --git a/lightning_examples/text-transformers/text-transformers.py b/lightning_examples/text-transformers/text-transformers.py index d14a2e397..57786f010 100644 --- a/lightning_examples/text-transformers/text-transformers.py +++ b/lightning_examples/text-transformers/text-transformers.py @@ -220,7 +220,7 @@ def setup(self, stage=None) -> None: if stage != "fit": return # Get dataloader by calling it - train_dataloader() is called after setup() by default - train_loader = self.train_dataloader() + train_loader = self.trainer.datamodule.train_dataloader() # Calculate total steps tb_size = self.hparams.train_batch_size * max(1, self.trainer.gpus) From 37e44c2353ecf6707ba90a97f4eda0160b20c763 Mon Sep 17 00:00:00 2001 From: Daniel Dale Date: Fri, 7 Jan 2022 14:57:35 -0800 Subject: [PATCH 24/44] enhance linkcheck configuration and switch two images to be base64 encoded --- docs/source/conf.py | 5 + .../finetuning-scheduler/emphasized_yaml.png | Bin 0 -> 116806 bytes .../finetuning-scheduler/emphasized_yaml.svg | 1176 ----------------- .../finetuning-scheduler.py | 4 +- .../side_by_side_yaml.png | Bin 0 -> 77035 bytes .../side_by_side_yaml.svg | 754 ----------- 6 files changed, 7 insertions(+), 1932 deletions(-) create mode 100644 lightning_examples/finetuning-scheduler/emphasized_yaml.png delete mode 100644 lightning_examples/finetuning-scheduler/emphasized_yaml.svg create mode 100644 lightning_examples/finetuning-scheduler/side_by_side_yaml.png delete mode 100644 lightning_examples/finetuning-scheduler/side_by_side_yaml.svg diff --git a/docs/source/conf.py b/docs/source/conf.py index b20fea9e3..cf7bc4387 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -181,6 +181,11 @@ # (source start file, name, description, authors, manual section). man_pages = [(master_doc, project, project + " Documentation", [author], 1)] +# -- Options for linkcheck builder ---------------------------------------------- +# regex pattern 0: allow linking to a specific selection state in +# tensorboard.dev links while continuing to validate the base experiment link +linkcheck_anchors_ignore = ["scalars.*&runSelectionState.*"] + # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. 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b/lightning_examples/finetuning-scheduler/emphasized_yaml.svg deleted file mode 100644 index eda7e7891..000000000 --- a/lightning_examples/finetuning-scheduler/emphasized_yaml.svg +++ /dev/null @@ -1,1176 +0,0 @@ - - - - - - - - image/svg+xml - - - - - - - - - diff --git a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py index ee8ae15c5..6fb7ea3a3 100644 --- a/lightning_examples/finetuning-scheduler/finetuning-scheduler.py +++ b/lightning_examples/finetuning-scheduler/finetuning-scheduler.py @@ -68,7 +68,7 @@ # # 2. Alter the schedule as desired. # -# +# ![side_by_side_yaml](side_by_side_yaml.png) # # 3. Once the finetuning schedule has been altered as desired, pass it to # ``FinetuningScheduler`` to commence scheduled training: @@ -94,7 +94,7 @@ # # **Tip:** Use of regex expressions can be convenient for specifying more complex schedules: # -# +# ![emphasized_yaml](emphasized_yaml.png) # #