From b99047d165cab0c50f58e5fed3fb60c330dccf13 Mon Sep 17 00:00:00 2001 From: anoushkabhutani Date: Thu, 1 Jun 2023 15:38:49 -0400 Subject: [PATCH] initial commit mist demo --- .github/workflows/CI.yaml | 17 + .github/workflows/pre-commit.yml | 18 + .gitignore | 30 ++ .pre-commit-config.yaml | 38 ++ README.md | 89 ++++ activate | 11 + mist/__init__.py | 0 mist/data_modules/__init__.py | 0 mist/data_modules/data_utils.py | 10 + mist/data_modules/roberta_dataset.py | 128 ++++++ mist/models/__init__.py | 0 mist/models/model_utils.py | 31 ++ mist/models/roberta_base.py | 119 ++++++ mist/utils/__init__.py | 0 mist/utils/callbacks.py | 112 +++++ mist/utils/ckpt.py | 87 ++++ mist/utils/lr_schedule.py | 70 ++++ mist/utils/tokenizer.py | 12 + notebooks/PretrainingMIST.ipynb | 551 +++++++++++++++++++++++++ notebooks/figures/MIST_TMAP.pdf | Bin 0 -> 790268 bytes notebooks/figures/MIST_TMAP.png | Bin 0 -> 1644516 bytes notebooks/figures/MIST_pretraining.pdf | Bin 0 -> 36986 bytes notebooks/figures/MIST_pretraining.png | Bin 0 -> 91176 bytes notebooks/figures/smiles.png | Bin 0 -> 18879 bytes poetry.toml | 6 + pyproject.toml | 40 ++ 26 files changed, 1369 insertions(+) create mode 100644 .github/workflows/CI.yaml create mode 100644 .github/workflows/pre-commit.yml create mode 100644 .gitignore create mode 100644 .pre-commit-config.yaml create mode 100644 README.md create mode 100755 activate create mode 100644 mist/__init__.py create mode 100644 mist/data_modules/__init__.py create mode 100644 mist/data_modules/data_utils.py create mode 100644 mist/data_modules/roberta_dataset.py create mode 100644 mist/models/__init__.py create mode 100644 mist/models/model_utils.py create mode 100644 mist/models/roberta_base.py create mode 100644 mist/utils/__init__.py create mode 100644 mist/utils/callbacks.py create mode 100644 mist/utils/ckpt.py create mode 100644 mist/utils/lr_schedule.py create mode 100644 mist/utils/tokenizer.py create mode 100644 notebooks/PretrainingMIST.ipynb create mode 100644 notebooks/figures/MIST_TMAP.pdf create mode 100644 notebooks/figures/MIST_TMAP.png create mode 100644 notebooks/figures/MIST_pretraining.pdf create mode 100644 notebooks/figures/MIST_pretraining.png create mode 100644 notebooks/figures/smiles.png create mode 100644 poetry.toml create mode 100644 pyproject.toml diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml new file mode 100644 index 0000000..d9ddbb6 --- /dev/null +++ b/.github/workflows/CI.yaml @@ -0,0 +1,17 @@ +on: + push: + pull_request: + workflow_dispatch: + +jobs: + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - run: pipx install poetry + - uses: actions/setup-python@v4 + with: + python-version: 3.11.8 + cache: 'poetry' + - run: poetry install + - run: source activate && pytest diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 0000000..58e04cd --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,18 @@ +on: + workflow_dispatch: + pull_request: + push: + branches: [master] + +jobs: + test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - run: pipx install poetry + - uses: actions/setup-python@v4 + with: + python-version: 3.11.8 + cache: 'poetry' + - run: poetry install + - uses: pre-commit/action@v3.0.1 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6427bfc --- /dev/null +++ b/.gitignore @@ -0,0 +1,30 @@ +__pycache__/ +*.sif +wandb/ +.conda/ +electrolyte-fm/ +lightning_logs/ +.venv/ + +# Log files +*.o* +*.e* + +# Checkpoints +*.ckpt/ +*.ckpt + +# NSight +*.nsys-rep +*.sqlite +*.qdstrm + +# Dataset +.cache/ + +# Spack +.spack-env/ +spack.lock + +# Notebook chekcpoints +.ipynb_checkpoints/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..baa5a38 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,38 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.6.0 + hooks: + - id: trailing-whitespace + args: [--markdown-linebreak-ext=md] + - id: check-yaml + - id: end-of-file-fixer + - id: check-merge-conflict + - id: check-case-conflict + - id: check-added-large-files + - id: check-shebang-scripts-are-executable + exclude_types: [jinja] + - id: check-executables-have-shebangs + - repo: https://github.com/Lucas-C/pre-commit-hooks + rev: v1.5.5 + hooks: + - id: remove-crlf + - id: remove-tabs + - repo: https://github.com/PyCQA/isort + rev: 5.13.2 + hooks: + - id: isort + - repo: https://github.com/psf/black + rev: 24.3.0 + hooks: + - id: black + - repo: https://github.com/FeryET/pre-commit-rust + rev: v1.1.0 + hooks: + - id: fmt + args: [--manifest-path, smirk/Cargo.toml, --] + - id: cargo-check + args: [--manifest-path, smirk/Cargo.toml, --] + - repo: https://github.com/python-poetry/poetry + rev: 1.8.0 + hooks: + - id: poetry-check diff --git a/README.md b/README.md new file mode 100644 index 0000000..acd35af --- /dev/null +++ b/README.md @@ -0,0 +1,89 @@ +# Pre-training MIST (Molecular Insight SMILES Transformer) +This repository is an example of the pre-training workflow for a transformer trained on molecular datasets. + +# Installation + +MIST is trained primarily on Polaris, installation instructions for this system are provided here. +Installation may be slightly different for other systems. + +## Polaris + +1. Load conda +```shell +module purge +module use /soft/modulefiles/ +module --ignore_cache load conda/2024-04-29 +conda activate base +``` + +2. Install poetry + pipx +```shell +python -m pip install pipx +python -m pipx ensurepath +python -m pipx install --python $(which python) poetry +``` + +3. Install environment: +```shell +cd mist +poetry install +``` + +5. Install `ipykernel` and add a kernel for the environment. +```shell +source ./activate +python -m pip install ipykernel +python -m ipykernel install --user --name mist_demo +``` + +## Using the notebooks + +The notebooks demonstrating the MIST pre-training workflow are in the `notebooks` directory. To run them: +1. Request an interactive session with one GPU node. +``` +qsub -I -l select=1 -l filesystems=[home:filesystem] -l walltime=01:00:00 -q debug -A [AccountName] +``` +2. Activate the environment +```shell +# Instructions for Polaris +module purge +module use /soft/modulefiles/ +module --ignore_cache load conda/2024-04-29 gcc-native/12.3 PrgEnv-nvhpc +export CC=gcc-12 +export CXX=g++-12 +cd mist +source ./activate +``` +4. Launch a `jupyter notebook` server and select the `mist_env` kernel. +``` +jupyter notebook --ip $(hostname) --no-browser +``` + +## Data + +The pre-training data is available on [Dropbox](https://www.dropbox.com/scl/fo/3z1lklbper07ojtp5t4iu/AHUEJ_3j5_CRVpWmcGLW3kQ?rlkey=2818imymvf5mk5byz0c7ei1ij&dl=0). +This data should be downloaded and extracted in the `sample_data` folder. It requires ~2.2GB of disk space. + +``` +sample_data +├── data +│ ├── train +│ │ ├── xaaa.txt +│ │ ├── xaab.txt +│ │ ├── ... +│ ├── test +│ │ ├── xaaa.txt +│ │ ├── xaab.txt +│ │ ├── ... +│ ├── val +│ │ ├── xaaa.txt +│ │ ├── xaab.txt +│ │ ├── ... +``` + +The data is pre-shuffled and split into training, validation and test sets with a 80:20:20 ratio. +The training dataset has `~0.25B` molecules, while the test and validation sets have `62M` molecules each. + +## Checkpoint + +A sample checkpoint is also available on Dropbox. This data should be downloaded and placed in the `sample_checkpoint` folder. diff --git a/activate b/activate new file mode 100755 index 0000000..733e40c --- /dev/null +++ b/activate @@ -0,0 +1,11 @@ +#!/bin/bash +# Source this to activate the environment + +# Activate virtual environment +source .venv/bin/activate + +# Add NVIDIA libraries +export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$(realpath .venv/lib64):$(realpath .venv/lib)" + +# DISABLE Compat Check +export DS_SKIP_CUDA_CHECK=1 diff --git a/mist/__init__.py b/mist/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mist/data_modules/__init__.py b/mist/data_modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mist/data_modules/data_utils.py b/mist/data_modules/data_utils.py new file mode 100644 index 0000000..0cd1d03 --- /dev/null +++ b/mist/data_modules/data_utils.py @@ -0,0 +1,10 @@ +from transformers import AutoTokenizer, PreTrainedTokenizerBase + +class DataSetupMixin: + + def setup_tokenizer(self, tokenizer: str): + # Locate Tokeniser and dataset + self.tokenizer: PreTrainedTokenizerBase = AutoTokenizer.from_pretrained( + tokenizer, + trust_remote_code=True + ) \ No newline at end of file diff --git a/mist/data_modules/roberta_dataset.py b/mist/data_modules/roberta_dataset.py new file mode 100644 index 0000000..88928c9 --- /dev/null +++ b/mist/data_modules/roberta_dataset.py @@ -0,0 +1,128 @@ +from pathlib import Path + +import pytorch_lightning as pl +from datasets import IterableDataset, IterableDatasetDict, load_dataset +from datasets.distributed import split_dataset_by_node +from torch.utils.data import DataLoader +from transformers import DataCollatorForLanguageModeling + +from ..utils.tokenizer import load_tokenizer + + +class RobertaDataSet(pl.LightningDataModule): + def __init__( + self, + path: str, + tokenizer: str, + mlm_probability=0.15, + batch_size: int = 64, + val_batch_size=None, + num_workers=0, + prefetch_factor=None, + persistent_workers=False, + ): + super().__init__() + + # Locate Tokeniser and dataset + self.tokenizer = load_tokenizer(tokenizer) + self.vocab_size = self.tokenizer.vocab_size + self.path: Path = Path(path) + assert self.path.is_dir() or self.path.is_file() + + self.mlm_probability = mlm_probability + self.batch_size = batch_size + self.val_batch_size = val_batch_size if val_batch_size else batch_size + self.num_workers = num_workers + self.prefetch_factor = prefetch_factor + self.persistent_workers = persistent_workers + self.save_hyperparameters() + + def prepare_data(self): + self.__load_dataset() + + def __load_dataset(self): + if not hasattr(self, "dataset"): + dataset = load_dataset( + str(self.path), + keep_in_memory=False, + streaming=True, + ) + assert isinstance(dataset, IterableDatasetDict) + self.dataset = dataset + + return self.dataset + + def setup(self, stage: str) -> None: + self.data_collator = DataCollatorForLanguageModeling( + tokenizer=self.tokenizer, + mlm_probability=self.mlm_probability, + mlm=True, + ) + ds = self.__load_dataset().map( + lambda batch: self.tokenizer(batch["text"]), + batched=True, + remove_columns="text", + ) + + # Setup to partition datasets over ranks + if self.trainer is not None: + rank = self.trainer.global_rank + world_size = self.trainer.world_size + ds_train: IterableDataset = ds["train"].shuffle(seed=42) + assert ds_train.n_shards % world_size == 0 + assert ds["validation"].n_shards % world_size == 0 + assert ds["test"].n_shards % world_size == 0 + else: + rank = 0 + world_size = 1 + ds_train: IterableDataset = ds["train"].shuffle(seed=42) + + # Partition Datasets + self.train_dataset: IterableDataset = split_dataset_by_node( + ds_train, + rank=rank, + world_size=world_size, + ) + self.val_dataset: IterableDataset = split_dataset_by_node( + ds["validation"], + rank=rank, + world_size=world_size, + ) + self.test_dataset: IterableDataset = split_dataset_by_node( + ds["test"], + rank=rank, + world_size=world_size, + ) + + def train_dataloader(self): + # Increment epoch to replicate shuffling + return DataLoader( + self.train_dataset, + collate_fn=self.data_collator, + batch_size=self.batch_size, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, + pin_memory=True, + persistent_workers=self.persistent_workers, + ) + + def val_dataloader(self): + return DataLoader( + self.val_dataset, + collate_fn=self.data_collator, + batch_size=self.val_batch_size, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, + pin_memory=True, + persistent_workers=self.persistent_workers, + shuffle=False, + ) + + def test_dataset(self): + return DataLoader( + self.test_dataset, + collate_fn=self.data_collator, + batch_size=self.val_batch_size, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, + ) diff --git a/mist/models/__init__.py b/mist/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mist/models/model_utils.py b/mist/models/model_utils.py new file mode 100644 index 0000000..f0fbf36 --- /dev/null +++ b/mist/models/model_utils.py @@ -0,0 +1,31 @@ +import json +from pathlib import Path + +import pytorch_lightning as pl + +from ..utils.ckpt import SaveConfigWithCkpts + + +class DeepSpeedMixin: + @staticmethod + def load(checkpoint_dir, **kwargs): + print(checkpoint_dir) + return SaveConfigWithCkpts.load(checkpoint_dir, **kwargs) + + def get_encoder(self): + raise NotImplementedError + + +class LoggingMixin(pl.LightningModule): + + def on_train_epoch_start(self) -> None: + # Update the dataset's internal epoch counter + self.trainer.train_dataloader.dataset.set_epoch(self.trainer.current_epoch) + self.log( + "train/dataloader_epoch", + self.trainer.train_dataloader.dataset._epoch, + rank_zero_only=True, + sync_dist=True, + ) + return super().on_train_epoch_start() + diff --git a/mist/models/roberta_base.py b/mist/models/roberta_base.py new file mode 100644 index 0000000..4194934 --- /dev/null +++ b/mist/models/roberta_base.py @@ -0,0 +1,119 @@ +import torch +from typing import Callable +from pytorch_lightning.loggers import WandbLogger +from transformers import RobertaConfig, RobertaForMaskedLM + +from .model_utils import DeepSpeedMixin, LoggingMixin + + +class RoBERTa(DeepSpeedMixin, LoggingMixin): + """ + PyTorch Lightning module for RoBERTa model MLM pre-training. + """ + + def __init__( + self, + vocab_size: int, + intermediate_size: int = 3072, + max_position_embeddings: int = 512, + num_attention_heads: int = 12, + num_hidden_layers: int = 6, + hidden_size: int = 768, + optimizer: Callable = torch.optim.AdamW, + lr_schedule: Callable | None = None, + ) -> None: + super().__init__() + self.optimizer = optimizer + self.lr_schedule = lr_schedule + self.vocab_size = vocab_size + self.save_hyperparameters(ignore=["optimizer", "lr_schedule"]) + + self.config = RobertaConfig( + vocab_size=vocab_size, + intermediate_size=intermediate_size, + hidden_size=hidden_size, + max_position_embeddings=max_position_embeddings, + num_attention_heads=num_attention_heads, + num_hidden_layers=num_hidden_layers, + hidden_dropout_prob=0.1, + attention_probs_dropout_prob=0.1, + type_vocab_size=1, + ) + + def configure_model(self): + self.model = RobertaForMaskedLM(config=self.config) + + def get_encoder(self): + if not hasattr(self, "model"): + self.configure_model() + return self.model.roberta + + def forward(self, batch, **kwargs): # type: ignore[override] + out = self.model( + batch["input_ids"], + labels=batch["labels"], + attention_mask=batch["attention_mask"], + **kwargs, + ) + return out + + def setup(self, stage: str) -> None: + if isinstance(self.logger, WandbLogger): + for m in ["train/loss", "val/loss"]: + for s in ["_step", "_epoch"]: + self.logger.experiment.define_metric(m + s, summary="min") + + def on_train_epoch_start(self) -> None: + # Update the dataset's internal epoch counter + self.trainer.train_dataloader.dataset.set_epoch(self.trainer.current_epoch) + self.log( + "train/dataloader_epoch", + self.trainer.train_dataloader.dataset._epoch, + rank_zero_only=True, + sync_dist=True, + ) + return super().on_train_epoch_start() + + def training_step(self, batch, batch_idx: int) -> torch.FloatTensor: + outputs = self(batch) + loss = outputs.loss + self.log( + "train/loss", + loss, + on_step=True, + on_epoch=True, + prog_bar=True, + sync_dist=True, + ) + return loss + + def validation_step(self, batch, batch_idx: int) -> torch.FloatTensor: + outputs = self(batch) + loss = outputs.loss + self.log( + "val/loss", loss, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True + ) + return loss + + def test_step(self, batch, batch_idx: int) -> torch.FloatTensor: + outputs = self(batch) + loss = outputs.loss + self.log( + "test/loss", + loss, + on_step=True, + on_epoch=True, + prog_bar=True, + sync_dist=True, + ) + return loss + + def configure_optimizers(self): + optimizer = self.optimizer(self.parameters()) + if schedule := self.lr_schedule: + return { + "optimizer": optimizer, + "lr_scheduler": {"scheduler": schedule(optimizer), "interval": "step"}, + } + + return optimizer diff --git a/mist/utils/__init__.py b/mist/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/mist/utils/callbacks.py b/mist/utils/callbacks.py new file mode 100644 index 0000000..e13ca65 --- /dev/null +++ b/mist/utils/callbacks.py @@ -0,0 +1,112 @@ +""" Custom callbacks for benchmarking, adapted from GenSLM + https://github.com/ramanathanlab/genslm/blob/71beb030df72010f5a4883a1f1a0b25bbafbe4a8/genslm/utils.py +""" + +import time +from typing import Any + +import pytorch_lightning as pl +from pytorch_lightning.callbacks import Callback +from pytorch_lightning.loggers import WandbLogger + + +class ThroughputMonitor(Callback): + """Custom callback in order to monitor the throughput and log to weights and biases.""" + + def __init__(self) -> None: + """Logs throughput statistics starting at the 2nd epoch.""" + super().__init__() + self.start_time = 0.0 + self.macro_batch_size = dict() + + def on_train_start( + self, trainer: "pl.Trainer", pl_module: "pl.LightningModule" + ) -> None: + if trainer.is_global_zero: + self.record_macro_batch_size( + "train", trainer.datamodule.batch_size, trainer + ) + self.record_macro_batch_size( + "val", trainer.datamodule.val_batch_size, trainer + ) + + def record_macro_batch_size( + self, + stage: str, + batch_size: int, + trainer: "pl.Trainer", + ): + self.macro_batch_size[stage] = batch_size * trainer.world_size + trainer.logger.log_hyperparams( + {f"stats/{stage}_macro_batch_size": self.macro_batch_size[stage]}, + ) + + # Configure summary metrics + if isinstance(trainer.logger, WandbLogger) and trainer.is_global_zero: + logger = trainer.logger + logger.experiment.define_metric(f"stats/{stage}_batch_time", summary="none") + logger.experiment.define_metric( + f"stats/{stage}_batch_throughput", summary="mean" + ) + + def start_batch_timer(self): + self.start_time = time.perf_counter() + + def record_batch_perf( + self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str + ): + batch_time = time.perf_counter() - self.start_time + macro_batch = self.macro_batch_size.get(stage, 1) + pl_module.log_dict( + { + f"stats/{stage}_batch_time": batch_time, + f"stats/{stage}_batch_throughput": macro_batch / batch_time, + }, + rank_zero_only=True, + on_epoch=True, + sync_dist=False, + ) + + def on_validation_batch_start( + self, + trainer: "pl.Trainer", + pl_module: "pl.LightningModule", + batch: Any, + batch_idx: int, + dataloader_idx: int = 0, + ) -> None: + if trainer.is_global_zero: + self.start_batch_timer() + + def on_train_batch_start( + self, + trainer: "pl.Trainer", + pl_module: "pl.LightningModule", + batch: Any, + batch_idx: int, + ) -> None: + if trainer.is_global_zero: + self.start_batch_timer() + + def on_train_batch_end( + self, + trainer: "pl.Trainer", + pl_module: "pl.LightningModule", + outputs: Any, + batch: Any, + batch_idx: int, + ) -> None: + if trainer.is_global_zero: + self.record_batch_perf(trainer, pl_module, "train") + + def on_validation_batch_end( + self, + trainer: "pl.Trainer", + pl_module: "pl.LightningModule", + outputs, + batch: Any, + batch_idx: int, + dataloader_idx: int = 0, + ) -> None: + if trainer.is_global_zero: + self.record_batch_perf(trainer, pl_module, "val") diff --git a/mist/utils/ckpt.py b/mist/utils/ckpt.py new file mode 100644 index 0000000..cff6498 --- /dev/null +++ b/mist/utils/ckpt.py @@ -0,0 +1,87 @@ +import importlib +import json +import os +from pathlib import Path + +from jsonargparse import Namespace +from pytorch_lightning import Callback, LightningModule, Trainer + + +class SaveConfigWithCkpts(Callback): + """Save Configuration with the model's checkpoints + + # Versions: Use Semantic Versioning for Model Checkpoint format + + ## Unnamed: + - Stored `trainer.lightning_model.hparams` in model_hparms.json + + ## 0.2.0 + - Added version field to model_hparams.json + - Added "class_path" field + - Moved model hparams to "init_args" field + """ + + def __init__( + self, + config: Namespace, + overwrite: bool = True, + ) -> None: + self.config = config + self.overwrite = overwrite + self.already_saved = False + self.config_path = None + + @staticmethod + def load(checkpoint_dir: str | Path, config_path=None) -> LightningModule: + """Restore from a deepspeed checkpoint, mainly used for downstream tasks""" + checkpoint_dir = Path(checkpoint_dir).resolve() + config_path = config_path or checkpoint_dir.parent.parent.joinpath( + "model_hparams.json" + ) + assert ( + checkpoint_dir.is_dir() + ), f"Missing deepspeed checkpoint director {checkpoint_dir}" + assert config_path.is_file(), f"Missing model config file {config_path}" + + with open(config_path, "r") as fid: + config = json.load(fid) + + # Get model class name and config + if "version" in config: + cls_name = config["class_path"] + model_config = config["init_args"] + else: + cls_name = "electrolyte_fm.models.roberta_base.RoBERTa" + model_config = config + + # Import the model class and initialize the model + import_path = cls_name.split(".") + model_cls = importlib.import_module( + ".".join(import_path[:-2]) + ).__getattribute__(import_path[-1]) + assert cls_name == f"{model_cls.__module__}.{model_cls.__name__}" + model = model_cls(**model_config) + model.configure_model() + # Load model weights from the checkpoint + from deepspeed.utils.zero_to_fp32 import ( + get_fp32_state_dict_from_zero_checkpoint, + ) + + state = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) + model.load_state_dict(state, strict=True, assign=True) + return model + + @staticmethod + def get_ckpt_tokenizer(path: str | Path): + print(f"loading tokenizer from {path}") + path = Path(path) + config_path = path.parent.parent.joinpath("config.json") + assert config_path.is_file() + with open(config_path, "r") as fid: + config = json.load(fid) + try: + tokenizer = config["data"]["tokenizer"] + except: + tokenizer = config["data"]["init_args"]["tokenizer"] + print(f"tokenizer: {tokenizer}") + return tokenizer diff --git a/mist/utils/lr_schedule.py b/mist/utils/lr_schedule.py new file mode 100644 index 0000000..19046cd --- /dev/null +++ b/mist/utils/lr_schedule.py @@ -0,0 +1,70 @@ +import math +from functools import partial + +from torch.optim import Optimizer +from torch.optim.lr_scheduler import LambdaLR + + +def _get_cosine_relative_decay_with_warmup( + current_step: int, + *, + num_training_steps: int, + num_warmup_steps: int, + rel_decay: float = 0.1, + num_cycles: float = 0.5, +): + if current_step < num_warmup_steps: + return float(current_step) / float(max(1, num_warmup_steps)) + progress = float(current_step - num_warmup_steps) / float( + max(1, num_training_steps - num_warmup_steps) + ) + amp = 0.5 * (1 - rel_decay) + offset = amp + rel_decay + return max( + rel_decay, + amp * math.cos(2.0 * math.pi * float(num_cycles) * progress) + offset, + ) + + +class RelativeCosineWarmup(LambdaLR): + def __init__( + self, + optimizer: Optimizer, + num_training_steps: int, + num_warmup_steps: int | str, + rel_decay: float = 0.1, + num_cycles: float = 0.5, + last_epoch: int = -1, + ) -> None: + """ + Create a schedule with a learning rate that warms up to the initial lr set in the optimizer from zero, + then decays following a cosine function to `rel_decay * initial_lr` after `num_training_steps` + + Args: + optimizer: the optimizer for which to schedule the learning rate + num_warmup_steps: the number of steps for the warm-up phase + num_training_steps: the total number of training steps + rel_decay: the fraction of the initial lr to decay to. Defaults to 0.1 + num_cycles: The number of waves in the cosine schedule + last_epoch: index of the last epoch when resuming training + + If `num_warmup_steps == "beta"` sets num_warmup_steps to 2/(1 - β₂) per http://arxiv.org/abs/1910.04209. + This only works if optimizer is in the Adam family + """ + + # Set num_warmup_steps from Adam's β₂ per http://arxiv.org/abs/1910.04209 + if isinstance(num_warmup_steps, str) and num_warmup_steps == "beta2": + assert "betas" in optimizer.defaults + beta2 = optimizer.defaults["betas"][1] + num_warmup_steps = math.ceil(2 / (1 - beta2)) + + assert isinstance(num_warmup_steps, int), f"Got {num_warmup_steps}, not an int" + + lr_lambda = partial( + _get_cosine_relative_decay_with_warmup, + num_training_steps=num_training_steps, + num_warmup_steps=num_warmup_steps, + rel_decay=rel_decay, + num_cycles=num_cycles, + ) + super().__init__(optimizer, lr_lambda, last_epoch) diff --git a/mist/utils/tokenizer.py b/mist/utils/tokenizer.py new file mode 100644 index 0000000..cae8511 --- /dev/null +++ b/mist/utils/tokenizer.py @@ -0,0 +1,12 @@ +from transformers import PreTrainedTokenizerBase + + +def load_tokenizer(name, **kwargs) -> PreTrainedTokenizerBase: + # Locate Tokeniser and dataset + from transformers import AutoTokenizer + + return AutoTokenizer.from_pretrained( + name, + trust_remote_code=True, + **kwargs, + ) diff --git a/notebooks/PretrainingMIST.ipynb b/notebooks/PretrainingMIST.ipynb new file mode 100644 index 0000000..80e910f --- /dev/null +++ b/notebooks/PretrainingMIST.ipynb @@ -0,0 +1,551 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-07-16 16:22:17.710192: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-07-16 16:22:19.153266: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "import torch\n", + "import pytorch_lightning as pl\n", + "\n", + "from mist.models.roberta_base import RoBERTa\n", + "from mist.data_modules.roberta_dataset import RobertaDataSet\n", + "from mist.utils.lr_schedule import RelativeCosineWarmup\n", + "\n", + "# enable RUST based parallelism for tokenizers\n", + "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pre-training Dataset\n", + "\n", + "We use a subset of randomly sampled molecules from [Enamine’s REAL Space Chemical Library](https://enamine.net/compound-collections/real-compounds/real-space-navigator), which is currently the largest library of commercially available compounds with 48B virtual products based on ~0.1M reagents and building blocks and 166 defined chemical rules to combine them. \n", + "\n", + "This pre-training dataset covers a significant fraction of the space of possible molecules. The plot below visualizes the chemical space covered by the pre-training dataset using the [TMAP](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-0416-x) (Tree Manifold Approximation and Projection) algorithm and compare it to the chemical space convered by datasets in MoleculeNet. MoleculeNet is a popular cheminformatics benchmark and is representative of datasets typically used to train machine learning models for chemistry.\n", + "\n", + "\"tmap\"\n", + "\n", + "\n", + "The molecules are stored as SMILES (Simplified Molecular-Input Line-Entry System) strings. SMILES are a cheminformatic line notation for describing chemical structures using short ASCII strings. SMILES strings are like a connection table in that they identify the nodes and edges of a molecular graph. In SMILES, hydrogen are typically implicitly implied and atoms are represented by their atomic symbol enclosed in brackets unless they are elements of the “organic subset” (`B`, `C`, `N`, `O`, `P`, `S`, `F`, `Cl`,`Br`, and `I`), which do not require brackets unless they are charged. So gold would be `[Au]` but chlorine would be just `Cl`. If hydrogens are explicitly implied brackets are used. A formal charge is represented by one of the symbols `+` or `-`. Single, double, triple, and aromatic bonds are represented by the symbols, `-`, `=`, `#`, and `:`, respectively. Single and aromatic bonds may be, and usually are, omitted. Below is an example of a SMILES string and the corresponding 2D molecular graph.\n", + "\n", + "\"smiles\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "pretraining_data_path = \"/eagle/FoundEnergy/realspace_v2\"\n", + "tokenizer = \"ibm/MoLFormer-XL-both-10pct\"\n", + "mlm_probability = 0.15 \n", + "batch_size = 64\n", + "val_batch_size = 1\n", + "\n", + "datamodule = RobertaDataSet(\n", + " path=pretraining_data_path,\n", + " tokenizer=tokenizer,\n", + " batch_size=batch_size,\n", + " val_batch_size=val_batch_size\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize Model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "vocab_size = datamodule.tokenizer.vocab_size\n", + "max_position_embeddings = 512\n", + "num_attention_heads = 12\n", + "num_attention_heads = 6\n", + "num_hidden_layers = 6\n", + "hidden_size = 768\n", + "intermediate_size = 768\n", + "relative_cosine_scheduler = lambda optimizer: RelativeCosineWarmup(optimizer, num_warmup_steps=\"beta2\", num_training_steps=50_000)\n", + "\n", + "model = RoBERTa(\n", + " vocab_size=vocab_size,\n", + " max_position_embeddings=max_position_embeddings,\n", + " num_attention_heads=num_attention_heads,\n", + " num_hidden_layers=num_hidden_layers,\n", + " hidden_size=hidden_size,\n", + " intermediate_size=intermediate_size,\n", + " optimizer = torch.optim.AdamW,\n", + " lr_schedule = relative_cosine_scheduler\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize Trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Some callbacks are defined for convinience\n", + "from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint\n", + "\n", + "lr_monitor = LearningRateMonitor(logging_interval=\"step\") # monitors and logs learning rate for schedulers during training\n", + "\n", + "checkpoint_callback = ModelCheckpoint(save_last=\"link\",\n", + " filename=\"epoch={epoch}-step={step}-val_loss={val/loss_epoch:.2f}\",\n", + " monitor=\"val/loss_epoch\",\n", + " save_top_k=5,\n", + " verbose=True,\n", + " auto_insert_metric_name=False\n", + " ) # saves the best model during training based on validation loss\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training!\n", + "\n", + "The pre-training strategy we use is analogous the MLM (Masked Language Modeling) used in NLP (Natural Language Processing). \n", + "Part of the SMILES string is replace with a 'mask'. The objective is a cross-entropy loss on predicting the masked tokens.\n", + "\n", + "\"tmap\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using 16bit Automatic Mixed Precision (AMP)\n", + "GPU available: True (cuda), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/soft/applications/conda/2024-04-29/mconda3/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "/soft/applications/conda/2024-04-29/mconda3/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/4\n", + "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/4\n", + "Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/4\n", + "Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/4\n", + "----------------------------------------------------------------------------------------------------\n", + "distributed_backend=nccl\n", + "All distributed processes registered. Starting with 4 processes\n", + "----------------------------------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bfd606f75f1a483f88a82e60711ce24b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Resolving data files: 0%| | 0/1024 [00:00C[13C-]S[Se@@]=O)(ONCC1=CN(CCOCOCCNC(=O)CN2N=C3OCN3(=OC2=O)[n-]=1\n", + "Labels ['C', '(', '=', ')', 'C', 'C', 'C', 'C', ')', 'N', 'N']\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'RoBERTa' object has no attribute 'model'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m labels \u001b[38;5;241m=\u001b[39m sample[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mflatten()[mask]\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLabels\u001b[39m\u001b[38;5;124m\"\u001b[39m, datamodule\u001b[38;5;241m.\u001b[39mtokenizer\u001b[38;5;241m.\u001b[39mconvert_ids_to_tokens(labels))\n\u001b[0;32m----> 7\u001b[0m pred \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m(\n\u001b[1;32m 8\u001b[0m input_ids\u001b[38;5;241m=\u001b[39msample[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 9\u001b[0m attention_mask \u001b[38;5;241m=\u001b[39m sample[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mattention_mask\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 10\u001b[0m )\n\u001b[1;32m 11\u001b[0m pred \u001b[38;5;241m=\u001b[39m pred\u001b[38;5;241m.\u001b[39mlogits[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39margmax(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)[mask]\n\u001b[1;32m 12\u001b[0m pred \u001b[38;5;241m=\u001b[39m datamodule\u001b[38;5;241m.\u001b[39mtokenizer\u001b[38;5;241m.\u001b[39mconvert_ids_to_tokens(pred)\n", + "File \u001b[0;32m~/mist/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1709\u001b[0m, in \u001b[0;36mModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1707\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m modules:\n\u001b[1;32m 1708\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m modules[name]\n\u001b[0;32m-> 1709\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'RoBERTa' object has no attribute 'model'" + ] + } + ], + "source": [ + "datamodule.setup(stage=\"test\")\n", + "for step, sample in enumerate(datamodule.val_dataloader()):\n", + " print(\"Masked Molecule\", datamodule.tokenizer.decode(sample['input_ids'].flatten()))\n", + " mask = sample['labels'].flatten()!=-100\n", + " labels = sample['labels'].flatten()[mask]\n", + " print(\"Labels\", datamodule.tokenizer.convert_ids_to_tokens(labels))\n", + " pred = trainer.model.model(\n", + " input_ids=sample['input_ids'],\n", + " attention_mask = sample['attention_mask'],\n", + " )\n", + " pred = pred.logits[0].argmax(axis=1)[mask]\n", + " pred = datamodule.tokenizer.convert_ids_to_tokens(pred)\n", + " print(\"Labels\", pred)\n", + " print(\"_\"*150)\n", + " if step > 3:\n", + " break" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mist_demo", + "language": "python", + "name": "mist_demo" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/figures/MIST_TMAP.pdf b/notebooks/figures/MIST_TMAP.pdf new file mode 100644 index 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