From 1081c002af442f5965e81b231e1926ae4d4588ee Mon Sep 17 00:00:00 2001 From: Meyer Scetbon Date: Fri, 13 Dec 2024 13:36:59 +0000 Subject: [PATCH] add cond-fip --- poetry.lock | 5270 +++++++------- pyproject.toml | 2 +- research_experiments/cond_fip/README.md | 61 + research_experiments/cond_fip/poetry.lock | 6270 +++++++++++++++++ research_experiments/cond_fip/pyproject.toml | 53 + .../cond_fip/config/cond_fip_inference.yaml | 11 + .../cond_fip/config/cond_fip_training.yaml | 63 + .../src/cond_fip/config/encoder_training.yaml | 59 + .../config/numpy_tensor_data_module.yaml | 12 + .../config/synthetic_data_module.yaml | 46 + .../cond_fip/src/cond_fip/entrypoint_test.py | 20 + .../cond_fip/launchers/inference_cond_fip.py | 84 + .../src/cond_fip/launchers/train_cond_fip.py | 123 + .../src/cond_fip/launchers/train_encoder.py | 115 + .../src/cond_fip/models/amortized_models.py | 341 + .../src/cond_fip/models/decoder_layer.py | 139 + .../src/cond_fip/tasks/cond_fip_inference.py | 264 + .../src/cond_fip/tasks/cond_fip_training.py | 682 ++ .../src/cond_fip/tasks/encoder_training.py | 203 + 19 files changed, 11372 insertions(+), 2446 deletions(-) create mode 100644 research_experiments/cond_fip/README.md create mode 100644 research_experiments/cond_fip/poetry.lock create mode 100644 research_experiments/cond_fip/pyproject.toml create mode 100644 research_experiments/cond_fip/src/cond_fip/config/cond_fip_inference.yaml create mode 100644 research_experiments/cond_fip/src/cond_fip/config/cond_fip_training.yaml create mode 100644 research_experiments/cond_fip/src/cond_fip/config/encoder_training.yaml create mode 100644 research_experiments/cond_fip/src/cond_fip/config/numpy_tensor_data_module.yaml create mode 100644 research_experiments/cond_fip/src/cond_fip/config/synthetic_data_module.yaml create mode 100644 research_experiments/cond_fip/src/cond_fip/entrypoint_test.py create mode 100644 research_experiments/cond_fip/src/cond_fip/launchers/inference_cond_fip.py create mode 100644 research_experiments/cond_fip/src/cond_fip/launchers/train_cond_fip.py create mode 100644 research_experiments/cond_fip/src/cond_fip/launchers/train_encoder.py create mode 100644 research_experiments/cond_fip/src/cond_fip/models/amortized_models.py create mode 100644 research_experiments/cond_fip/src/cond_fip/models/decoder_layer.py create mode 100644 research_experiments/cond_fip/src/cond_fip/tasks/cond_fip_inference.py create mode 100644 research_experiments/cond_fip/src/cond_fip/tasks/cond_fip_training.py create mode 100644 research_experiments/cond_fip/src/cond_fip/tasks/encoder_training.py diff --git a/poetry.lock b/poetry.lock index 15a3c3e..634a062 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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0248fdb..a84ddbb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "causica" -version = "0.4.4" +version = "0.4.5" description = "" readme = "README.md" authors = ["Microsoft Research - Causica"] diff --git a/research_experiments/cond_fip/README.md b/research_experiments/cond_fip/README.md new file mode 100644 index 0000000..f0b4b0c --- /dev/null +++ b/research_experiments/cond_fip/README.md @@ -0,0 +1,61 @@ +# Zero-Shot Learning of Causal Models (Cond-FiP) +[![Static Badge](https://img.shields.io/badge/paper-CondFiP-brightgreen?style=plastic&label=Paper&labelColor=yellow) +](https://arxiv.org/pdf/2410.06128) + +This repo implements Cond-FiP proposed in the paper "Zero-Shot Learning of Causal Models". + +Cond-FiP is a transformer-based approach to infer Structural Causal Models (SCMs) in a zero-shot manner. Rather than learning a specific SCM for each dataset, we enable the Fixed-Point Approach (FiP) proposed in [Scetbon et al. (2024)](https://openreview.net/pdf?id=JpzIGzru5F), to infer the generative SCMs conditionally on their empirical representations. More specifically, we propose to amortize the learning + of a conditional version of FiP to infer generative SCMs from observations and causal structures on synthetically generated datasets. + + Cond-FiP is composed of two models: (1) a dataset Encoder that produces embeddings given the empirical representations of SCMs, and (2) a Decoder that conditionnally on the dataset embedding infers the generative functional mechanisms of the associated SCM. + +## Dependency +We use [Poetry](https://python-poetry.org/) to manage the project dependencies, they are specified in [pyproject](pyproject.toml) file. To install poetry, run: + +```console + curl -sSL https://install.python-poetry.org | python3 - +``` +To install the environment, run `poetry install` in the directory of cond_fip project. + + +## Run experiments +In the [launchers](src/cond_fip/launchers) directory, we provide scripts to run the training of both the encoder and decoder. + + +### Amortized Learning of the Encoder +To train the Encoder on the synthetically generated datasets of [AVICI](https://arxiv.org/abs/2205.12934), run the following command: +```console + python -m cond_fip.launchers.train_encoder +``` +The model as well as the config file will be saved in `src/cond_fip/outputs`. + + +### Amortized Learning of Cond-FiP +To train the Decoder on the synthetically generated datasets of [AVICI](https://arxiv.org/abs/2205.12934), run the following command: +```console + python -m cond_fip.launchers.train_cond_fip\ + --run_id +``` +The model as well as the config file will be saved in `src/cond_fip/outputs`. This command assumes that an Encoder model has been trained and saved in a directory located at `src/cond_fip/outputs/`. + +### Test Cond-FiP on a new Dataset +To test a trained Cond-FiP, we also provide a [launcher file](src/cond_fip/launchers/inference_cond_fip.py), that enables to infer SCMs with Cond-FiP on new datasets. + +To use this file, one needs to provide the path to the data in the [config file](src/cond_fip/config/numpy_tensor_data_module.yaml) by replacing the value of `data_dir`. +The data should respect a specific format. One can generate example of datasets by running: + +```console + python -m fip.data_generation.avici_data --func_type linear --graph_type er --noise_type gaussian --dist_case in --seed 1 --data_dim 5 --num_interventions 5 +``` +The data will be stored in `./data`. + +To test a pre-trained Cond-FiP model on a specific dataset, one simply needs to run: +```console + python -m cond_fip.launchers.inference_cond_fip\ + --run_id \ + --path_data +``` + +This command assumes that a pre-trained Cond-FiP model has been saved in a directory located at `src/cond_fip/outputs/`, and the data has been saved at the location `path_to_the_data`. + + diff --git a/research_experiments/cond_fip/poetry.lock b/research_experiments/cond_fip/poetry.lock new file mode 100644 index 0000000..4fba38e --- /dev/null +++ b/research_experiments/cond_fip/poetry.lock @@ -0,0 +1,6270 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "absl-py" +version = 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a/research_experiments/cond_fip/pyproject.toml b/research_experiments/cond_fip/pyproject.toml new file mode 100644 index 0000000..11150f7 --- /dev/null +++ b/research_experiments/cond_fip/pyproject.toml @@ -0,0 +1,53 @@ +[tool.poetry] +name = "cond_fip" +version = "0.1.0" +description = "Zero-Shot Learning of Causal Models" +readme = "README.md" +authors = ["Meyer Scetbon", "Divyat Mahajan"] +packages = [ + { include = "cond_fip", from = "src" }, +] +license = "MIT" + +[tool.poetry.dependencies] +python = "~3.10" +fip = { path = "../fip"} + +[tool.poetry.group.dev.dependencies] +black = {version="^22.6.0", extras=["jupyter"]} +isort = "^5.10.1" +jupyter = "^1.0.0" +jupytext = "^1.13.8" +mypy = "^1.0.0" +pre-commit = "^2.19.0" +pylint = "^2.14.4" +pytest = "^7.1.2" +pytest-cov = "^3.0.0" +seaborn = "^0.12.2" +types-python-dateutil = "^2.8.18" +types-requests = "^2.31.0.10" +ema-pytorch= "^0.6.0" + + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.black] +line-length = 120 + +[tool.isort] +line_length = 120 +profile = "black" +py_version = 310 +known_first_party = ["cond_fip"] + +# Keep import sorts by code jupytext percent block (https://github.com/PyCQA/isort/issues/1338) +treat_comments_as_code = ["# %%"] + +[tool.pytest.ini_options] +addopts = "--durations=200" +junit_family = "xunit1" + + + diff --git a/research_experiments/cond_fip/src/cond_fip/config/cond_fip_inference.yaml b/research_experiments/cond_fip/src/cond_fip/config/cond_fip_inference.yaml new file mode 100644 index 0000000..55b6d90 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/config/cond_fip_inference.yaml @@ -0,0 +1,11 @@ +seed_everything: 2048 + +model: + class_path: cond_fip.tasks.cond_fip_inference.CondFiPInference + init_args: + enc_dec_model_path: ./src/cond_fip/outputs/amortized_enc_dec_training_2024-09-09_13-51-00/outputs/best_model.ckpt + +trainer: + logger: MLFlowLogger + accelerator: gpu + devices: 1 diff --git a/research_experiments/cond_fip/src/cond_fip/config/cond_fip_training.yaml b/research_experiments/cond_fip/src/cond_fip/config/cond_fip_training.yaml new file mode 100644 index 0000000..d935052 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/config/cond_fip_training.yaml @@ -0,0 +1,63 @@ +seed_everything: 2048 + +model: + class_path: cond_fip.tasks.cond_fip_training.CondFiPTraining + init_args: + encoder_model_path: ./src/cond_fip/outputs/amortized_encoder_training_2024-07-02_19-09-00/outputs/best_model.ckpt + + learning_rate: 1e-4 + beta1: 0.9 + beta2: 0.95 + weight_decay: 1e-10 + + use_scheduler: true + linear_warmup_steps: 1000 + scheduler_steps: 10_000 + + d_model: 256 + num_heads: 8 + num_layers: 4 + d_ff: 512 + dropout: 0.1 + dim_key: 64 + num_layers_dataset: 2 + + distributed: false + with_true_target: true + final_pair_only: true + + with_ema: true + ema_beta: 0.99 + ema_update_every: 10 + +trainer: + max_epochs: 7000 + logger: MLFlowLogger + accelerator: gpu + check_val_every_n_epoch: 1 + log_every_n_steps: 10 + accumulate_grad_batches: 16 + log_dir: "./src/cond_fip/logging_enc_dec/" + inference_mode: false + devices: 1 + num_nodes: 1 + +early_stopping_callback: + monitor: "val_loss" + min_delta: 0.0001 + patience: 500 + verbose: False + mode: "min" + +best_checkpoint_callback: + dirpath: "./src/cond_fip/logging_enc_dec/" + filename: "best_model" + save_top_k: 1 + mode: "min" + monitor: "val_loss" + every_n_epochs: 1 + +last_checkpoint_callback: + save_last: true + filename: "last_model" + save_top_k: 0 # only the last checkpoint is saved diff --git a/research_experiments/cond_fip/src/cond_fip/config/encoder_training.yaml b/research_experiments/cond_fip/src/cond_fip/config/encoder_training.yaml new file mode 100644 index 0000000..513ed30 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/config/encoder_training.yaml @@ -0,0 +1,59 @@ +seed_everything: 2048 + +model: + class_path: cond_fip.tasks.encoder_training.EncoderTraining + init_args: + + learning_rate: 1e-4 + beta1: 0.9 + beta2: 0.95 + weight_decay: 5e-4 + + use_scheduler: true + linear_warmup_steps: 1000 + scheduler_steps: 10_000 + + d_model: 256 + num_heads: 8 + num_layers: 4 + d_ff: 512 + dropout: 0.0 + dim_key: 32 + d_hidden_head: 1024 + + distributed: false + + with_ema: true + ema_beta: 0.99 + ema_update_every: 10 + +trainer: + max_epochs: 5000 + logger: MLFlowLogger + accelerator: gpu + check_val_every_n_epoch: 1 + log_every_n_steps: 10 + log_dir: "./src/cond_fip/logging_enc/" + inference_mode: false + devices: 1 + num_nodes: 1 + +early_stopping_callback: + monitor: "val_loss" + min_delta: 0.0001 + patience: 500 + verbose: False + mode: "min" + +best_checkpoint_callback: + dirpath: "./src/cond_fip/logging_enc/" + filename: "best_model" + save_top_k: 1 + mode: "min" + monitor: "val_loss" + every_n_epochs: 1 + +last_checkpoint_callback: + save_last: true + filename: "last_model" + save_top_k: 0 # only the last checkpoint is saved diff --git a/research_experiments/cond_fip/src/cond_fip/config/numpy_tensor_data_module.yaml b/research_experiments/cond_fip/src/cond_fip/config/numpy_tensor_data_module.yaml new file mode 100644 index 0000000..5d5ac9f --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/config/numpy_tensor_data_module.yaml @@ -0,0 +1,12 @@ +class_path: fip.data_modules.numpy_tensor_data_module.NumpyTensorDataModule +init_args: + data_dir : "./data/er_linear_gaussian_in/total_nodes_5/seed_1/" + train_batch_size: 400 + test_batch_size: 400 + standardize: false + with_true_graph: true + split_data_noise: true + dod: true + num_workers: 23 + shuffle: false + num_interventions: 1 \ No newline at end of file diff --git a/research_experiments/cond_fip/src/cond_fip/config/synthetic_data_module.yaml b/research_experiments/cond_fip/src/cond_fip/config/synthetic_data_module.yaml new file mode 100644 index 0000000..fb7a4c3 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/config/synthetic_data_module.yaml @@ -0,0 +1,46 @@ +class_path: fip.data_modules.synthetic_data_module.SyntheticDataModule +init_args: + sem_samplers: + class_path: fip.data_generation.sem_factory.SemSamplerFactory + init_args: + node_nums: [20] + noises: ['gaussian'] + graphs: ['er', 'sf_in', 'sf_out'] + funcs: ['linear', 'rff'] + config_gaussian: + low: 0.2 + high: 2.0 + config_er: + edges_per_node: [1,2,3] + config_sf: + edges_per_node: [1,2,3] + attach_power: [1.] + config_linear: + weight_low: 1. + weight_high: 3. + bias_low: -3. + bias_high: 3. + config_rff: + num_rf: 100 + length_low: 7. + length_high: 10. + out_low: 10. + out_high: 20. + bias_low: -3. + bias_high: 3. + train_batch_size: 4 + test_batch_size: 4 + sample_dataset_size: 400 + standardize: true + num_samples_used: 400 + num_workers: 23 + pin_memory: true + persistent_workers: true + prefetch_factor: 2 + factor_epoch: 32 + num_sems: 0 + shuffle: true + num_interventions: 2 + num_intervention_samples: 100 + proportion_treatment: 0. + sample_counterfactuals: false \ No newline at end of file diff --git a/research_experiments/cond_fip/src/cond_fip/entrypoint_test.py b/research_experiments/cond_fip/src/cond_fip/entrypoint_test.py new file mode 100644 index 0000000..f5bd6d4 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/entrypoint_test.py @@ -0,0 +1,20 @@ +import pytorch_lightning as pl +from pytorch_lightning import Trainer +from pytorch_lightning.cli import LightningCLI + + +def main(): + cli = LightningCLI( + model_class=pl.LightningModule, + datamodule_class=pl.LightningDataModule, + trainer_class=Trainer, + subclass_mode_data=True, + subclass_mode_model=True, + save_config_kwargs={"overwrite": True}, + run=False, + ) + cli.trainer.test(cli.model, datamodule=cli.datamodule) + + +if __name__ == "__main__": + main() diff --git a/research_experiments/cond_fip/src/cond_fip/launchers/inference_cond_fip.py b/research_experiments/cond_fip/src/cond_fip/launchers/inference_cond_fip.py new file mode 100644 index 0000000..cddf400 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/launchers/inference_cond_fip.py @@ -0,0 +1,84 @@ +import argparse +import os + +from fip.launchers.basic_commands import launch_job + + +def get_path_pretrained_cond_fip(run_id: str, dir_model: str): + path_model_learned = f"{dir_model}/{run_id}/outputs/best_model.ckpt" + if not os.path.exists(path_model_learned): + raise FileNotFoundError(f"Model checkpoint not found at {path_model_learned}") + return path_model_learned + + +def generate_base_script( + path_data: str, + path_model_learned: str, +): + path_model_config = "./src/cond_fip/config/cond_fip_inference.yaml" + path_data_config = "./src/cond_fip/config/numpy_tensor_data_module.yaml" + param_base_script = [ + (" --config ", path_model_config), + (" --data ", path_data_config), + (" --model.enc_dec_model_path ", path_model_learned), + (" --data.data_dir ", path_data), + ] + command_base_script = " python -m cond_fip.entrypoint_test " + " ".join([f"{k}{v}" for k, v in param_base_script]) + + return command_base_script + + +def command_python( + dir_model: str, + run_id: str, + path_data: str, +): + path_model_learned = get_path_pretrained_cond_fip(run_id, dir_model) + return generate_base_script( + path_data, + path_model_learned, + ) + + +def main( + dir_model: str, + run_id: str, + path_data: str, + num_workers: int, +): + command_base_script = command_python(dir_model, run_id, path_data) + + launch_job( + dir_model=dir_model, + command_base_script=command_base_script, + instance_count=-1, + gpus_per_node=1, + local_resume=False, + run_name_resume="", + num_workers=num_workers, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + # pretrained argument + parser.add_argument("--dir_model", type=str, default="./src/cond_fip/outputs", help="Where the ckpts are stored") + parser.add_argument("--run_id", type=str, help="run id of the pre-trained cond-fip", required=True) + + # data argument + parser.add_argument( + "--path_data", type=str, default="./data/er_linear_gaussian_in/total_nodes_5/seed_1/", help="Path to the data" + ) + + # machine arguments + parser.add_argument("--num_workers", type=int, default=23, help="Number of workers") + + args = parser.parse_args() + + main( + args.dir_model, + args.run_id, + args.path_data, + args.num_workers, + ) diff --git a/research_experiments/cond_fip/src/cond_fip/launchers/train_cond_fip.py b/research_experiments/cond_fip/src/cond_fip/launchers/train_cond_fip.py new file mode 100644 index 0000000..7180520 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/launchers/train_cond_fip.py @@ -0,0 +1,123 @@ +import argparse + +from fip.launchers.basic_commands import generate_path_logging, generate_time_name, launch_job + + +def get_path_pretrained_encoder(run_id: str, dir_model: str): + path_model_learned = f"{dir_model}/{run_id}/outputs/best_model.ckpt" + return path_model_learned + + +def generate_base_script( + path_model_config: str, + path_data_config: str, + path_logging: str, + batch_size: int, + factor_epoch: int, + accumulate_grad_batches: int, + path_model_learned: str, +): + param_base_script = [ + (" --config ", path_model_config), + (" --data ", path_data_config), + (" --best_checkpoint_callback.dirpath ", path_logging), + (" --data.train_batch_size ", batch_size), + (" --data.test_batch_size ", batch_size), + (" --data.factor_epoch ", factor_epoch), + (" --trainer.accumulate_grad_batches ", accumulate_grad_batches), + (" --model.encoder_model_path ", path_model_learned), + ] + command_base_script = " python -m fip.entrypoint " + " ".join([f"{k}{v}" for k, v in param_base_script]) + + return command_base_script + + +def command_python( + dir_model: str, + run_name: str, + batch_size: int, + factor_epoch: int, + accumulate_grad_batches: int, + run_id: str, +): + path_model_config = "./src/cond_fip/config/cond_fip_training.yaml" + path_data_config = "./src/cond_fip/config/synthetic_data_module.yaml" + path_logging = generate_path_logging(run_name=run_name, dir_model=dir_model) + path_model_learned = get_path_pretrained_encoder(run_id, dir_model) + return generate_base_script( + path_model_config, + path_data_config, + path_logging, + batch_size, + factor_epoch, + accumulate_grad_batches, + path_model_learned, + ) + + +def main( + dir_model: str, + batch_size: int, + factor_epoch: int, + accumulate_grad_batches: int, + instance_count: int, + gpus_per_node: int, + local_resume: bool, + run_name_resume: str, + num_workers: int, + run_id: str, +): + run_name = generate_time_name() + run_name = "cond_fip_training_" + run_name + command_base_script = command_python(dir_model, run_name, batch_size, factor_epoch, accumulate_grad_batches, run_id) + + launch_job( + dir_model=dir_model, + command_base_script=command_base_script, + instance_count=instance_count, + gpus_per_node=gpus_per_node, + local_resume=local_resume, + run_name_resume=run_name_resume, + num_workers=num_workers, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + # logging arguments + parser.add_argument("--dir_model", type=str, default="./src/cond_fip/outputs", help="Where to store ckpts") + + # pretrained argument + parser.add_argument("--run_id", type=str, help="run id of the pre-trained encoder", required=True) + + # machine arguments + parser.add_argument("--num_workers", type=int, default=23, help="Number of workers") + + # training arguments + parser.add_argument("--batch_size", type=int, default=4, help="Batch size") + parser.add_argument("--factor_epoch", type=int, default=8, help="Factor epoch") + parser.add_argument("--accumulate_grad_batches", type=int, default=1, help="Accumulate grad batches") + + # GPU arguments + parser.add_argument("--instance_count", type=int, default=-1, help="Number of instances") + parser.add_argument("--gpus_per_node", type=int, default=1, help="Number of gpus per instance") + + # resume arguments + parser.add_argument("--local_resume", action="store_true", help="") + parser.add_argument("--run_name_resume", type=str, default="", help="Name of the run to be resumed") + + args = parser.parse_args() + + main( + args.dir_model, + args.batch_size, + args.factor_epoch, + args.accumulate_grad_batches, + args.instance_count, + args.gpus_per_node, + args.local_resume, + args.run_name_resume, + args.num_workers, + args.run_id, + ) diff --git a/research_experiments/cond_fip/src/cond_fip/launchers/train_encoder.py b/research_experiments/cond_fip/src/cond_fip/launchers/train_encoder.py new file mode 100644 index 0000000..79dab22 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/launchers/train_encoder.py @@ -0,0 +1,115 @@ +import argparse + +from fip.launchers.basic_commands import generate_path_logging, generate_time_name, launch_job + + +def generate_base_script( + path_model_config: str, + path_data_config: str, + path_logging: str, + batch_size: int, + factor_epoch: int, + accumulate_grad_batches: int, +): + param_base_script = [ + (" --config ", path_model_config), + (" --data ", path_data_config), + (" --best_checkpoint_callback.dirpath ", path_logging), + (" --data.train_batch_size ", batch_size), + (" --data.test_batch_size ", batch_size), + (" --data.factor_epoch ", factor_epoch), + (" --trainer.accumulate_grad_batches ", accumulate_grad_batches), + ] + + command_base_script = " python -m fip.entrypoint " + " ".join([f"{k}{v}" for k, v in param_base_script]) + + return command_base_script + + +def command_python( + dir_model: str, + run_name: str, + batch_size: int, + factor_epoch: int, + accumulate_grad_batches: int, +): + path_model_config = "./src/cond_fip/config/encoder_training.yaml" + path_data_config = "./src/cond_fip/config/synthetic_data_module.yaml" + path_logging = generate_path_logging(run_name=run_name, dir_model=dir_model) + return generate_base_script( + path_model_config, + path_data_config, + path_logging, + batch_size, + factor_epoch, + accumulate_grad_batches, + ) + + +def main( + dir_model: str, + batch_size: int, + factor_epoch: int, + accumulate_grad_batches: int, + instance_count: int, + gpus_per_node: int, + local_resume: bool, + run_name_resume: str, + num_workers: int, +): + run_name = generate_time_name() + run_name = "encoder_training_" + run_name + command_base_script = command_python( + dir_model, + run_name, + batch_size, + factor_epoch, + accumulate_grad_batches, + ) + + launch_job( + dir_model=dir_model, + command_base_script=command_base_script, + instance_count=instance_count, + gpus_per_node=gpus_per_node, + local_resume=local_resume, + run_name_resume=run_name_resume, + num_workers=num_workers, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + # logging arguments + parser.add_argument("--dir_model", type=str, default="./src/cond_fip/outputs", help="Where to store ckpts") + + # machine arguments + parser.add_argument("--num_workers", type=int, default=23, help="Number of workers") + + # training arguments + parser.add_argument("--batch_size", type=int, default=4, help="Batch size") + parser.add_argument("--factor_epoch", type=int, default=8, help="Factor epoch") + parser.add_argument("--accumulate_grad_batches", type=int, default=1, help="Accumulate grad batches") + + # GPU argumnents + parser.add_argument("--instance_count", type=int, default=-1, help="Number of instances") + parser.add_argument("--gpus_per_node", type=int, default=1, help="Number of gpus per instance") + + # resume arguments + parser.add_argument("--local_resume", action="store_true", help="") + parser.add_argument("--run_name_resume", type=str, default="", help="Name of the run to be resumed") + + args = parser.parse_args() + + main( + args.dir_model, + args.batch_size, + args.factor_epoch, + args.accumulate_grad_batches, + args.instance_count, + args.gpus_per_node, + args.local_resume, + args.run_name_resume, + args.num_workers, + ) diff --git a/research_experiments/cond_fip/src/cond_fip/models/amortized_models.py b/research_experiments/cond_fip/src/cond_fip/models/amortized_models.py new file mode 100644 index 0000000..af1263d --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/models/amortized_models.py @@ -0,0 +1,341 @@ +import torch +from torch import nn + +from cond_fip.models.decoder_layer import AdaptiveAmortizedDecoderLayer, AmortizedDecoderLayer + + +class AmortizedEncoder(nn.Module): + """Architecture for the dataset embedding network based on self standard and DAG attentions on sample and nodes respectively.""" + + def __init__( + self, + d_model: int, + num_heads: int, + dim_key: int, + num_layers: int, + d_ff: int, + dropout: float, + ): + """ + Args: + d_model: Embedding Dimension + num_heads: Total number of attention heads + dim_key: Dimension of queries, keys and values per head + num_layers: Total number of attention layers + d_ff: Hidden dimension for feedforward layer + dropout: Dropout probability + """ + super().__init__() + + self.emb_data = nn.Linear(1, d_model) + + self.decoder_layers_nodes = nn.ModuleList( + [ + AmortizedDecoderLayer( + d_model, + num_heads, + dim_key, + d_ff, + dropout, + max_seq_length=2, # this parameter has no effect + attn_type="causal", + cost_type="dot_product", + ) + for _ in range(num_layers) + ] + ) + + self.decoder_layers_samples = nn.ModuleList( + [ + AmortizedDecoderLayer( + d_model, + num_heads, + dim_key, + d_ff, + dropout, + max_seq_length=2, # this parameter has no effect + attn_type="standard", + cost_type="dot_product", + ) + for _ in range(num_layers) + ] + ) + + self.norm = nn.LayerNorm(d_model) + + self.num_layers = num_layers + self.dec_mask = None + self.sample_mask = None + + def forward(self, tgt: torch.Tensor) -> torch.Tensor: + """ + Args: + tgt: expected shape (*, num_samples, max_seq_length) + + Returns: + output: expected shape: (*, num_samples, max_seq_length, d_model) + + """ + + dec_output = tgt.unsqueeze(-1) + dec_output = self.emb_data(dec_output) + + for k in range(self.num_layers): + dec_output = dec_output.transpose(-2, -3) + dec_output = self.decoder_layers_samples[k](dec_output, dec_output, dec_output, self.sample_mask) + dec_output = dec_output.transpose(-2, -3) + + dec_output = self.decoder_layers_nodes[k](dec_output, dec_output, dec_output, self.dec_mask) + + dec_output = self.norm(dec_output) + + return dec_output + + +class AmortizedNoise(nn.Module): + """Architecture for the encoder comprising of dataset embedding network and noise prediction layer.""" + + def __init__( + self, + d_model: int, + num_heads: int, + num_layers: int, + d_ff: int, + dropout: float, + dim_key: int, + d_hidden_head: int, + ): + """ + Args: + d_model: Embedding Dimension + num_heads: Total number of attention heads + num_layers: Total number of attention layers + d_ff: Hidden dimension for feedforward layer + dropout: Dropout probability + dim_key: Dimension of queries, keys and values per head + d_hidden_head: Hidden dimension of the head MLP + """ + super().__init__() + + self.encoder = AmortizedEncoder( + d_model=d_model, + num_heads=num_heads, + dim_key=dim_key, + num_layers=num_layers, + d_ff=d_ff, + dropout=dropout, + ) + + self.pred_network = nn.Sequential( + nn.Linear(d_model, d_hidden_head), + nn.LeakyReLU(), + nn.Dropout(p=dropout), + nn.Linear(d_hidden_head, d_hidden_head), + nn.LeakyReLU(), + nn.Dropout(p=dropout), + nn.Linear(d_hidden_head, 1), + ) + + self.d_model = d_model + + def compute_encoding(self, tgt: torch.Tensor) -> torch.Tensor: + """ + Args: + tgt: expected shape (*, batch_size, max_seq_length) + + Returns: + output: expected shape: (*, batch_size, max_seq_length, d_model) + + """ + return self.encoder(tgt) + + def compute_proj(self, tgt: torch.Tensor) -> torch.Tensor: + """ + Args: + tgt: expected shape (*, batch_size, max_seq_length, d_model) + + Returns: + output: expected shape: (*, batch_size, max_seq_length) + + """ + preds = self.pred_network(tgt) + return preds.squeeze(-1) + + def forward(self, tgt: torch.Tensor) -> torch.Tensor: + """ + Args: + tgt: expected shape (*, batch_size, max_seq_length) + + Returns: + output: expected shape: (*, batch_size, max_seq_length) + + """ + dec_output = self.encoder(tgt) + return self.compute_proj(dec_output) + + +class AmortizedEncoderDecoder(nn.Module): + """Architecture for the conditional fixed-point (cond-FiP) method to learn causal functional relationships.""" + + def __init__( + self, + d_model: int, + num_heads: int, + dim_key: int, + num_layers: int, + d_ff: int, + dropout: float, + d_data_emb: int, + num_layers_dataset: int, + ): + """ + Args: + d_model: Embedding Dimension + num_heads: Total number of attention heads + dim_key: Dimension of queries, keys and values per head + num_layers: Total number of attention layers + d_ff: Hidden dimension for feedforward layer + dropout: Dropout probability + d_data_emb: Dimension of dataset embedding input + num_layers_dataset: Number of extra layers to embed the condition + """ + super().__init__() + + self.proj_dataset = nn.Linear(d_data_emb, d_model) + self.norm_dataset_1 = nn.LayerNorm(d_model) + self.norm_dataset_2 = nn.LayerNorm(d_model) + + self.emb_dataset = nn.Linear(d_model, d_model, bias=False) + self.emb_position = nn.Linear(d_model, d_model, bias=False) + self.emb_condition = nn.Linear(d_model, d_model, bias=False) + + self.norm_dataset = nn.LayerNorm(d_model) + self.norm_position = nn.LayerNorm(d_model) + self.norm_condition = nn.LayerNorm(d_model) + + self.num_layers_dataset = num_layers_dataset + self.decoder_layers_nodes_dataset = nn.ModuleList( + [ + AmortizedDecoderLayer( + d_model, + num_heads, + dim_key, + d_ff, + dropout, + max_seq_length=2, # this parameter has no effect + attn_type="causal", + cost_type="dot_product", + ) + for _ in range(num_layers_dataset) + ] + ) + + self.decoder_layers_samples_dataset = nn.ModuleList( + [ + AmortizedDecoderLayer( + d_model, + num_heads, + dim_key, + d_ff, + dropout, + max_seq_length=2, # this parameter has no effect + attn_type="standard", + cost_type="dot_product", + ) + for _ in range(num_layers_dataset) + ] + ) + + self.decoder_layers_nodes = nn.ModuleList( + [ + AdaptiveAmortizedDecoderLayer( + d_model, + num_heads, + dim_key, + d_ff, + dropout, + max_seq_length=2, + attn_type="causal", + cost_type="dot_product", + ) + for _ in range(num_layers) + ] + ) + + self.norm = nn.LayerNorm(d_model) + self.pred_network = nn.Linear(d_model, 1) + + self.num_layers = num_layers + self.dec_mask = None + + def embed_dataset(self, dataset_embedded: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Args: + dataset_embedded: expected shape (*, num_samples, max_seq_length, d_data_emb) + + Returns: + output: expected shape: (*, 1, max_seq_length, d_model, d_model) + + """ + # proj dataset_embedded + dataset_embedded = self.proj_dataset(dataset_embedded) + + # perform attention on dataset_embedded + for k in range(self.num_layers_dataset): + + dataset_embedded = dataset_embedded.transpose(-2, -3) + dataset_embedded = self.decoder_layers_samples_dataset[k]( + dataset_embedded, dataset_embedded, dataset_embedded, None + ) + dataset_embedded = dataset_embedded.transpose(-2, -3) + + dataset_embedded = self.decoder_layers_nodes_dataset[k]( + dataset_embedded, dataset_embedded, dataset_embedded, self.dec_mask + ) + + # normalize the dataset_embedded + dataset_embedded = self.norm_dataset_1(dataset_embedded) + + # take the max w.r.t the sample dimension + dataset_embedded = torch.max(dataset_embedded, dim=1).values.unsqueeze(1) + + # normalize the dataset_embedded + dataset_embedded = self.norm_dataset_2(dataset_embedded) + + # get the conditional embeddings + vector_embedding = self.emb_dataset(dataset_embedded) + pos_embedding = self.emb_position(dataset_embedded) + condition_embedding = self.emb_condition(dataset_embedded) + + # normalize the embeddings + vector_embedding = self.norm_dataset(vector_embedding) + pos_embedding = self.norm_position(pos_embedding) + condition_embedding = self.norm_condition(condition_embedding) + + return vector_embedding, pos_embedding, condition_embedding + + def forward(self, tgt: torch.Tensor, dataset_embedded: torch.Tensor) -> torch.Tensor: + """ + Args: + tgt: expected shape (*, num_samples, max_seq_length) + dataset_embedded: expected shape (*, 1, max_seq_length, d_data_emb) + + Returns: + output: expected shape: (*, num_samples, max_seq_length, d_model) + + """ + + vector_embedding, pos_embedding, condition_embedding = self.embed_dataset(dataset_embedded) + + tgt_embedded = tgt.unsqueeze(-1) + tgt_embedded = tgt_embedded * vector_embedding + pos_embedding + + for k in range(self.num_layers): + pos_embedding = self.decoder_layers_nodes[k]( + pos_embedding, tgt_embedded, tgt_embedded, condition_embedding, self.dec_mask + ) + + dec_output = self.norm(pos_embedding) + preds = self.pred_network(dec_output) + + return preds.squeeze(-1) diff --git a/research_experiments/cond_fip/src/cond_fip/models/decoder_layer.py b/research_experiments/cond_fip/src/cond_fip/models/decoder_layer.py new file mode 100644 index 0000000..0282df2 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/models/decoder_layer.py @@ -0,0 +1,139 @@ +from typing import Optional + +import torch +from torch import nn + +from causica.nn.fip.attention_layer import MultiHeadAttention +from causica.nn.fip.embeddings import PositionWiseFeedForward + + +class AmortizedDecoderLayer(nn.Module): + def __init__( + self, + d_model: int, + num_heads: int, + dim_key: int, + d_ff: int, + dropout: float, + max_seq_length: int, + attn_type: str = "standard", + cost_type: str = "dot_product", + ): + super().__init__() + + self.norm_q = nn.LayerNorm(d_model) + self.norm_k = nn.LayerNorm(d_model) + self.norm_v = nn.LayerNorm(d_model) + + self.self_attn = MultiHeadAttention( + d_model, num_heads, max_seq_length, dim_key=dim_key, attn_type=attn_type, cost_type=cost_type + ) + + self.feed_forward = PositionWiseFeedForward(d_model, d_ff) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + def forward( + self, queries: torch.Tensor, keys: torch.Tensor, values: torch.Tensor, mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + + """ + Args: + queries: expected shape: (batch_size, max_seq_length, d_model) + keys: expected shape: (batch_size, max_seq_length, d_model) + values: expected shape: (batch_size, max_seq_length, d_model) + + Returns: + output: expected shape: (batch_size, max_seq_length, d_model) + + """ + queries_n = self.norm_q(queries) + keys_n = self.norm_k(keys) + values_n = self.norm_v(values) + attn_output = self.self_attn(queries_n, keys_n, values_n, mask) + + x_trans = queries + self.dropout(attn_output) + + x_trans_n = self.norm(x_trans) + ff_output = self.dropout(self.feed_forward(x_trans_n)) + + res = x_trans + ff_output + + return res + + +def modulate(x, shift, scale): + return x * (1 + scale) + shift + + +class AdaptiveAmortizedDecoderLayer(nn.Module): + def __init__( + self, + d_model: int, + num_heads: int, + dim_key: int, + d_ff: int, + dropout: float, + max_seq_length: int, + attn_type: str = "standard", + cost_type: str = "dot_product", + ): + super().__init__() + + self.norm_q = nn.LayerNorm(d_model) + self.norm_k = nn.LayerNorm(d_model) + self.norm_v = nn.LayerNorm(d_model) + + self.self_attn = MultiHeadAttention( + d_model, num_heads, max_seq_length, dim_key=dim_key, attn_type=attn_type, cost_type=cost_type + ) + + self.feed_forward = PositionWiseFeedForward(d_model, d_ff) + + self.norm = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + + self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(d_model, 6 * d_model, bias=True)) + + def forward( + self, + queries: torch.Tensor, + keys: torch.Tensor, + values: torch.Tensor, + condition: torch.Tensor, + mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + + """ + Args: + queries: expected shape: (batch_size, max_seq_length, d_model) + keys: expected shape: (batch_size, max_seq_length, d_model) + values: expected shape: (batch_size, max_seq_length, d_model) + condition: expected shape: (**, d_model) + + Returns: + output: expected shape: (batch_size, max_seq_length, d_model) + + """ + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(condition).chunk( + 6, dim=-1 + ) + + queries_n = self.norm_q(queries) + keys_n = self.norm_k(keys) + values_n = self.norm_v(values) + queries_n_modulated = modulate(queries_n, shift_msa, scale_msa) + keys_n_modulated = modulate(keys_n, shift_msa, scale_msa) + values_n_modulated = modulate(values_n, shift_msa, scale_msa) + + attn_output = self.self_attn(queries_n_modulated, keys_n_modulated, values_n_modulated, mask) + x_trans = queries + gate_msa * self.dropout(attn_output) + + x_trans_n = self.norm(x_trans) + x_trans_n_modulated = modulate(x_trans_n, shift_mlp, scale_mlp) + + ff_output = self.dropout(self.feed_forward(x_trans_n_modulated)) + res = x_trans + gate_mlp * ff_output + + return res diff --git a/research_experiments/cond_fip/src/cond_fip/tasks/cond_fip_inference.py b/research_experiments/cond_fip/src/cond_fip/tasks/cond_fip_inference.py new file mode 100644 index 0000000..55d3808 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/tasks/cond_fip_inference.py @@ -0,0 +1,264 @@ +from typing import Optional + +import pytorch_lightning as pl +import torch +from fip.data_modules.numpy_tensor_data_module import NumpyTensorDataModule +from torch import nn + +from cond_fip.tasks.cond_fip_training import CondFiPTraining + + +class CondFiPInference(pl.LightningModule): + """Lightning module for evaluation of Cond-FiP.""" + + def __init__( + self, + enc_dec_model_path: str = "research_experiments/logging_enc/best_model-v6.ckpt", + ) -> None: + """ + Args: + enc_dec_model_path: Path to pretrained Cond-FiP model + """ + + super().__init__() + + self.enc_dec = CondFiPTraining.load_from_checkpoint(enc_dec_model_path) # pylint: disable=E1120 + + self.encoder = self.enc_dec.encoder + + self.with_ema = self.enc_dec.with_ema + if self.with_ema: + self.decoder = self.enc_dec.ema.ema_model + else: + self.decoder = self.enc_dec.method + + self.encoder.eval() + self.decoder.eval() + + # remove gradient computation in encoder + for param in self.encoder.parameters(): + param.requires_grad = False + + # remove gradient computation in decoder + for param in self.decoder.parameters(): + param.requires_grad = False + + self.is_setup = False + + def setup(self, stage: Optional[str] = None): + _ = stage + if self.is_setup: + return # Already setup + + datamodule = getattr(self.trainer, "datamodule", None) + if not isinstance(datamodule, NumpyTensorDataModule): + raise TypeError( + f"Incompatible data module {datamodule}, requires a NumpyTensorDataModule but is " + f"{type(datamodule).mro()}" + ) + # get the training batches + train_batches = datamodule.train_dataloader() + + # take only the first batch to compute the embedding + train_batch = next(iter(train_batches)) + # apply the transformation on the training batch + train_batch = datamodule.on_before_batch_transfer(train_batch, 0) + train_X, train_y, true_graph, *_ = train_batch + train_X, train_y, true_graph = train_X.to(self.device), train_y.to(self.device), true_graph.to(self.device) + + # save train_X, train_y + self.train_X = train_X + self.train_y = train_y + + self.update_mask(true_graph) + + self.is_setup = True + + def update_mask(self, true_graph): + self.encoder.encoder.dec_mask = true_graph.unsqueeze(1).unsqueeze(2) + self.decoder.dec_mask = true_graph.unsqueeze(1).unsqueeze(2) + + self.encoder.encoder.sample_mask = None + + def test_step(self, batch, _, dataloader_idx=0): + match dataloader_idx: + case 0: + # validation step + self.test_obs(batch, with_true_target=True) + self.test_obs(batch, with_true_target=False) + + # test generation of new points + self.test_generation_new_points(batch, with_true_noise=True) + self.test_generation_new_points(batch, with_true_noise=False) + + case 1: + # test counterfactuals + self.test_prediction_counterfactual(batch, with_true_noise=True) + self.test_prediction_counterfactual(batch, with_true_noise=False) + + def test_obs(self, batch, with_true_target=False): + """Starting from observational data, test whether Cond-FiP can infer noise variables and also reconstruct observational data. + Args: + batch: Batch from the lightning dataset loader + with_true_noise: Whether we utilize the true noise variables or the inferred noise variables + """ + (val_X, val_y, *_) = batch + + # get current meand and std + curr_data = torch.cat([self.train_X, val_X], dim=1) + mean_data = curr_data.mean(dim=1, keepdim=True) + std_data = curr_data.std(dim=1, keepdim=True) + + # standardize the data + train_X = (self.train_X - mean_data) / std_data + train_y = self.train_y / std_data + val_X = (val_X - mean_data) / std_data + val_y = val_y / std_data + + with torch.enable_grad(): + dataset_embedded = self.encoder.compute_encoding(train_X) + + # rmse noise prediction + pred_enc = self.encoder.compute_proj(dataset_embedded) + target_enc = train_X - train_y + err_enc = torch.sqrt(torch.mean((target_enc - pred_enc) ** 2, dim=-1)) + err_enc = torch.mean(err_enc) + self.log( + "test_rmse_noise_loss_std", + err_enc, + prog_bar=True, + on_step=True, + on_epoch=True, + batch_size=train_X.shape[0], + ) + + err_mse_enc = nn.MSELoss()(target_enc, pred_enc) + self.log( + "test_mse_noise_loss_std", + err_mse_enc, + prog_bar=True, + on_step=True, + on_epoch=True, + batch_size=train_X.shape[0], + ) + + if with_true_target: + target = val_X - val_y + else: + target = self.encoder(val_X) + + x_pred = self.decoder(val_X, dataset_embedded) + + test_loss = torch.sqrt(nn.MSELoss(reduction="none")(x_pred, target).mean(-1).mean(-1)).mean() + name_log = "test_loss" if with_true_target else "test_loss_predicted_target" + self.log( + name_log, + test_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + batch_size=x_pred.shape[0], + ) + + mse_loss = nn.MSELoss()(x_pred, target) + name_log = "mse_test_loss" if with_true_target else "mse_test_loss_predicted_target" + self.log(name_log, mse_loss, prog_bar=True, on_step=True, on_epoch=True, batch_size=x_pred.shape[0]) + + def test_generation_new_points(self, batch, with_true_noise=False): + """Starting from noise n, test whether Cond-FiP can generate observational data x. + Args: + batch: Batch from the lightning dataset loader + with_true_noise: Whether we utilize the true noise variables or the inferred noise variables + """ + (val_X, val_y, *_) = batch + batch_size = val_X.shape[0] + + # get current meand and std + curr_data = torch.cat([self.train_X, val_X], dim=1) + mean_data = curr_data.mean(dim=1, keepdim=True) + std_data = curr_data.std(dim=1, keepdim=True) + + # standardize the data + train_X = (self.train_X - mean_data) / std_data + val_X = (val_X - mean_data) / std_data + val_y = val_y / std_data + + with torch.no_grad(): + dataset_embedded = self.encoder.compute_encoding(train_X) + + if not with_true_noise: + noise = val_X - self.encoder( + val_X + ) # we can also concatenate val_X with train_X from set up to improve the prediction of the noise + else: + noise = val_y + + curr_gen = noise.clone() + for _ in range(val_y.shape[-1]): + curr_gen = self.decoder(curr_gen, dataset_embedded) + curr_gen += noise + + loss_gen_rmse_per_sample = torch.sqrt(torch.mean((curr_gen - val_X) ** 2, dim=-1)) + loss_gen = loss_gen_rmse_per_sample.mean() + name_log = ( + "test_rmse_generation_loss_std" if with_true_noise else "test_rmse_generation_loss_std_predicted_noise" + ) + self.log(name_log, loss_gen, prog_bar=True, on_step=True, on_epoch=True, batch_size=batch_size) + + loss_gen_mse = nn.MSELoss()(curr_gen, val_X) + name_log = ( + "test_mse_generation_loss_std" if with_true_noise else "test_mse_generation_loss_std_predicted_noise" + ) + self.log(name_log, loss_gen_mse, prog_bar=True, on_step=True, on_epoch=True, batch_size=batch_size) + + def test_prediction_counterfactual(self, batch, with_true_noise=False): + """Starting from observed data, test whether Cond-Fip can generate counterfactual data. + Args: + batch: Batch from the lightning dataset loader + with_true_noise: Whether we utilize the true noise variables or the inferred noise variables + """ + f_data, n_data, cf_data, int_index, int_values, true_graph = batch + batch_size = f_data.shape[0] + + f_data = f_data.unsqueeze(0) + n_data = n_data.unsqueeze(0) + cf_data = cf_data.unsqueeze(0) + true_graph = true_graph.unsqueeze(0) + int_index = int_index.item() + int_values = int_values[0].item() + + curr_data = torch.cat([self.train_X[0].unsqueeze(0), f_data], dim=1) + mean_data = curr_data.mean(dim=1, keepdim=True) + std_data = curr_data.std(dim=1, keepdim=True) + + f_data_norm = (f_data - mean_data) / std_data + n_data_norm = n_data / std_data + cf_data_norm = (cf_data - mean_data) / std_data + int_values_norm = (int_values - mean_data[..., int_index]) / std_data[..., int_index] + int_values_norm = int_values_norm.item() + + with torch.no_grad(): + dataset_embedded = self.encoder.compute_encoding(f_data_norm) + + if with_true_noise: + noise_norm = n_data_norm + else: + noise_norm = f_data_norm - self.encoder( + f_data_norm + ) # we can also concatenate f_data_norm with train_X from set up to improve the prediction of the noise + + cf_pred = noise_norm.clone() + for _ in range(noise_norm.shape[-1]): + cf_pred = self.decoder(cf_pred, dataset_embedded) + cf_pred += noise_norm + cf_pred[..., int_index] = int_values_norm + + loss_cf_rmse_per_sample = torch.sqrt(torch.mean((cf_pred - cf_data_norm) ** 2, dim=-1)) + loss_cf = loss_cf_rmse_per_sample.mean() + + name_log = "test_rmse_cf_loss" if with_true_noise else "test_rmse_cf_loss_predicted_noise" + self.log(name_log, loss_cf, prog_bar=True, on_step=True, on_epoch=True, batch_size=batch_size) + + loss_cf_mse = nn.MSELoss()(cf_pred, cf_data_norm) + name_log = "test_mse_cf_loss" if with_true_noise else "test_mse_cf_loss_predicted_noise" + self.log(name_log, loss_cf_mse, prog_bar=True, on_step=True, on_epoch=True, batch_size=batch_size) diff --git a/research_experiments/cond_fip/src/cond_fip/tasks/cond_fip_training.py b/research_experiments/cond_fip/src/cond_fip/tasks/cond_fip_training.py new file mode 100644 index 0000000..4816f59 --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/tasks/cond_fip_training.py @@ -0,0 +1,682 @@ +import pytorch_lightning as pl +import torch +from ema_pytorch import EMA +from torch import nn, optim + +from causica.functional_relationships.linear_functional_relationships import LinearFunctionalRelationships +from cond_fip.models.amortized_models import AmortizedEncoderDecoder +from cond_fip.tasks.encoder_training import EncoderTraining + + +class CondFiPTraining(pl.LightningModule): + """Lightning trainer for the amortized learning of causal functional relationships (Cond-FiP).""" + + def __init__( + self, + encoder_model_path: str = "research_experiments/logging_enc/best_model-v6.ckpt", + learning_rate: float = 1e-2, + beta1: float = 0.9, + beta2: float = 0.95, + weight_decay: float = 5e-4, + use_scheduler: bool = False, + linear_warmup_steps: int = 1000, + scheduler_steps: int = 10_000, + d_model: int = 64, + num_heads: int = 1, + num_layers: int = 4, + d_ff: int = 128, + dropout: float = 0.0, + dim_key: int = 32, + num_layers_dataset: int = 1, + distributed: bool = False, + with_true_target: bool = True, + final_pair_only: bool = True, + with_ema: bool = True, + ema_beta: float = 0.99, + ema_update_every: int = 10, + ) -> None: + """ + Args: + encoder_model_path: Path to pretrained encoder for obtaining dataset representations + learning_rate: Learning rate for the optimizer + beta1: First moment of Adam + beta2: Second moment of Adam + weight_decay: Weight decay for the optimizer + use_scheduler: Whether to use a scheduler for learning rate + linear_warmup_steps: Number of steps for initializing the learning rate scheduler + scheduler_steps: Total number of steps when using scheduler + d_model: Embedding dimension used in the transformer model + num_heads: Total number heads for self attention in the transformer model + num_layers: Total self attention layers for the transformer model + d_ff: Hidden dimension for feedforward layer in the transformer model + dropout: Dropout probability for the transformer model + dim_key: Dimension of queries, keys and values per head + num_layers_dataset: Total number of self attention layers for dataset representation transfomer model + distributed: Decide whether we use multiple gpus for training a single job + with_true_target: Whether we use ground truth target or use pretrained encoder to obtain targets + final_pair_only: Whether to use the final pair (input, target) from the fixed-point trajectory for training + with_ema: Decide whether to use exponential moving average of model's weight or not + ema_beta: Hyperparameter for EMA + ema_update_every: Number of iterations before updating EMA + """ + + super().__init__() + + self.encoder_lightning = EncoderTraining.load_from_checkpoint(encoder_model_path) # pylint: disable=E1120 + + self.encoder_with_ema = self.encoder_lightning.with_ema + if self.encoder_with_ema: + self.encoder = self.encoder_lightning.ema.ema_model + else: + self.encoder = self.encoder_lightning.method + + self.encoder.eval() + # remove gradient computation + for param in self.encoder.parameters(): + param.requires_grad = False + + self.method = AmortizedEncoderDecoder( + d_model=d_model, + num_heads=num_heads, + dim_key=dim_key, + num_layers=num_layers, + d_ff=d_ff, + dropout=dropout, + d_data_emb=self.encoder.d_model, + num_layers_dataset=num_layers_dataset, + ) + + self.with_ema = with_ema + if self.with_ema: + self.ema = EMA(self.method, beta=ema_beta, update_every=ema_update_every) + + self.learning_rate = learning_rate + self.weight_decay = weight_decay + self.betas = (beta1, beta2) + + self.use_scheduler = use_scheduler + self.linear_warmup_steps = linear_warmup_steps + self.scheduler_steps = scheduler_steps + + self.with_true_target = with_true_target + self.final_pair_only = final_pair_only + + self.distributed = distributed + + self.save_hyperparameters() + + def configure_optimizers(self): + lr = self.learning_rate + + optimizer = optim.Adam( + self.method.parameters(), + lr=lr, + weight_decay=self.weight_decay, + betas=self.betas, + ) + + if self.use_scheduler: + + def lr_lambda(current_step): + if current_step < self.linear_warmup_steps: + return min(1.0, current_step / self.linear_warmup_steps) + return ( + 0.5 + * ( + 1 + + torch.cos( + torch.Tensor([torch.pi]) + * (current_step - self.linear_warmup_steps) + / (self.scheduler_steps - self.linear_warmup_steps) + ) + ).item() + ) + + scheduler = { + "scheduler": torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda), + "interval": "step", + } + + return [optimizer], [scheduler] + + return optimizer + + def update_mask(self, true_graph): + self.encoder.encoder.dec_mask = true_graph.unsqueeze(1).unsqueeze(2) + self.method.dec_mask = true_graph.unsqueeze(1).unsqueeze(2) + if self.with_ema: + self.ema.ema_model.dec_mask = true_graph.unsqueeze(1).unsqueeze(2) + + self.encoder.encoder.sample_mask = None + + def training_step(self, batch): + (train_X, train_y, true_graph, *_) = batch + + self.update_mask(true_graph) + + # Obtain dataset embedding and log the encoder error + dataset_embedded, rmse_err_enc, mse_err_enc = self.get_dataset_embedding(train_X, train_y) + self.log( + "train_rmse_noise_loss_std", + rmse_err_enc, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=train_X.shape[0], + ) + + self.log( + "train_mse_noise_loss_std", + mse_err_enc, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=train_X.shape[0], + ) + + # Compute (input, target) pairs from true SCM + dec_input, dec_target = self.compute_path_and_target( + batch, + final_pair_only=self.final_pair_only, + ) + + # Predictions + dec_pred = self.method(dec_input, dataset_embedded) + + # compute the loss + train_loss = torch.sqrt(nn.MSELoss(reduction="none")(dec_pred, dec_target).mean(-1).mean(-1)).mean() + self.log( + "train_loss", + train_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=dec_input.shape[0], + ) + + # Update EMA + if self.with_ema: + self.ema.update() + + return train_loss + + def validation_step(self, batch): + (val_X, val_y, true_graph, *_) = batch + + self.update_mask(true_graph) + + # Obtain dataset embedding and log the encoder error + dataset_embedded, rmse_err_enc, mse_err_enc = self.get_dataset_embedding(val_X, val_y) + self.log( + "val_rmse_noise_loss_std", + rmse_err_enc, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=val_X.shape[0], + ) + self.log( + "val_mse_noise_loss_std", + mse_err_enc, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=val_X.shape[0], + ) + + # Compute (input, target) pairs from true SCM + dec_input, dec_target = self.compute_path_and_target( + batch, + final_pair_only=self.final_pair_only, + ) + + # Predict using the decoder on the input points + if self.with_ema: + dec_pred = self.ema.ema_model(dec_input, dataset_embedded) + else: + dec_pred = self.method(dec_input, dataset_embedded) + + val_loss = torch.sqrt(nn.MSELoss(reduction="none")(dec_pred, dec_target).mean(-1).mean(-1)).mean() + # val loss + self.log( + "val_loss", + val_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=dec_input.shape[0], + ) + + # mse loss + mse_loss = nn.MSELoss()(dec_pred, dec_target) + self.log( + "mse_val_loss", + mse_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=dec_input.shape[0], + ) + + # rmse loss + rmse_loss = torch.sqrt(torch.mean((dec_pred - dec_target) ** 2, dim=-1)) + rmse_loss = torch.mean(rmse_loss) + self.log( + "rmse_val_loss", + rmse_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=dec_input.shape[0], + ) + + def test_step(self, batch, _): + (val_X, val_y, true_graph, *_) = batch + + self.update_mask(true_graph) + + # Obtain dataset embedding and log the encoder error + dataset_embedded, rmse_err_enc, mse_err_enc = self.get_dataset_embedding(val_X, val_y) + self.log( + "test_rmse_noise_loss_std", + rmse_err_enc, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=val_X.shape[0], + ) + self.log( + "test_mse_noise_loss_std", + mse_err_enc, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=val_X.shape[0], + ) + + # Compute (input, target) pairs from true SCM + dec_input, dec_target = self.compute_path_and_target( + batch, + final_pair_only=self.final_pair_only, + ) + + # Predict using the decoder on the input points + if self.with_ema: + dec_pred = self.ema.ema_model(dec_input, dataset_embedded) + else: + dec_pred = self.method(dec_input, dataset_embedded) + + test_loss = torch.sqrt(nn.MSELoss(reduction="none")(dec_pred, dec_target).mean(-1).mean(-1)).mean() + self.log( + "test_loss", + test_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=dec_input.shape[0], + ) + + # mse loss + mse_loss = nn.MSELoss()(dec_pred, dec_target) + self.log( + "mse_test_loss", + mse_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=dec_input.shape[0], + ) + + # rmse loss + rmse_loss = torch.sqrt(torch.mean((dec_pred - dec_target) ** 2, dim=-1)) + rmse_loss = torch.mean(rmse_loss) + self.log( + "rmse_test_loss", + rmse_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=dec_input.shape[0], + ) + + # test generation of new points + self.test_generation_new_points(val_X, val_y, with_true_noise=True, dataset_embedded=dataset_embedded) + self.test_generation_new_points(val_X, val_y, with_true_noise=False, dataset_embedded=dataset_embedded) + + # test counterfactuals + self.test_prediction_counterfactual(batch, dataset_embedded=dataset_embedded) + + def get_dataset_embedding(self, data_X, data_y): + """Provides dataset embeddings using the pretrained encoder on a split of the dataset + Args: + data_X: expected shape (batch_size, num_samples, max_seq_length) + data_y: expected shape (batch_size, num_samples, max_seq_length) + + Returns: + dataset_embdeed: expected shape (batch_size, num_emb_samples, max_seq_length) + rmse_err_enc: RMSE noise prediction error + mse_err_enc: MSE noise prediction error + + """ + num_emb_samples = int(data_X.shape[1] / 2) + with torch.no_grad(): + dataset_embedded = self.encoder.compute_encoding(data_X[:, :num_emb_samples, :]) + pred_enc = self.encoder.compute_proj(dataset_embedded) + target_enc = data_X[:, :num_emb_samples, :] - data_y[:, :num_emb_samples, :] + + # rmse noise prediction + rmse_err_enc = torch.sqrt(torch.mean((target_enc - pred_enc) ** 2, dim=-1)) + rmse_err_enc = torch.mean(rmse_err_enc) + + # mse noise prediction + mse_err_enc = nn.MSELoss()(pred_enc, target_enc) + + return dataset_embedded, rmse_err_enc, mse_err_enc + + def compute_path_and_target(self, batch, final_pair_only=True): + """Samples the (inputs, targets) for training cond-FiP from the fixed-point iterations. + Args: + batch: Batch from the lightning dataset loader + final_pair_only: Whether to sample from the last fixed-point iteration, or randomly from intermediate fixed-points iterations. + + Returns: + inputs to the cond-FiP; expected shape (batch_size, num_emb_samples, max_seq_length) + targets corresponding to the inputs for cond-FiP; expected shape (batch_size, num_emb_samples, max_seq_length) + + """ + (data_X, data_y, *_) = batch + total_datasets = data_X.shape[0] + num_emb_samples = int(data_X.shape[1] / 2) + total_nodes = data_X.shape[-1] + + with torch.no_grad(): + if final_pair_only: + # take only the final fixed-point equation + inputs = data_X.clone() + targets = inputs - data_y.clone() + else: + idx_path = torch.randint(0, total_nodes, (total_datasets,)) + + list_inputs = [] + list_targets = [] + X_up_true_path = data_y.clone() + for k in range(total_nodes): + # Update as per the next iterate given by the true SCM + list_inputs.append(X_up_true_path) + X_target = self.compute_true_func(batch, X_up_true_path) + list_targets.append(X_target) + X_up_true_path = X_target + data_y + + inputs = torch.zeros_like(data_X) + targets = torch.zeros_like(data_y) + for k in range(total_datasets): + # List_inputs is of shape: Path length * Num Datasets * Sample Size * Total Nodes + # For each dataset, select corresponding to the random iterate give by idx_path + curr_path = idx_path[k] + inputs[k] = list_inputs[curr_path][k] + targets[k] = list_targets[curr_path][k] + + # Take the second half of the data to obtain input points + if self.with_true_target: + return ( + inputs[:, num_emb_samples:, :], + targets[:, num_emb_samples:, :], + ) + + if final_pair_only: + # with predicted target as per the learned encoder + return ( + inputs[:, num_emb_samples:, :], + self.encoder(inputs[:, num_emb_samples:, :]), + ) + + raise NotImplementedError("When with_true_target is False, only num_path == 1 is implemented") + + def test_generation_new_points(self, val_X, val_y, with_true_noise=False, dataset_embedded=None): + """Starting from noise n, test whether Cond-FiP can generate observational data x. + Args: + val_X: expected shape (batch_size, num_samples, max_seq_length) + val_y: expected shape (batch_size, num_samples, max_seq_length) + with_true_noise: Whether we utilize the true noise variables or the inferred noise variables + dataset_embedded: expected shape (batch_size, num_emb_samples, max_seq_length) + """ + num_emb_samples = int(val_X.shape[1] / 2) + + with torch.no_grad(): + val_X_h2 = val_X[:, num_emb_samples:, :] + + if with_true_noise: + val_y_h2 = val_y[:, num_emb_samples:, :] + else: + val_y_h2 = val_X_h2 - self.encoder(val_X_h2) + + curr_gen = val_y_h2.clone() + for _ in range(val_y_h2.shape[-1]): + # Apply the learned functional mechanisms via the decoder + if self.with_ema: + curr_gen = self.ema.ema_model(curr_gen, dataset_embedded) + else: + curr_gen = self.method(curr_gen, dataset_embedded) + + # Add the noise as part of the additive SCM to obtain next iterate for fixed-poin problem + curr_gen += val_y_h2 + + test_rmse_generation_loss_std = torch.sqrt(torch.mean((curr_gen - val_X_h2) ** 2, dim=-1)) + test_rmse_generation_loss_std = test_rmse_generation_loss_std.mean() + rmse_name_log = ( + "test_rmse_generation_loss_std" if with_true_noise else "test_rmse_generation_loss_std_fake_noise" + ) + self.log( + rmse_name_log, + test_rmse_generation_loss_std, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=val_y_h2.shape[0], + ) + + test_mse_generation_loss_std = nn.MSELoss()(curr_gen, val_X_h2) + mse_name_log = ( + "test_mse_generation_loss_std" if with_true_noise else "test_mse_generation_loss_std_fake_noise" + ) + self.log( + mse_name_log, + test_mse_generation_loss_std, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=val_y_h2.shape[0], + ) + + def test_prediction_counterfactual(self, batch, dataset_embedded=None): + (val_X, *_) = batch + + num_interventions = val_X.shape[-1] + loss_cf_w = 0.0 + loss_cf_wo = 0.0 + for _ in range(num_interventions): + tensor_int_idx, tensor_int_val = self.generate_one_intervention(batch) + + # with true noise + loss_cf_w_curr = self.compute_error_counterfactual( + batch, tensor_int_idx, tensor_int_val, with_true_noise=True, dataset_embedded=dataset_embedded + ) + loss_cf_w += loss_cf_w_curr + + # with fake noise + loss_cf_wo_curr = self.compute_error_counterfactual( + batch, tensor_int_idx, tensor_int_val, with_true_noise=False, dataset_embedded=dataset_embedded + ) + loss_cf_wo += loss_cf_wo_curr + + loss_cf_w = loss_cf_w / num_interventions + loss_cf_wo = loss_cf_wo / num_interventions + + def compute_error_counterfactual(self, batch, int_idx, int_val, with_true_noise=False, dataset_embedded=None): + """Starting from observed data, test whether Cond-Fip can generate counterfactual data. + Args: + batch: Batch from the lightning dataset loader + int_idx: Index of the causal variable to be intervened + int_val: Value of the causal variable post hard intervention + with_true_noise: Whether we utilize the true noise variables or the inferred noise variables + dataset_embedded: expected shape (batch_size, num_emb_samples, max_seq_length) + + Returns: + MSE reconstruction loss in countefactual generation + + """ + (val_X, val_y, *_) = batch + num_emb_samples = int(val_X.shape[1] / 2) + + # compute true counterfactuals + cf_true = self.generate_counterfactual(batch, int_idx, int_val) + cf_true_h2 = cf_true[:, num_emb_samples:, :] + + with torch.no_grad(): + # embed all the data + val_X_h2 = val_X[:, num_emb_samples:, :] + + if with_true_noise: + val_y_h2 = val_y[:, num_emb_samples:, :] + noise = val_y_h2 + else: + noise = val_X_h2 - self.encoder(val_X_h2) + + cf_pred_h2 = noise.clone() + for _ in range(val_y.shape[-1]): + if self.with_ema: + cf_pred_h2 = self.ema.ema_model(cf_pred_h2, dataset_embedded) + else: + cf_pred_h2 = self.method(cf_pred_h2, dataset_embedded) + + cf_pred_h2 += noise + cf_pred_h2 = self.apply_intervention(int_idx, int_val, cf_pred_h2) + + loss_rmse_cf_per_sample = torch.sqrt(torch.mean((cf_pred_h2 - cf_true_h2) ** 2, dim=-1)) + loss_rmse_cf = loss_rmse_cf_per_sample.mean() + + rmse_name_log = "test_rmse_cf_loss" if with_true_noise else "test_rmse_cf_loss_fake_noise" + self.log( + rmse_name_log, + loss_rmse_cf, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=val_X.shape[0], + ) + + loss_mse_cf = nn.MSELoss()(cf_pred_h2, cf_true_h2) + mse_name_log = "test_mse_cf_loss" if with_true_noise else "test_mse_cf_loss_fake_noise" + self.log( + mse_name_log, + loss_mse_cf, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=val_X.shape[0], + ) + + return loss_mse_cf + + def compute_true_func(self, batch, points): + (_, _, true_graph, *metadata) = batch + + mean_data = metadata[0] + std_data = metadata[1] + list_func = metadata[-1] + + f_val = self.from_list_func_to_tensor(list_func, points, true_graph.transpose(-2, -1), mean_data, std_data) + return f_val + + def from_list_func_to_tensor(self, list_func, val_X, true_graph, mean_data, std_data): + targets = [] + val_X_unnormalized = val_X * std_data + mean_data + for num_func, curr_func in enumerate(list_func): + if isinstance(curr_func, LinearFunctionalRelationships): + target_val = curr_func.linear_map(val_X_unnormalized[num_func], true_graph[num_func]) + else: + target_val = curr_func.non_linear_map(val_X_unnormalized[num_func], true_graph[num_func]) + target_val = (target_val.unsqueeze(0) - mean_data[num_func]) / std_data[num_func] + targets.append(target_val) + return torch.cat(targets, dim=0) + + def compute_true_fixed_point(self, batch): + (val_X, val_y, *_) = batch + + with torch.no_grad(): + f_val = val_y + list_true_preds = [val_y] + for _ in range(val_X.shape[-1]): + f_val = self.compute_true_func(batch, f_val) + f_val = f_val + val_y + list_true_preds.append(f_val) + + return f_val, list_true_preds + + def generate_counterfactual(self, batch, int_idx, int_val): + (_, val_y, *_) = batch + + with torch.no_grad(): + f_val = val_y.clone() + for _ in range(val_y.shape[-1]): + f_val = self.compute_true_func(batch, f_val) + f_val = f_val + val_y + f_val = self.apply_intervention(int_idx, int_val, f_val) + + return f_val + + def apply_intervention(self, tensor_int_idx, tensor_int_val, points): + list_cf = [] + size = len(tensor_int_idx) + + for k in range(size): + idx_curr = tensor_int_idx[k].item() + val_curr = tensor_int_val[k].item() + points[k][..., idx_curr] = val_curr + list_cf.append(points[k]) + + return torch.stack(list_cf, dim=0) + + def generate_one_intervention(self, batch, type_treatment="quantile"): + (val_X, *_) = batch + size = len(val_X) + + # generate random index and values for intervention + list_int_idx = [] + list_int_val = [] + for k in range(size): + int_idx = torch.randint(0, val_X[k].shape[-1], (1,)).item() + list_int_idx.append(int_idx) + + if type_treatment == "quantile": + min_val = torch.quantile(val_X[k][:, int_idx], 0.1).item() + max_val = torch.quantile(val_X[k][:, int_idx], 0.9).item() + else: + min_val = val_X[k][:, int_idx].min() + max_val = val_X[k][:, int_idx].max() + + int_val = torch.rand((1,)).to(val_X.device) * (max_val - min_val) + min_val + list_int_val.append(int_val.item()) + + # from list to tensor + tensor_int_idx = torch.tensor(list_int_idx, device=val_X.device).unsqueeze(-1) + tensor_int_val = torch.tensor(list_int_val, device=val_X.device).unsqueeze(-1) + + return tensor_int_idx, tensor_int_val diff --git a/research_experiments/cond_fip/src/cond_fip/tasks/encoder_training.py b/research_experiments/cond_fip/src/cond_fip/tasks/encoder_training.py new file mode 100644 index 0000000..5daed5d --- /dev/null +++ b/research_experiments/cond_fip/src/cond_fip/tasks/encoder_training.py @@ -0,0 +1,203 @@ +import pytorch_lightning as pl +import torch +from ema_pytorch import EMA +from torch import nn, optim + +from cond_fip.models.amortized_models import AmortizedNoise + + +class EncoderTraining(pl.LightningModule): + """Lightning trainer for the amortized learning of dataset embeddings.""" + + def __init__( + self, + learning_rate: float = 1e-4, + beta1: float = 0.9, + beta2: float = 0.95, + weight_decay: float = 5e-4, + use_scheduler: bool = False, + linear_warmup_steps: int = 1000, + scheduler_steps: int = 10_000, + d_model: int = 64, + num_heads: int = 1, + num_layers: int = 4, + d_ff: int = 128, + dropout: float = 0.0, + dim_key: int = 32, + d_hidden_head: int = 1024, + distributed: bool = False, + with_ema: bool = True, + ema_beta: float = 0.99, + ema_update_every: int = 10, + ) -> None: + """ + Args: + learning_rate: Learning rate for the optimizer + beta1: First moment of Adam + beta2: Second moment for Adam + weight_decay: Weight decay strength for the optimizer + use_scheduler: Whether to apply a scheduler for the learning rate + linear_warmup_steps: Warmup for initializing the learning rate scheduler + scheduler_steps: Total number of steps when using scheduler + d_model: Embedding dimension used in the transformer model + num_heads: Total number heads for self attention in the transformer model + num_layers: Total self attention layers for the transformer model + d_ff: Hidden dimension for feedforward layer in the transformer model + dropout: Dropout probability for the transformer model + dim_key: Dimension of queries, keys and values per head + d_hidden_head: Hidden dimension of the head MLP + distributed: Whether we use multiple gpus for training a single job + with_ema: Whether to use exponential moving average of model's weight or not + ema_beta: Hyperparameter for EMA + ema_update_every: Number of iterations before updating EMA + """ + + super().__init__() + + self.method = AmortizedNoise( + d_model=d_model, + num_heads=num_heads, + num_layers=num_layers, + d_ff=d_ff, + dropout=dropout, + dim_key=dim_key, + d_hidden_head=d_hidden_head, + ) + self.with_ema = with_ema + if self.with_ema: + self.ema = EMA(self.method, beta=ema_beta, update_every=ema_update_every) + + self.learning_rate = learning_rate + self.weight_decay = weight_decay + self.betas = (beta1, beta2) + + self.use_scheduler = use_scheduler + self.linear_warmup_steps = linear_warmup_steps + self.scheduler_steps = scheduler_steps + + self.distributed = distributed + + self.save_hyperparameters() + + def configure_optimizers(self): + lr = self.learning_rate + + optimizer = optim.Adam( + self.method.parameters(), + lr=lr, + weight_decay=self.weight_decay, + betas=self.betas, + ) + + if self.use_scheduler: + + def lr_lambda(current_step): + if current_step < self.linear_warmup_steps: + return min(1.0, current_step / self.linear_warmup_steps) + return ( + 0.5 + * ( + 1 + + torch.cos( + torch.Tensor([torch.pi]) + * (current_step - self.linear_warmup_steps) + / (self.scheduler_steps - self.linear_warmup_steps) + ) + ).item() + ) + + scheduler = { + "scheduler": torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda), + "interval": "step", + } + + return [optimizer], [scheduler] + + return optimizer + + def update_mask(self, true_graph): + self.method.encoder.dec_mask = true_graph.unsqueeze(1).unsqueeze(2) + if self.with_ema: + self.ema.ema_model.encoder.dec_mask = true_graph.unsqueeze(1).unsqueeze(2) + + self.method.encoder.sample_mask = None + if self.with_ema: + self.ema.ema_model.encoder.sample_mask = None + + def training_step(self, batch): + (train_X, train_y, true_graph, *_) = batch + batch_size = train_X.shape[0] + + self.update_mask(true_graph) + + # Main Training Loop + y_pred = self.method(train_X) + + # rmse loss + train_loss = torch.sqrt(torch.mean((y_pred - (train_X - train_y)) ** 2, dim=-1)) + train_loss = torch.mean(train_loss) + self.log( + "train_loss", + train_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=batch_size, + ) + + # mse loss + mse_loss = nn.MSELoss()(y_pred, train_X - train_y) + self.log( + "mse_train_loss", + mse_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=batch_size, + ) + + # Update EMA + if self.with_ema: + self.ema.update() + + return train_loss + + def validation_step(self, batch, _): + (val_X, val_y, true_graph, *_) = batch + batch_size = val_X.shape[0] + + with torch.enable_grad(): + + self.update_mask(true_graph) + + if self.with_ema: + y_pred = self.ema.ema_model(val_X) + else: + y_pred = self.method(val_X) + + # rmse loss + val_loss = torch.sqrt(torch.mean((y_pred - (val_X - val_y)) ** 2, dim=-1)) + val_loss = torch.mean(val_loss) + self.log( + "val_loss", + val_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=batch_size, + ) + + # mse loss + mse_loss = nn.MSELoss()(y_pred, val_X - val_y) + self.log( + "mse_val_loss", + mse_loss, + prog_bar=True, + on_step=True, + on_epoch=True, + sync_dist=self.distributed, + batch_size=batch_size, + )