Add syntheticFail c15

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syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+.DS_Store
+# data/adult
+data/beijing
+data/default
+data/magic
+data/news
+data/shoppers
+
+wandb/
+
+*.DS_Store

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/LICENCE

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+Copyright 2024 Andrés Guzmán-Cordero
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/README.md

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+# Exponential Family Variational Flow Matching for Tabular Data Generation
+
+<p align="center">
+  <a href="https://github.com/andresguzco/ef-vfm/blob/main/LICENSE.txt">
+    <img alt="MIT License" src="https://img.shields.io/badge/License-MIT-yellow.svg">
+  </a>
+  <a href="https://openreview.net/pdf?id=kjtvCSkSsy">
+    <img alt="Openreview" src="https://img.shields.io/badge/review-OpenReview-blue">
+  </a>
+  <a href="https://arxiv.org/pdf/2506.05940">
+    <img alt="Paper URL" src="https://img.shields.io/badge/cs.LG-2506.05940-B31B1B.svg">
+  </a>
+</p>
+
+<div align="center">
+  <img src="images/demo.jpg" alt="Model Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+  <p><em> Figure 1: Exponential Family Variational Flow Matching (EF-VFM) is a generative modeling framework designed for mixed continuous
+and discrete variables. By leveraging the exponential family and a mean-field assumption, EF-VFM efficiently matches the sufficient
+statistics of the distributions via learned probability paths, ensuring state-of-the-art fidelity and diversity in synthetic data.</a></em></p>
+</div>
+
+This repository provides the prototypical implementation of EF-VFM: TabbyFlow (ICML, 2025).
+
+## Latest Update
+
+- [2025.09]：We are finally releasing our code! To speed the release and avoid compatibility isues, we removed the hyperparameter scripts we use to launch out experiments in our available cluster. Contact us if you have any questions!
+
+## Introduction
+
+EF-VFM uses the exponential family to jointly model different distributions with a single variational flow matching framework. Its key contributions are:  
+
+1) We propose Exponential Family Variational Flow Matching (EF-VFM), an extension of VFM that incorporates exponential family distributions that facilitates efficient training via moment matching.
+2) We establish a deep connection between VFM and a generalized flow matching objective through the lens of Bregman divergences, offering a theoretical foundation for learning probability paths over mixed data types.
+3) To demonstrate the effectiveness of EF-VFM, we introduce TabbyFlow, a model that achieves state-of-the-art performance on standard tabular benchmarks, improving both fidelity and diversity in synthetic data generation.
+
+The schema of EF-VFM is presented in the figure above. For more details, please refer to [our paper](https://arxiv.org/pdf/2506.05940).
+
+## Environment Setup
+
+Create the main environment with [ef_vfm.yaml](ef_vfm.yaml). This environment will be used for all tasks except for the evaluation of additional data fidelity metrics (i.e., $\alpha$-precision and $\beta$-recall scores)
+
+```conda env create -f ef_vfm.yaml```
+
+Create another environment with [synthcity.yaml](synthcity.yaml) to evaluate additional data fidelity metrics
+
+```conda env create -f synthcity.yaml```
+
+## Datasets Preparation
+
+### Using the datasets experimented in the paper
+
+Download raw datasets:
+
+```python download_dataset.py```
+
+Process datasets:
+
+```python process_dataset.py```
+
+## Training TabbyFlow
+
+To train an unconditional EF-VFM model across the entire table, run
+
+```python main.py --dataname <NAME_OF_DATASET> --mode train --exp_name <EXP_NAME>```
+
+where ```<NAME_OF_DATASET>``` is the name of the dataset you want to train on, and ```<EXP_NAME>``` is the name of your experiment.
+
+Current Options of ```<NAME_OF_DATASET>``` are: adult, default, shoppers, magic, beijing, news
+
+Wanb logging is enabled by default. To disable it and log locally, add the ```--no_wandb``` flag.
+
+You must specify the experiment name, which will be used for logging and saving files, add ```--exp_name <your experiment name>```.
+
+## Sampling and Evaluating TabbyFlow (Density, MLE, C2ST)
+
+To sample synthetic tables from trained EF-VFM models and evaluate them, run
+
+```python main.py --dataname <NAME_OF_DATASET> --mode test --report --no_wandb --exp_name <EXP_NAME>```
+
+where ```<NAME_OF_DATASET>``` and ```<EXP_NAME>``` should be the same as those used in training.
+
+This will sample 20 synthetic tables randomly. Meanwhile, it will evaluate the density, mle, and c2st scores for each sample and report their average and standard deviation. The results will be printed out in the terminal, and the samples and detailed evaluation results will be placed in ./eval/report_runs/<EXP_NAME>/<NAME_OF_DATASET>/.
+
+## Evaluating on Additional Fidelity Metrics ($\alpha$-precision and $\beta$-recall scores)
+
+To evaluate EF-VFM on the additional fidelity metrics ($\alpha$-precision and $\beta$-recall scores), you need to first make sure that you have already generated some samples by the previous commands. Then, you need to switch to the `synthcity` environment (as the synthcity packet used to compute those metrics conflicts with the main environment), by running
+
+```conda activate synthcity```
+
+Then, evaluate the metrics by running
+
+```python eval/eval_quality.py --dataname <NAME_OF_DATASET>```
+
+Similarly, the results will be printed out in the terminal and added to ./eval/report_runs/<EXP_NAME>/<NAME_OF_DATASET>/
+
+## Evaluating Data Privacy (DCR score)
+
+To evalute the privacy metric DCR score, you first need to retrain all the models, as the metric requires an equal split between the training and testing data (our initial splits employ a 90/10 ratio). To retrain with an equal split, run the training command but append `_dcr` to ```<NAME_OF_DATASET>```
+
+```python main.py --dataname <NAME_OF_DATASET>_dcr --mode train```
+
+Then, test the models on DCR with the same `_dcr` suffix
+
+```python main.py --dataname <NAME_OF_DATASET>_dcr --mode test --report --no_wandb```
+
+## License
+
+This work is licensed under the MIT License.
+
+## Acknowledgement
+
+This repo is built upon the previous work TabDiff's [[codebase]](https://github.com/MinkaiXu/TabDiff). Many thanks to Juntong, Minkai, Harper and Hengrui!
+
+## Citation
+
+```@inproceedings{
+guzmancordero2025exponentialfamily,
+title={Exponential Family Variational Flow Matching for Tabular Data Generation},
+author={Andr\'es Guzm\'an-Cordero and Floor Eijkelboom and Jan-Willem van de Meent},
+booktitle={The Forty-Second International Conference on Machine Learning},
+year={2025},
+url={https://openreview.net/forum?id=kjtvCSkSsy}
+}
+```
+
+## Contact
+
+If you encounter any problem or you have any question regarding the paper, please contact [Andrés](andresguzco@gmail.com).

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syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/download_dataset.py

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+import os
+from urllib import request
+import zipfile
+
+DATA_DIR = 'data'
+
+
+NAME_URL_DICT_UCI = {
+    'adult': 'https://archive.ics.uci.edu/static/public/2/adult.zip',
+    'default': 'https://archive.ics.uci.edu/static/public/350/default+of+credit+card+clients.zip',
+    'magic': 'https://archive.ics.uci.edu/static/public/159/magic+gamma+telescope.zip',
+    'shoppers': 'https://archive.ics.uci.edu/static/public/468/online+shoppers+purchasing+intention+dataset.zip',
+    'beijing': 'https://archive.ics.uci.edu/static/public/381/beijing+pm2+5+data.zip',
+    'news': 'https://archive.ics.uci.edu/static/public/332/online+news+popularity.zip',
+    'news_nocat': 'https://archive.ics.uci.edu/static/public/332/online+news+popularity.zip',
+    'adult_dcr': 'https://archive.ics.uci.edu/static/public/2/adult.zip',
+    'default_dcr': 'https://archive.ics.uci.edu/static/public/350/default+of+credit+card+clients.zip',
+    'magic_dcr': 'https://archive.ics.uci.edu/static/public/159/magic+gamma+telescope.zip',
+    'shoppers_dcr': 'https://archive.ics.uci.edu/static/public/468/online+shoppers+purchasing+intention+dataset.zip',
+    'beijing_dcr': 'https://archive.ics.uci.edu/static/public/381/beijing+pm2+5+data.zip',
+    'news_dcr': 'https://archive.ics.uci.edu/static/public/332/online+news+popularity.zip',
+}
+
+def unzip_file(zip_filepath, dest_path):
+    with zipfile.ZipFile(zip_filepath, 'r') as zip_ref:
+        zip_ref.extractall(dest_path)
+
+
+def download_from_uci(name):
+
+    print(f'Start processing dataset {name} from UCI.')
+    save_dir = f'{DATA_DIR}/{name}'
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+
+        url = NAME_URL_DICT_UCI[name]
+        request.urlretrieve(url, f'{save_dir}/{name}.zip')
+        print(f'Finish downloading dataset from {url}, data has been saved to {save_dir}.')
+        
+        unzip_file(f'{save_dir}/{name}.zip', save_dir)
+        print(f'Finish unzipping {name}.')
+    
+    else:
+        print('Aready downloaded.')
+
+if __name__ == '__main__':
+    for name in NAME_URL_DICT_UCI.keys():
+        download_from_uci(name)
+    

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/ef_vfm/ckpt/pipeline_c15/adapter_efvfm/config.pkl

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+size 971

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/ef_vfm/ckpt/pipeline_c15/adapter_efvfm/ema_model_100.pt

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+size 43078323

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/ef_vfm/ckpt/pipeline_c15/adapter_efvfm/model_100.pt

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+oid sha256:4a36d0c4d7afeeb520131b5a952820b503e48c4faf319a4e3eb047a3664aa094
+size 43077579

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/ef_vfm/configs/ef_vfm_configs.toml

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+version https://git-lfs.github.com/spec/v1
+oid sha256:677698232b3ea325b54527cccdc11c49fadb971129a77065e71d64aa7567ddb6
+size 674

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/ef_vfm/main.py

@@ -0,0 +1,246 @@
+import glob
+import json
+import os
+import pickle
+import random
+
+import numpy as np
+from ef_vfm.metrics import TabMetrics
+from ef_vfm.modules.main_modules import UniModMLP
+from ef_vfm.models.flow_model import ExpVFM
+from ef_vfm.trainer import Trainer
+import src
+import torch
+
+from torch.utils.data import DataLoader
+import argparse
+import warnings
+
+import wandb
+
+
+from utils_train import EFVFMDataset
+
+warnings.filterwarnings('ignore')
+
+
+def main(args):
+    device = args.device
+
+    ## Disable scientific numerical format
+    np.set_printoptions(suppress=True)
+    torch.set_printoptions(sci_mode=False)
+
+    ## Get data info
+    dataname = args.dataname
+    data_dir = f'data/{dataname}'
+    info_path = f'data/{dataname}/info.json'
+    with open(info_path, 'r') as f:
+        info = json.load(f)
+    
+    ## Set up flags
+    is_dcr = 'dcr' in dataname
+
+    ## Set experiment name
+    exp_name = args.exp_name
+    assert args.exp_name is not None, "Experiment name must be provided"
+    
+    ## Load configs
+    curr_dir = os.path.dirname(os.path.abspath(__file__))
+    config_path = f'{curr_dir}/configs/ef_vfm_configs.toml'
+    raw_config = src.load_config(config_path)
+    
+    print(f"{args.mode.capitalize()} Mode is Enabled")
+    num_samples_to_generate = None
+    ckpt_path = None
+    if args.mode == 'train':
+        print("NEW training is started")
+    elif args.mode == 'test':
+        num_samples_to_generate = args.num_samples_to_generate
+        ckpt_path = args.ckpt_path
+        if ckpt_path is None:
+            ckpt_parent_path = f"{curr_dir}/ckpt/{dataname}/{exp_name}"
+            ckpt_path_arr = glob.glob(f"{ckpt_parent_path}/best_ema_model*")
+            assert ckpt_path_arr, f"Cannot not infer ckpt_path from {ckpt_parent_path}, please make sure that you first train a model before testing!"
+            ckpt_path = ckpt_path_arr[0]
+        config_path = os.path.join(os.path.dirname(ckpt_path), 'config.pkl')
+        if os.path.exists(config_path):
+            with open(config_path, 'rb') as f:
+                cached_raw_config = pickle.load(f)
+                print(f"Found cached config at {config_path}")
+        raw_config = cached_raw_config
+    
+    
+    ## Creat model_save and result paths
+    model_save_path, result_save_path = None, None
+    if args.mode == 'train':
+        model_save_path = 'debug/ckpt' if args.debug else f'{curr_dir}/ckpt/{dataname}/{exp_name}'
+        result_save_path = model_save_path.replace('ckpt', 'result')  #i.e., f'{curr_dir}/results/{dataname}/{exp_name}'
+    elif args.mode == 'test':
+        if args.report:
+            result_save_path = f"eval/report_runs/{exp_name}/{dataname}"
+        else:
+            result_save_path = os.path.dirname(ckpt_path).replace('ckpt', 'result')    # infer the exp_name from the ckpt_name
+    raw_config['model_save_path'] = model_save_path
+    raw_config['result_save_path'] = result_save_path
+    if model_save_path is not None:
+        if not os.path.exists(model_save_path):
+            os.makedirs(model_save_path)
+    if result_save_path is not None:
+        if not os.path.exists(result_save_path):
+            os.makedirs(result_save_path)
+    
+    ## Make everything determinstic if needed
+    raw_config['deterministic'] = args.deterministic
+    if args.deterministic:
+        print("DETERMINISTIC MODE is enabled!!!")
+        ## Set global random seeds
+        torch.manual_seed(0)
+        random.seed(0)
+        np.random.seed(0)
+
+        ## Ensure deterministic CUDA operations
+        os.environ['PYTHONHASHSEED'] = '0'
+        os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'  # or ':16:8'
+        torch.use_deterministic_algorithms(True)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed(0)
+            torch.cuda.manual_seed_all(0)
+            torch.backends.cudnn.deterministic = True
+            torch.backends.cudnn.benchmark = False
+    
+    ## Set debug mode parameters
+    if args.debug:  # fast eval for DEBUG mode
+        raw_config['train']['main']['check_val_every'] = 2
+        raw_config['train']['main']['batch_size'] = 4096
+        raw_config['sample']['batch_size'] = 10000
+
+    _smoke = os.environ.get("EFVFM_SMOKE_STEPS", "").strip()
+    if _smoke and args.mode == "train":
+        n = max(1, int(_smoke))
+        raw_config["train"]["main"]["steps"] = n
+        raw_config["train"]["main"]["check_val_every"] = max(
+            1, min(n, raw_config["train"]["main"]["check_val_every"])
+        )
+    if os.environ.get("EFVFM_ADAPTER_TRAIN", "").strip() and args.mode == "train":
+        raw_config["train"]["main"]["check_val_every"] = int(raw_config["train"]["main"]["steps"])
+
+    _sample_batch = os.environ.get("EFVFM_SAMPLE_BATCH_SIZE", "").strip()
+    if _sample_batch:
+        raw_config["sample"]["batch_size"] = max(1, int(_sample_batch))
+    _train_workers = os.environ.get("EFVFM_TRAIN_NUM_WORKERS", "").strip()
+    train_num_workers = max(0, int(_train_workers)) if _train_workers else 4
+
+    ## Load training data
+    batch_size = raw_config['train']['main']['batch_size']
+
+    train_data = EFVFMDataset(dataname, data_dir, info, isTrain=True, dequant_dist=raw_config['data']['dequant_dist'], int_dequant_factor=raw_config['data']['int_dequant_factor'])
+    train_loader = DataLoader(
+        train_data,
+        batch_size = batch_size,
+        shuffle = True,
+        num_workers = train_num_workers,
+    )
+    d_numerical, categories = train_data.d_numerical, train_data.categories
+    
+    val_data = EFVFMDataset(dataname, data_dir, info, isTrain=False, dequant_dist=raw_config['data']['dequant_dist'], int_dequant_factor=raw_config['data']['int_dequant_factor'])
+
+    ## Load Metrics
+    real_data_path = f'synthetic/{dataname}/real.csv'
+    test_data_path = f'synthetic/{dataname}/test.csv'
+    val_data_path = f'synthetic/{dataname}/val.csv'
+    if not os.path.exists(val_data_path):
+        print(f"{args.dataname} does not have its validation set. During MLE evaluation, a validation set will be splitted from the training set!")
+        val_data_path = None
+    if args.mode == 'train':
+        metric_list = ["density"]
+    else:
+        if is_dcr:
+            metric_list = ["dcr"]
+        else:
+            metric_list = [
+                "density", 
+                "mle", 
+                "c2st",
+            ]
+    metrics = TabMetrics(real_data_path, test_data_path, val_data_path, info, device, metric_list=metric_list)
+    
+    ## Load the module and models
+    raw_config['unimodmlp_params']['d_numerical'] = d_numerical
+    raw_config['unimodmlp_params']['categories'] = (categories).tolist() 
+    model = UniModMLP(**raw_config['unimodmlp_params'])
+    model.to(device)
+    
+    flow_model = ExpVFM(
+        num_classes=categories,
+        num_numerical_features=d_numerical,
+        vf_fn=model,
+        device=device,
+    )
+    num_params = sum(p.numel() for p in flow_model.parameters())
+    print("The number of parameters = ", num_params)
+    flow_model.to(device)
+    flow_model.train()
+
+    ## Print the configs
+    printed_configs = json.dumps(raw_config, default=lambda x: int(x) if isinstance(x, np.int64) else x, indent=4)
+    print(f"The config of the current run is : \n {printed_configs}")
+    
+    ## Enable Wandb
+    project_name = f"XVFM_{dataname}"
+    raw_config['project_name'] = project_name
+    logger = wandb.init(
+        project=raw_config['project_name'], 
+        name=exp_name,
+        config=raw_config,
+        mode='disabled' if args.debug or args.no_wandb else 'online',
+    )
+
+    ## Load Trainer
+    sample_batch_size = raw_config['sample']['batch_size']
+    trainer = Trainer(
+        flow_model,
+        train_loader,
+        train_data,
+        val_data,
+        metrics,
+        logger,
+        **raw_config['train']['main'],
+        sample_batch_size=sample_batch_size,
+        num_samples_to_generate=num_samples_to_generate,
+        model_save_path=raw_config['model_save_path'],
+        result_save_path=raw_config['result_save_path'],
+        device=device,
+        ckpt_path=ckpt_path,
+    )
+    if args.mode == 'test':
+        if args.report:
+            if  is_dcr:
+                trainer.report_test_dcr(args.num_runs)
+            else:
+                trainer.report_test(args.num_runs)
+        else:
+            trainer.test()
+    else:
+        ## Save config
+        config_save_path = raw_config['model_save_path']
+        with open (os.path.join(config_save_path, 'config.pkl'), 'wb') as f:
+            pickle.dump(raw_config, f)
+        trainer.run_loop()
+
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser(description='Training of TabbyFlow')
+
+    parser.add_argument('--dataname', type=str, default='adult', help='Name of dataset.')
+    parser.add_argument('--gpu', type=int, default=0, help='GPU index.')
+
+    args = parser.parse_args()
+
+    # check cuda
+    if args.gpu != -1 and torch.cuda.is_available():
+        args.device = f'cuda:{args.gpu}'
+    else:
+        args.device = 'cpu'

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/ef_vfm/metrics.py

@@ -0,0 +1,306 @@
+from copy import deepcopy
+import numpy as np
+import torch
+import pandas as pd
+# Metrics
+from eval.mle.mle import get_evaluator
+from eval.visualize_density import plot_density
+from sdmetrics.reports.single_table import QualityReport, DiagnosticReport
+from sdmetrics.single_table import LogisticDetection
+from sklearn.preprocessing import OneHotEncoder
+
+from tqdm import tqdm
+
+
+class TabMetrics(object):
+    def __init__(self, real_data_path, test_data_path, val_data_path, info, device, metric_list) -> None:
+        self.real_data_path = real_data_path
+        self.test_data_path = test_data_path
+        self.val_data_path = val_data_path
+        self.info = info
+        self.device = device
+        self.real_data_size = len(pd.read_csv(real_data_path))
+        self.metric_list = metric_list
+
+    def evaluate(self, syn_data):
+        metrics, extras = {}, {}
+        syn_data_cp = deepcopy(syn_data)
+        for metric in self.metric_list:
+            func = eval(f"self.evaluate_{metric}")
+            print(f"Evaluating {metric}")
+            out_metrics, out_extras = func(syn_data_cp)
+            metrics.update(out_metrics)
+            extras.update(out_extras)
+        return metrics, extras
+    
+    def evaluate_density(self, syn_data):
+        real_data = pd.read_csv(self.real_data_path)
+        real_data.columns = range(len(real_data.columns))
+        syn_data.columns = range(len(syn_data.columns))
+        
+
+        info = deepcopy(self.info)
+        
+        y_only = len(syn_data.columns)==1
+        if y_only:
+            target_col_idx = info['target_col_idx'][0]
+            syn_data = self.complete_y_only_data(syn_data, real_data, target_col_idx)
+
+        metadata = info['metadata']
+        metadata['columns'] = {int(key): value for key, value in metadata['columns'].items()} # ensure that keys are all integers?
+
+        new_real_data, new_syn_data, metadata = reorder(real_data, syn_data, info)
+
+        qual_report = QualityReport()
+        qual_report.generate(new_real_data, new_syn_data, metadata)
+
+        diag_report = DiagnosticReport()
+        diag_report.generate(new_real_data, new_syn_data, metadata)
+
+        quality =  qual_report.get_properties()
+        diag = diag_report.get_properties()
+
+        Shape = quality['Score'][0]
+        Trend = quality['Score'][1]
+
+        Overall = (Shape + Trend) / 2
+
+        shape_details = qual_report.get_details(property_name='Column Shapes')
+        trend_details = qual_report.get_details(property_name='Column Pair Trends')
+
+        if y_only:
+            Shape = shape_details['Score'].min()
+        out_metrics = {
+            "density/Shape": Shape,
+            "density/Trend": Trend,
+            "density/Overall": Overall,
+        }
+        out_extras = {
+            "shapes": shape_details,
+            "trends": trend_details
+        }
+        return out_metrics, out_extras
+    
+    def evaluate_mle(self, syn_data):
+        train = syn_data.to_numpy()
+        test = pd.read_csv(self.test_data_path).to_numpy()
+        val = pd.read_csv(self.val_data_path).to_numpy() if self.val_data_path else None
+        
+        info = deepcopy(self.info)
+
+        task_type = info['task_type']
+
+        evaluator = get_evaluator(task_type)
+
+        if task_type == 'regression':
+            best_r2_scores, best_rmse_scores = evaluator(train, test, info, val=val)
+            
+            overall_scores = {}
+            for score_name in ['best_r2_scores', 'best_rmse_scores']:
+                overall_scores[score_name] = {}
+                
+                scores = eval(score_name)
+                for method in scores:
+                    name = method['name']  
+                    method.pop('name')
+                    overall_scores[score_name][name] = method 
+
+        else:
+            best_f1_scores, best_weighted_scores, best_auroc_scores, best_acc_scores, best_avg_scores = evaluator(train, test, info, val=val)
+
+            overall_scores = {}
+            for score_name in ['best_f1_scores', 'best_weighted_scores', 'best_auroc_scores', 'best_acc_scores', 'best_avg_scores']:
+                overall_scores[score_name] = {}
+                
+                scores = eval(score_name)
+                for method in scores:
+                    name = method['name']  
+                    method.pop('name')
+                    overall_scores[score_name][name] = method
+                    
+        mle_score = overall_scores['best_rmse_scores']['XGBRegressor']['RMSE'] if task_type == 'regression' else overall_scores['best_auroc_scores']['XGBClassifier']['roc_auc']
+        out_metrics = {
+            "mle": mle_score,
+        }
+        out_extras = {
+            "mle": overall_scores,
+        }
+        return out_metrics, out_extras
+    
+    def evaluate_c2st(self, syn_data):
+        info = deepcopy(self.info)
+        real_data = pd.read_csv(self.real_data_path)
+
+        real_data.columns = range(len(real_data.columns))
+        syn_data.columns = range(len(syn_data.columns))
+
+        metadata = info['metadata']
+        metadata['columns'] = {int(key): value for key, value in metadata['columns'].items()}
+
+        new_real_data, new_syn_data, metadata = reorder(real_data, syn_data, info)
+
+        score = LogisticDetection.compute(
+            real_data=new_real_data,
+            synthetic_data=new_syn_data,
+            metadata=metadata
+        )
+        
+        out_metrics = {
+            "c2st": score,
+        }
+        out_extras = {}
+        return out_metrics, out_extras
+
+    def evaluate_dcr(self, syn_data):
+        info = deepcopy(self.info)
+        real_data = pd.read_csv(self.real_data_path)
+        test_data = pd.read_csv(self.test_data_path)
+        
+        num_col_idx = info['num_col_idx']
+        cat_col_idx = info['cat_col_idx']
+        target_col_idx = info['target_col_idx']
+
+        task_type = info['task_type']
+        if task_type == 'regression':
+            num_col_idx += target_col_idx
+        else:
+            cat_col_idx += target_col_idx
+
+        num_ranges = []
+
+        real_data.columns = list(np.arange(len(real_data.columns)))
+        syn_data.columns = list(np.arange(len(real_data.columns)))
+        test_data.columns = list(np.arange(len(real_data.columns)))
+        for i in num_col_idx:
+            num_ranges.append(real_data[i].max() - real_data[i].min()) 
+        
+        num_ranges = np.array(num_ranges)
+
+
+        num_real_data = real_data[num_col_idx]
+        cat_real_data = real_data[cat_col_idx]
+        num_syn_data = syn_data[num_col_idx]
+        cat_syn_data = syn_data[cat_col_idx]
+        num_test_data = test_data[num_col_idx]
+        cat_test_data = test_data[cat_col_idx]
+
+        num_real_data_np = num_real_data.to_numpy()
+        cat_real_data_np = cat_real_data.to_numpy().astype('str')
+        num_syn_data_np = num_syn_data.to_numpy()
+        cat_syn_data_np = cat_syn_data.to_numpy().astype('str')
+        num_test_data_np = num_test_data.to_numpy()
+        cat_test_data_np = cat_test_data.to_numpy().astype('str')
+
+        encoder = OneHotEncoder()
+        cat_complete_data_np = np.concatenate([cat_real_data_np, cat_test_data_np], axis=0)
+        encoder.fit(cat_complete_data_np)
+        # encoder.fit(cat_real_data_np)
+
+
+        cat_real_data_oh = encoder.transform(cat_real_data_np).toarray()
+        cat_syn_data_oh = encoder.transform(cat_syn_data_np).toarray()
+        cat_test_data_oh = encoder.transform(cat_test_data_np).toarray()
+
+        num_real_data_np = num_real_data_np / num_ranges
+        num_syn_data_np = num_syn_data_np / num_ranges
+        num_test_data_np = num_test_data_np / num_ranges
+
+        real_data_np = np.concatenate([num_real_data_np, cat_real_data_oh], axis=1)
+        syn_data_np = np.concatenate([num_syn_data_np, cat_syn_data_oh], axis=1)
+        test_data_np = np.concatenate([num_test_data_np, cat_test_data_oh], axis=1)
+
+        device = self.device
+
+        real_data_th = torch.tensor(real_data_np).to(device)
+        syn_data_th = torch.tensor(syn_data_np).to(device)  
+        test_data_th = torch.tensor(test_data_np).to(device)
+
+        dcrs_real = []
+        dcrs_test = []
+        batch_size = 10000 // cat_real_data_oh.shape[1]   # This esitmation should make sure that dcr_real and dcr_test can be fit into 10GB GPU memory
+
+        for i in tqdm(range((syn_data_th.shape[0] // batch_size) + 1)):
+            if i != (syn_data_th.shape[0] // batch_size):
+                batch_syn_data_th = syn_data_th[i*batch_size: (i+1) * batch_size]
+            else:
+                batch_syn_data_th = syn_data_th[i*batch_size:]
+                
+            dcr_real = (batch_syn_data_th[:, None] - real_data_th).abs().sum(dim = 2).min(dim = 1).values
+            dcr_test = (batch_syn_data_th[:, None] - test_data_th).abs().sum(dim = 2).min(dim = 1).values
+            dcrs_real.append(dcr_real)
+            dcrs_test.append(dcr_test)
+            
+        dcrs_real = torch.cat(dcrs_real)
+        dcrs_test = torch.cat(dcrs_test)
+        
+        score = (dcrs_real < dcrs_test).nonzero().shape[0] / dcrs_real.shape[0]
+        
+        out_metrics = {
+            "dcr": score,
+        }
+        out_extras = {
+            "dcr_real": dcrs_real.cpu().numpy(),
+            "dcr_test": dcrs_test.cpu().numpy(),
+        }
+        return out_metrics, out_extras
+        
+    
+    def plot_density(self, syn_data):
+        syn_data_cp = deepcopy(syn_data)
+        real_data = pd.read_csv(self.real_data_path)
+        info = deepcopy(self.info)
+        y_only = len(syn_data_cp.columns)==1
+        if y_only:
+            target_col_idx = info['target_col_idx'][0]
+            target_col_name = info['column_names'][target_col_idx]
+            syn_data_cp = self.complete_y_only_data(syn_data_cp, real_data, target_col_name)
+        img = plot_density(syn_data_cp, real_data, info)
+        return img
+    
+    def complete_y_only_data(self, syn_data, real_data, target_col_idx):
+        syn_target_col = deepcopy(syn_data.iloc[:, 0])
+        syn_data = deepcopy(real_data)
+        syn_data[target_col_idx] = syn_target_col
+        return syn_data
+        
+
+def reorder(real_data, syn_data, info):
+    num_col_idx = deepcopy(info['num_col_idx']) # BUG: info will be modified by += in the next few lines
+    cat_col_idx = deepcopy(info['cat_col_idx'])
+    target_col_idx = deepcopy(info['target_col_idx'])
+
+    task_type = info['task_type']
+    if task_type == 'regression':
+        num_col_idx += target_col_idx
+    else:
+        cat_col_idx += target_col_idx
+
+    real_num_data = real_data[num_col_idx]
+    real_cat_data = real_data[cat_col_idx]
+
+    new_real_data = pd.concat([real_num_data, real_cat_data], axis=1)
+    new_real_data.columns = range(len(new_real_data.columns))
+
+    syn_num_data = syn_data[num_col_idx]
+    syn_cat_data = syn_data[cat_col_idx]
+    
+    new_syn_data = pd.concat([syn_num_data, syn_cat_data], axis=1)
+    new_syn_data.columns = range(len(new_syn_data.columns))
+
+    
+    metadata = info['metadata']
+
+    columns = metadata['columns']
+    metadata['columns'] = {}
+
+    inverse_idx_mapping = info['inverse_idx_mapping']
+
+
+    for i in range(len(new_real_data.columns)):
+        if i < len(num_col_idx):
+            metadata['columns'][i] = columns[num_col_idx[i]]
+        else:
+            metadata['columns'][i] = columns[cat_col_idx[i-len(num_col_idx)]]
+    
+
+    return new_real_data, new_syn_data, metadata

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/ef_vfm/models/flow_model.py

@@ -0,0 +1,195 @@
+import torch.nn.functional as F
+import torch
+import numpy as np
+from torchdiffeq import odeint_adjoint as odeint
+
+
+class ExpVFM(torch.nn.Module):
+    def __init__(
+            self,
+            num_classes: np.array,
+            num_numerical_features: int,
+            vf_fn,
+            device=torch.device('cpu'),
+            **kwargs
+        ):
+
+        super(ExpVFM, self).__init__()
+
+        self.num_numerical_features = num_numerical_features
+        self.num_classes = num_classes # it as a vector [K1, K2, ..., Km]
+        self.num_classes_expanded = torch.from_numpy(
+            np.concatenate([num_classes[i].repeat(num_classes[i]) for i in range(len(num_classes))])
+        ).to(device) if len(num_classes)>0 else torch.tensor([]).to(device).int()
+        self.neg_infinity = -1000000.0 
+
+        offsets = np.cumsum(self.num_classes)
+        offsets = np.append([0], offsets)
+        self.slices_for_classes = []
+        for i in range(1, len(offsets)):
+            self.slices_for_classes.append(np.arange(offsets[i - 1], offsets[i]))
+        self.offsets = torch.from_numpy(offsets).to(device)
+        
+        offsets = np.cumsum(self.num_classes) + np.arange(1, len(self.num_classes)+1)
+        offsets = np.append([0], offsets)
+
+        self._vf_fn = vf_fn
+        self.device = device
+    
+
+    def mixed_loss(self, x):
+        b = x.shape[0]
+        dev = x.device
+
+        x_num = x[:, :self.num_numerical_features]
+        x_cat = x[:, self.num_numerical_features:].long()
+
+        t = torch.rand(b, device=dev, dtype=x_num.dtype)
+        t = t[:, None]
+            
+        # Continuous interpolation
+        x_num_t = x_num
+        if x_num.shape[1] > 0:
+            noise = torch.randn_like(x_num)
+            x_num_t = t * x_num + (1 - t) * noise # + noise * sigma_num
+        
+        # Discrete interpolation
+        x_cat_oh = self.to_one_hot(x_cat).float()
+        x_cat_t = x_cat_oh
+        if x_cat.shape[1] > 0:
+            x_cat_t = t * x_cat_oh + (1 - t) * torch.randn_like(x_cat_oh)
+
+        # Predict orignal data (distribution)
+        model_out_num, model_out_cat = self._vf_fn(x_num_t, x_cat_t, t.squeeze())
+
+        d_loss = torch.zeros((1,)).float()
+        c_loss = torch.zeros((1,)).float()
+
+        # Compute the loss
+        if x_num.shape[1] > 0:
+            c_loss = self._mvgloss(model_out_num, x_num, t)
+
+        if x_cat.shape[1] > 0:
+            d_loss = self._absorbed_closs(model_out_cat, x_cat, self._vf_fn.categories)
+            
+        return d_loss.mean(), c_loss.mean()
+    
+    def _mvgloss(self, mu_t, x_num_t, t):
+        n, k = mu_t.shape
+        dev = mu_t.device
+        dt = mu_t.dtype
+
+        identity = torch.eye(k, device=dev, dtype=dt).unsqueeze(0).expand(n, -1, -1)
+        scale = 1 - (1 - 0.01) * t.unsqueeze(1) ** 2
+        sigma = scale * identity
+        dist = torch.distributions.MultivariateNormal(mu_t, sigma)
+        return -dist.log_prob(x_num_t).mean()
+
+    @torch.no_grad()
+    def sample(self, num_samples):
+        dev = self.device
+        dt = torch.float32
+        d_in = self.num_numerical_features + sum(self.num_classes)
+        d_out = self.num_numerical_features + len(self.num_classes)
+
+        x0 = torch.randn(num_samples, d_in, device=dev)
+        t = torch.tensor([0.0, 0.999]).to(dev)
+        vf = Velocity(self._vf_fn)
+        trajectory = odeint(vf, x0, t, method="dopri5", rtol=1e-5, atol=1e-5)
+        out = trajectory[1]
+
+        sample = torch.zeros(num_samples, d_out, device=dev, dtype=dt)
+        sample[:, :self.num_numerical_features] = out[:, :self.num_numerical_features].to(torch.float32)
+        if sum(self.num_classes) != 0:
+            idx = self.num_numerical_features
+            for i, val in enumerate(self.num_classes):
+                col = self.num_numerical_features + i
+                sample[:, col] = torch.argmax(out[:, idx:idx + val], dim=1)
+                idx += val
+                assert val >= sample[:, col].max() >= 0, f"Sampled value {sample[:, col].max()} is out of range for categorical feature {i} with {val} classes."
+
+        return sample.cpu()
+    
+    def sample_all(self, num_samples, batch_size, keep_nan_samples=False):        
+        b = batch_size
+
+        all_samples = []
+        num_generated = 0
+        while num_generated < num_samples:
+            print(f"Samples left to generate: {num_samples-num_generated}")
+            sample = self.sample(b)
+            mask_nan = torch.any(sample.isnan(), dim=1)
+            if keep_nan_samples:
+                # If the sample instances that contains Nan are decided to be kept, the row with Nan will be foreced to all zeros
+                sample = sample * (~mask_nan)[:, None]
+            else:
+                # Otherwise the instances with Nan will be eliminated
+                sample = sample[~mask_nan]
+
+            all_samples.append(sample)
+            num_generated += sample.shape[0]
+
+        x_gen = torch.cat(all_samples, dim=0)[:num_samples]
+
+        return x_gen
+
+    def to_one_hot(self, x_cat):
+        if len(self.num_classes) == 0:
+            return torch.zeros(x_cat.shape[0], 0, device=x_cat.device, dtype=torch.long)
+        x_cat_oh = torch.cat(
+            [F.one_hot(x_cat[:, i], num_classes=self.num_classes[i]) for i in range(len(self.num_classes))],
+            dim=-1
+        )
+        return x_cat_oh
+    
+    def _absorbed_closs(self, model_output, x0, cats): #, sigma, dsigma):
+        """
+            alpha: (bs,)
+        """
+        cum_sum =0
+        losses = torch.zeros(len(cats), device=model_output.device)
+        for i, val in enumerate(cats):
+            dist = torch.distributions.Categorical(logits=model_output[:, cum_sum:cum_sum+val])
+            losses[i] = -dist.log_prob(x0[:, i]).mean()
+            cum_sum += val
+
+        loss = losses.sum()
+        return loss
+    
+
+class Velocity(torch.nn.Module):
+    def __init__(self, model):
+        super(Velocity, self).__init__()
+        self.model = model
+
+    def forward(self, t, x):
+        t = t * torch.ones(x.shape[0]).to(x.device)
+
+        x_num = x[:, :self.model.d_numerical]
+        x_cat = x[:, self.model.d_numerical:]
+        mu, logits = self.model(x_num, x_cat, t)
+
+        # Numerical velocity
+        if self.model.d_numerical > 0:
+            v_num = (mu - (1 - 0.01) * x_num) / (1 - (1 - 0.01) * t.unsqueeze(1))
+        else:
+            v_num = torch.zeros_like(x_num)
+
+        # Categorical velocity: normalize logits into probability space before computing velocity
+        if len(self.model.categories) > 0:
+            v_cat_parts = []
+            logit_idx = 0
+            oh_idx = 0
+            for k in self.model.categories:
+                probs_k = F.softmax(logits[:, logit_idx:logit_idx + k], dim=-1)
+                x_k = x_cat[:, oh_idx:oh_idx + k]
+                v_k = (probs_k - (1 - 0.01) * x_k) / (1 - (1 - 0.01) * t.unsqueeze(1))
+                v_cat_parts.append(v_k)
+                logit_idx += k
+                oh_idx += k
+            v_cat = torch.cat(v_cat_parts, dim=1)
+        else:
+            v_cat = torch.zeros_like(x_cat)
+
+        v_t = torch.cat([v_num, v_cat], dim=1)
+        return v_t

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/ef_vfm/modules/main_modules.py

@@ -0,0 +1,102 @@
+from typing import Callable, Union
+
+from ef_vfm.modules.transformer import Reconstructor, Tokenizer, Transformer
+import torch
+import torch.nn as nn
+import torch.optim
+
+ModuleType = Union[str, Callable[..., nn.Module]]
+
+class SiLU(nn.Module):
+    def forward(self, x):
+        return x * torch.sigmoid(x)
+
+
+class PositionalEmbedding(torch.nn.Module):
+    def __init__(self, num_channels, max_positions=10000, endpoint=False):
+        super().__init__()
+        self.num_channels = num_channels
+        self.max_positions = max_positions
+        self.endpoint = endpoint
+
+    def forward(self, x):
+        freqs = torch.arange(start=0, end=self.num_channels//2, dtype=torch.float32, device=x.device)
+        freqs = freqs / (self.num_channels // 2 - (1 if self.endpoint else 0))
+        freqs = (1 / self.max_positions) ** freqs
+        x = x.ger(freqs.to(x.dtype))
+        x = torch.cat([x.cos(), x.sin()], dim=1)
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, d_in, dim_t = 512, use_mlp=True):
+        super().__init__()
+        self.dim_t = dim_t
+
+        self.proj = nn.Linear(d_in, dim_t)
+
+        self.mlp = nn.Sequential(
+            nn.Linear(dim_t, dim_t * 2),
+            nn.SiLU(),
+            nn.Linear(dim_t * 2, dim_t * 2),
+            nn.SiLU(),
+            nn.Linear(dim_t * 2, dim_t),
+            nn.SiLU(),
+            nn.Linear(dim_t, d_in),
+        ) if use_mlp else nn.Linear(dim_t, d_in)
+
+        self.map_noise = PositionalEmbedding(num_channels=dim_t)
+        self.time_embed = nn.Sequential(
+            nn.Linear(dim_t, dim_t),
+            nn.SiLU(),
+            nn.Linear(dim_t, dim_t)
+        )
+        
+        self.use_mlp = use_mlp
+    
+    def forward(self, x, timesteps):
+        emb = self.map_noise(timesteps)
+        emb = emb.reshape(emb.shape[0], 2, -1).flip(1).reshape(*emb.shape) # swap sin/cos
+        emb = self.time_embed(emb)
+    
+        x = self.proj(x) + emb
+        return self.mlp(x)
+    
+
+class UniModMLP(nn.Module):
+    """
+        Input:
+            x_num: [bs, d_numerical]
+            x_cat: [bs, len(categories)]
+        Output:
+            x_num_pred: [bs, d_numerical], the predicted mean for numerical data
+            x_cat_pred: [bs, sum(categories)], the predicted UNORMALIZED logits for categorical data
+    """
+    def __init__(
+            self, d_numerical, categories, num_layers, d_token,
+            n_head = 1, factor = 4, bias = True, dim_t=512, use_mlp=True,
+            activation='gelu', **kwargs
+        ):
+        super().__init__()
+        self.d_numerical = d_numerical
+        self.categories = categories
+
+        self.tokenizer = Tokenizer(d_numerical, categories, d_token, bias = bias)
+        self.encoder = Transformer(num_layers, d_token, n_head, d_token, factor, activation=activation)
+        d_in = d_token * (d_numerical + len(categories))
+        self.mlp = MLP(d_in, dim_t=dim_t, use_mlp=use_mlp)
+        self.decoder = Transformer(num_layers, d_token, n_head, d_token, factor, activation=activation)
+        self.detokenizer = Reconstructor(d_numerical, categories, d_token)
+        
+        self.model = nn.ModuleList([self.tokenizer, self.encoder, self.mlp, self.decoder, self.detokenizer])
+
+    def forward(self, x_num, x_cat, timesteps):
+        e = self.tokenizer(x_num, x_cat)
+        decoder_input = e[:, 1:, :]        # ignore the first CLS token. 
+        y = self.encoder(decoder_input)
+        pred_y = self.mlp(y.reshape(y.shape[0], -1), timesteps)
+        pred_e = self.decoder(pred_y.reshape(*y.shape))
+        x_num_pred, x_cat_pred = self.detokenizer(pred_e)
+        x_cat_pred = torch.cat(x_cat_pred, dim=-1) if len(x_cat_pred)>0 else torch.zeros_like(x_cat).to(x_num_pred.dtype)
+
+        return x_num_pred, x_cat_pred

syntheticFail/c15/tabbyflow/tabbyflow-c15-20260510_233513/_efvfm_runtime/ef_vfm/modules/transformer.py

@@ -0,0 +1,269 @@
+import torch
+import torch.nn as nn
+import torch.nn.init as nn_init
+import torch.nn.functional as F
+from torch import Tensor
+
+import math
+
+class Tokenizer(nn.Module):
+
+    def __init__(self, d_numerical, categories, d_token, bias):
+        super().__init__()
+        if categories is None:
+            d_bias = d_numerical
+            self.category_offsets = None
+            self.category_embeddings = None
+            self.n_categories = 0
+        else:
+            d_bias = d_numerical + len(categories)
+            category_offsets = torch.tensor([0] + list(categories[:-1])).cumsum(0)
+            category_ends = torch.tensor(list(categories)).cumsum(0)
+            self.register_buffer('category_offsets', category_offsets)
+            self.register_buffer('category_ends', category_ends)
+            self.cat_weight = nn.Parameter(Tensor(sum(categories), d_token))
+            nn.init.kaiming_uniform_(self.cat_weight, a=math.sqrt(5))
+            self.n_categories = len(categories)
+
+        # take [CLS] token into account
+        self.weight = nn.Parameter(Tensor(d_numerical + 1, d_token))
+        self.bias = nn.Parameter(Tensor(d_bias, d_token)) if bias else None
+        # The initialization is inspired by nn.Linear
+        nn_init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        if self.bias is not None:
+            nn_init.kaiming_uniform_(self.bias, a=math.sqrt(5))
+
+    @property
+    def n_tokens(self):
+        return len(self.weight) + (
+            0 if self.category_offsets is None else len(self.category_offsets)
+        )
+
+    def forward(self, x_num, x_cat):
+        x_some = x_num if x_cat is None else x_cat
+        assert x_some is not None
+        x_num = torch.cat(
+            [torch.ones(len(x_some), 1, device=x_some.device)]  # [CLS]
+            + ([] if x_num is None else [x_num]),
+            dim=1,
+        )
+
+        x = self.weight[None] * x_num[:, :, None]
+
+        if x_cat is not None and self.n_categories > 0:
+            # Vectorized categorical token computation: one matmul per category
+            cat_tokens = []
+            for start, end in zip(self.category_offsets, self.category_ends):
+                # x_cat[:, start:end] @ cat_weight[start:end] -> [batch, d_token]
+                cat_tokens.append(
+                    (x_cat[:, start:end] @ self.cat_weight[start:end]).unsqueeze(1)
+                )
+            x = torch.cat([x] + cat_tokens, dim=1)
+
+        if self.bias is not None:
+            bias = torch.cat(
+                [
+                    torch.zeros(1, self.bias.shape[1], device=x.device),
+                    self.bias,
+                ]
+            )
+            x = x + bias[None]
+
+        return x
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(self, d, n_heads, dropout, initialization = 'kaiming'):
+
+        if n_heads > 1:
+            assert d % n_heads == 0
+        assert initialization in ['xavier', 'kaiming']
+
+        super().__init__()
+        self.W_q = nn.Linear(d, d)
+        self.W_k = nn.Linear(d, d)
+        self.W_v = nn.Linear(d, d)
+        self.W_out = nn.Linear(d, d) if n_heads > 1 else None
+        self.n_heads = n_heads
+        self.dropout = nn.Dropout(dropout) if dropout else None
+
+        for m in [self.W_q, self.W_k, self.W_v]:
+            if initialization == 'xavier' and (n_heads > 1 or m is not self.W_v):
+                # gain is needed since W_qkv is represented with 3 separate layers
+                nn_init.xavier_uniform_(m.weight, gain=1 / math.sqrt(2))
+            nn_init.zeros_(m.bias)
+        if self.W_out is not None:
+            nn_init.zeros_(self.W_out.bias)
+
+    def _reshape(self, x):
+        batch_size, n_tokens, d = x.shape
+        d_head = d // self.n_heads
+        return (
+            x.reshape(batch_size, n_tokens, self.n_heads, d_head)
+            .transpose(1, 2)
+            .reshape(batch_size * self.n_heads, n_tokens, d_head)
+        )
+
+    def forward(self, x_q, x_kv, key_compression = None, value_compression = None):
+  
+        q, k, v = self.W_q(x_q), self.W_k(x_kv), self.W_v(x_kv)
+        for tensor in [q, k, v]:
+            assert tensor.shape[-1] % self.n_heads == 0
+        if key_compression is not None:
+            assert value_compression is not None
+            k = key_compression(k.transpose(1, 2)).transpose(1, 2)
+            v = value_compression(v.transpose(1, 2)).transpose(1, 2)
+        else:
+            assert value_compression is None
+
+        batch_size = len(q)
+        d_head_key = k.shape[-1] // self.n_heads
+        d_head_value = v.shape[-1] // self.n_heads
+        n_q_tokens = q.shape[1]
+
+        q = self._reshape(q)
+        k = self._reshape(k)
+
+        a = q @ k.transpose(1, 2)
+        b = math.sqrt(d_head_key)
+        attention = F.softmax(a/b , dim=-1)
+
+        
+        if self.dropout is not None:
+            attention = self.dropout(attention)
+        x = attention @ self._reshape(v)
+        x = (
+            x.reshape(batch_size, self.n_heads, n_q_tokens, d_head_value)
+            .transpose(1, 2)
+            .reshape(batch_size, n_q_tokens, self.n_heads * d_head_value)
+        )
+        if self.W_out is not None:
+            x = self.W_out(x)
+
+        return x
+        
+class Transformer(nn.Module):
+
+    def __init__(
+        self,
+        n_layers: int,
+        d_token: int,
+        n_heads: int,
+        d_out: int,
+        d_ffn_factor: int,
+        attention_dropout = 0.0,
+        ffn_dropout = 0.0,
+        residual_dropout = 0.0,
+        activation = 'relu',
+        prenormalization = True,
+        initialization = 'kaiming',      
+    ):
+        super().__init__()
+
+        def make_normalization():
+            return nn.LayerNorm(d_token)
+
+        d_hidden = int(d_token * d_ffn_factor)
+        self.layers = nn.ModuleList([])
+        for layer_idx in range(n_layers):
+            layer = nn.ModuleDict(
+                {
+                    'attention': MultiheadAttention(
+                        d_token, n_heads, attention_dropout, initialization
+                    ),
+                    'linear0': nn.Linear(
+                        d_token, d_hidden
+                    ),
+                    'linear1': nn.Linear(d_hidden, d_token),
+                    'norm1': make_normalization(),
+                }
+            )
+            if not prenormalization or layer_idx:
+                layer['norm0'] = make_normalization()
+   
+            self.layers.append(layer)
+
+        _activations = {
+            'relu': nn.ReLU,
+            'gelu': nn.GELU,
+            'silu': nn.SiLU,
+        }
+        if activation not in _activations:
+            raise ValueError(f"Unknown activation '{activation}'. Choose from: {list(_activations)}")
+        self.activation = _activations[activation]()
+        self.last_activation = _activations[activation]()
+        self.prenormalization = prenormalization
+        self.last_normalization = make_normalization() if prenormalization else None
+        self.ffn_dropout = ffn_dropout
+        self.residual_dropout = residual_dropout
+        self.head = nn.Linear(d_token, d_out)
+
+
+    def _start_residual(self, x, layer, norm_idx):
+        x_residual = x
+        if self.prenormalization:
+            norm_key = f'norm{norm_idx}'
+            if norm_key in layer:
+                x_residual = layer[norm_key](x_residual)
+        return x_residual
+
+    def _end_residual(self, x, x_residual, layer, norm_idx):
+        if self.residual_dropout:
+            x_residual = F.dropout(x_residual, self.residual_dropout, self.training)
+        x = x + x_residual
+        if not self.prenormalization:
+            x = layer[f'norm{norm_idx}'](x)
+        return x
+
+    def forward(self, x):
+        for layer_idx, layer in enumerate(self.layers):
+            is_last_layer = layer_idx + 1 == len(self.layers)
+
+            x_residual = self._start_residual(x, layer, 0)
+            x_residual = layer['attention'](
+                # for the last attention, it is enough to process only [CLS]
+                x_residual,
+                x_residual,
+            )
+
+            x = self._end_residual(x, x_residual, layer, 0)
+
+            x_residual = self._start_residual(x, layer, 1)
+            x_residual = layer['linear0'](x_residual)
+            x_residual = self.activation(x_residual)
+            if self.ffn_dropout:
+                x_residual = F.dropout(x_residual, self.ffn_dropout, self.training)
+            x_residual = layer['linear1'](x_residual)
+            x = self._end_residual(x, x_residual, layer, 1)
+        return x
+
+
+class Reconstructor(nn.Module):
+    def __init__(self, d_numerical, categories, d_token):
+        super(Reconstructor, self).__init__()
+
+        self.d_numerical = d_numerical
+        self.categories = categories
+        self.d_token = d_token
+        
+        self.weight = nn.Parameter(Tensor(d_numerical, d_token))  
+        nn.init.xavier_uniform_(self.weight, gain=1 / math.sqrt(2))
+        self.cat_recons = nn.ModuleList()
+
+        for d in categories:
+            recon = nn.Linear(d_token, d)
+            nn.init.xavier_uniform_(recon.weight, gain=1 / math.sqrt(2))
+            self.cat_recons.append(recon)
+
+    def forward(self, h):
+        h_num  = h[:, :self.d_numerical]
+        h_cat  = h[:, self.d_numerical:]
+
+        recon_x_num = torch.mul(h_num, self.weight.unsqueeze(0)).sum(-1)
+        recon_x_cat = []
+
+        for i, recon in enumerate(self.cat_recons):
+      
+            recon_x_cat.append(recon(h_cat[:, i]))
+
+        return recon_x_num, recon_x_cat

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