add runner

Signed-off-by: Zhiyuan Chen <[email protected]>

docs/docs/data/multitask.md

@@ -0,0 +1,9 @@
+---
+authors:
+  - Zhiyuan Chen
+date: 2024-05-04
+---
+
+# MultiTask
+
+::: multimolecule.data.multitask

docs/docs/runners/config.md

@@ -0,0 +1,9 @@
+---
+authors:
+  - Zhiyuan Chen
+date: 2024-05-04
+---
+
+# MultiMoleculeConfig
+
+::: multimolecule.runners.MultiMoleculeConfig

docs/docs/runners/index.md

@@ -0,0 +1,9 @@
+---
+authors:
+  - Zhiyuan Chen
+date: 2024-05-04
+---
+
+# runners
+
+--8<-- "multimolecule/runners/README.md:8:"

docs/docs/runners/runner.md

@@ -0,0 +1,9 @@
+---
+authors:
+  - Zhiyuan Chen
+date: 2024-05-04
+---
+
+# MultiMoleculeRunner
+
+::: multimolecule.runners.base_runner.BaseRunner

docs/mkdocs.yml

@@ -9,9 +9,14 @@ repo_url: https://github.com/DLS5-Omics/multimolecule
 
 nav:
   - index.md
+  - runners:
+      - runners/index.md
+      - MultiMoleculeRunner: runners/runner.md
+      - MultiMoleculeConfig: runners/config.md
   - data:
       - data/index.md
       - Dataset: data/dataset.md
+      - multitask: data/multitask.md
   - datasets:
       - datasets/index.md
       - DNA:

multimolecule/__init__.py

@@ -20,6 +20,7 @@
 # <https://multimolecule.danling.org/about/license-faq>.
 
 
+from .apis import evaluate, infer, train
 from .data import Dataset
 from .models import (
     AutoModelForContactPrediction,
@@ -130,6 +131,7 @@
     TokenKMerHead,
     TokenPredictionHead,
 )
+from .runners import MultiMoleculeConfig, MultiMoleculeRunner
 from .tasks import Task, TaskLevel, TaskType
 from .tokenisers import Alphabet, DnaTokenizer, DotBracketTokenizer, ProteinTokenizer, RnaTokenizer, Tokenizer
 from .utils import count_parameters

multimolecule/apis/__init__.py

@@ -0,0 +1,19 @@
+# MultiMolecule
+# Copyright (C) 2024-Present  MultiMolecule
+
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+from .run import evaluate, infer, train
+
+__all__ = ["train", "evaluate", "infer"]

multimolecule/apis/run.py

@@ -0,0 +1,115 @@
+# MultiMolecule
+# Copyright (C) 2024-Present  MultiMolecule
+
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+# mypy: disable-error-code="attr-defined"
+
+import atexit
+import os
+import warnings
+from typing import Type
+
+import danling as dl
+import torch
+
+from multimolecule.runners import MultiMoleculeConfig, MultiMoleculeRunner
+
+try:
+    import nni
+except ImportError:
+    nni = None
+
+
+def train(
+    config: MultiMoleculeConfig = None,  # type: ignore
+    runner_cls: Type[MultiMoleculeRunner] = MultiMoleculeRunner,
+):
+    if config is None:
+        config = MultiMoleculeConfig()
+    config = config.parse(default_config="config", no_default_config_action="warn")
+    config.interpolate(unsafe_eval=True)
+    config.training = True
+    if config.allow_tf32:
+        torch.backends.cudnn.allow_tf32 = True
+        torch.backends.cuda.matmul.allow_tf32 = True
+    if config.reduced_precision_reduction:
+        torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
+        torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
+    if config.get("nni", False):
+        if nni is None:
+            raise ValueError("Unable to retrieve nni parameters, since nni is not installed.")
+        config.merge(nni.get_next_parameter())
+    with dl.debug(config.get("debug", False)):
+        runner = runner_cls(config)
+        atexit.register(runner.print_result)
+        atexit.register(runner.save_result)
+        atexit.register(runner.save_checkpoint)
+        result = runner.train()
+        return result
+
+
+def evaluate(
+    config: MultiMoleculeConfig = None,  # type: ignore
+    runner_cls: Type[MultiMoleculeRunner] = MultiMoleculeRunner,
+):
+    if config is None:
+        config = MultiMoleculeConfig.empty()
+    config = config.parse(default_config="config", no_default_config_action="warn")
+    config.interpolate(unsafe_eval=True)
+    config.training = False
+    if config.allow_tf32:
+        torch.backends.cudnn.allow_tf32 = True
+        torch.backends.cuda.matmul.allow_tf32 = True
+    if config.reduced_precision_reduction:
+        torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
+        torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
+    if "checkpoint" not in config or not isinstance(config.checkpoint, str):
+        raise RuntimeError("Please specify `checkpoint` to run evaluate")
+    for name, data in config.datas.items():
+        if "evaluation" not in data or not isinstance(data.evaluate, str):
+            raise RuntimeError(f"Please specify `evaluation` to run evaluate in datas.{name}")
+    runner = runner_cls(config)
+    result = runner.evaluate_epoch("evaluation")
+    print(result)
+    return result
+
+
+def infer(
+    config: MultiMoleculeConfig = None,  # type: ignore
+    runner_cls: Type[MultiMoleculeRunner] = MultiMoleculeRunner,
+):
+    if config is None:
+        config = MultiMoleculeConfig.empty()
+    config = config.parse(default_config="config", no_default_config_action="warn")
+    config.interpolate(unsafe_eval=True)
+    config.training = False
+    if config.allow_tf32:
+        torch.backends.cudnn.allow_tf32 = True
+        torch.backends.cuda.matmul.allow_tf32 = True
+    if config.reduced_precision_reduction:
+        torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
+        torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
+    if "checkpoint" not in config or not isinstance(config.checkpoint, str):
+        raise RuntimeError("Please specify `checkpoint` to run infer.")
+    for name, data in config.datas.items():
+        if "inference" not in data or not isinstance(data.inference, str):
+            raise RuntimeError(f"Please specify `inference` to run infer in datas.{name}")
+    if "result_path" not in config or not isinstance(config.result_path, str):
+        config.result_path = os.path.join(os.getcwd(), "result.json")
+        warnings.warn("`result_path` is not specified, default to `result.json`.", RuntimeWarning, stacklevel=2)
+    runner = runner_cls(config)
+    result = runner.infer()
+    runner.save(result, config.result_path)
+    return result

multimolecule/apis/stat.py

@@ -0,0 +1,99 @@
+# MultiMolecule
+# Copyright (C) 2024-Present  MultiMolecule
+
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+import os
+import shutil
+from statistics import mean
+from typing import List
+
+import chanfig
+import pandas as pd
+from chanfig import NestedDict
+from tqdm import tqdm
+
+
+class Result(NestedDict):
+    pretrained: str
+    id: str
+    seed: int
+    epoch: int
+    validation: NestedDict
+    test: NestedDict
+
+
+def get_result_stat(experiment_root: str, remove_empty: bool = True) -> List[Result]:
+    results = []
+    for root, _, files in tqdm(os.walk(experiment_root)):
+        if "run.log" in files:
+            if "best.json" not in files:
+                if remove_empty:
+                    shutil.rmtree(root)
+                continue
+            best = NestedDict.from_json(os.path.join(root, "best.json"))
+            if "index" not in best:
+                if remove_empty:
+                    shutil.rmtree(root)
+                continue
+            config = NestedDict.from_yaml(os.path.join(root, "trainer.yaml"))
+            pretrained = config.pretrained.split("/")[-1]
+            seed = config.seed
+            result = Result(id=best.id, pretrained=pretrained, seed=seed)
+            result.validation = NestedDict(
+                {k: format(mean(v) if isinstance(v, list) else v, ".8f") for k, v in best.validation.all_items()}
+            )
+            result.test = NestedDict(
+                {k: format(mean(v) if isinstance(v, list) else v, ".8f") for k, v in best.test.all_items()}
+            )
+            result.epoch = best.index
+            result.pop("validation.time", None)
+            result.pop("test.time", None)
+            result.pop("validation.loss", None)
+            result.pop("test.loss", None)
+            result.pop("validation.lr", None)
+            result.pop("test.lr", None)
+            results.append(result)
+    # Remove empty directories, perform twice to remove all empty directories
+    if remove_empty:
+        for root, dirs, files in os.walk(experiment_root):
+            if not files and not dirs:
+                os.rmdir(root)
+        for root, dirs, files in os.walk(experiment_root):
+            if not files and not dirs:
+                os.rmdir(root)
+    results.sort(key=lambda x: (x.pretrained, x.seed, x.id))
+    return results
+
+
+def write_result_stat(results: List[Result], path: str):
+    results = [dict(result.all_items()) for result in results]  # type: ignore[misc]
+    df = pd.DataFrame.from_dict(results)
+    df.insert(len(df.keys()) - 1, "comment", "")
+    df.fillna("")
+    df.to_csv(path, index=False)
+
+
+class Config(chanfig.Config):
+    experiment_root: str = "experiments"
+    out_path: str = "result.csv"
+    remove_empty: bool = True
+
+
+if __name__ == "__main__":
+    config = Config().parse()
+    result_stat = get_result_stat(config.experiment_root, config.remove_empty)
+    if not len(result_stat) > 0:
+        raise ValueError("No results found")
+    write_result_stat(result_stat, config.out_path)

multimolecule/data/__init__.py

@@ -20,9 +20,13 @@
 # https://multimolecule.danling.org/about/license-faq
 
 from .dataset import Dataset
+from .multitask import DistributedMultiTaskSampler, MultiTaskDataset, MultiTaskSampler
 from .utils import no_collate
 
 __all__ = [
     "Dataset",
+    "MultiTaskDataset",
+    "MultiTaskSampler",
+    "DistributedMultiTaskSampler",
     "no_collate",
 ]