[Feature] Improved experiment reloading and evaluation

.gitignore

@@ -173,3 +173,4 @@ cython_debug/
 #  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/
+!/examples/evaluate/outputs/

README.md

@@ -469,6 +469,15 @@ python benchmarl/run.py task=vmas/balance algorithm=mappo experiment.max_n_iters
 
 [![Example](https://img.shields.io/badge/Example-blue.svg)](examples/checkpointing/reload_experiment.py)
 
+
+There are also ways to **resume** and **evaluate** hydra experiments directly from the file
+```bash
+python benchmarl/evaluate.py ../outputs/2024-09-09/20-39-31/mappo_balance_mlp__cd977b69_24_09_09-20_39_31/checkpoints/checkpoint_100.pt
+```
+```bash
+python benchmarl/resume.py ../outputs/2024-09-09/20-39-31/mappo_balance_mlp__cd977b69_24_09_09-20_39_31/checkpoints/checkpoint_100.pt
+```
+
 ### Callbacks
 
 Experiments optionally take a list of [`Callback`](benchmarl/experiment/callback.py) which have several methods

benchmarl/conf/experiment/base_experiment.yaml

@@ -86,17 +86,22 @@ evaluation_episodes: 10
 evaluation_deterministic_actions: True
 
 # List of loggers to use, options are: wandb, csv, tensorboard, mflow
-loggers: []
+loggers: [csv]
 # Wandb project name
 project_name: "benchmarl"
 # Create a json folder as part of the output in the format of marl-eval
 create_json: True
 
 # Absolute path to the folder where the experiment will log.
 # If null, this will default to the hydra output dir (if using hydra) or to the current folder when the script is run (if not).
+# If you are reloading an experiment with "restore_file", this will default to the reloaded experiment folder.
 save_folder: null
 # Absolute path to a checkpoint file where the experiment was saved. If null the experiment is started fresh.
 restore_file: null
+# Map location given to `torch.load()` when reloading.
+# If you are reloading in a cpu-only machine a gpu experiment, you can use `restore_map_location: {"cuda":"cpu"}`
+# to map gpu tensors to the cpu
+restore_map_location: null
 # Interval for experiment saving in terms of collected frames (this should be a multiple of on/off_policy_collected_frames_per_batch).
 # Set it to 0 to disable checkpointing
 checkpoint_interval: 0

benchmarl/evaluate.py

@@ -0,0 +1,21 @@
+#  Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+#  This source code is licensed under the license found in the
+#  LICENSE file in the root directory of this source tree.
+#
+import argparse
+from pathlib import Path
+
+from benchmarl.hydra_config import reload_experiment_from_file
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Evaluates the experiment from a checkpoint file."
+    )
+    parser.add_argument(
+        "checkpoint_file", type=str, help="The name of the checkpoint file"
+    )
+    args = parser.parse_args()
+    checkpoint_file = str(Path(args.checkpoint_file).resolve())
+    experiment = reload_experiment_from_file(checkpoint_file)
+    experiment.evaluate()

benchmarl/experiment/experiment.py

@@ -14,7 +14,7 @@
 from collections import deque, OrderedDict
 from dataclasses import dataclass, MISSING
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Any, Dict, List, Optional
 
 import torch
 from tensordict import TensorDictBase
@@ -99,6 +99,7 @@ class ExperimentConfig:
 
     save_folder: Optional[str] = MISSING
     restore_file: Optional[str] = MISSING
+    restore_map_location: Optional[Any] = MISSING
     checkpoint_interval: int = MISSING
     checkpoint_at_end: bool = MISSING
     keep_checkpoints_num: Optional[int] = MISSING
@@ -506,33 +507,40 @@ def _setup_name(self):
         self.task_name = self.task.name.lower()
         self._checkpointed_files = deque([])
 
-        if self.config.restore_file is not None and self.config.save_folder is not None:
-            raise ValueError(
-                "Experiment restore file and save folder have both been specified."
-                "Do not set a save_folder when you are reloading an experiment as"
-                "it will by default reloaded into the old folder."
-            )
-        if self.config.restore_file is None:
-            if self.config.save_folder is not None:
-                folder_name = Path(self.config.save_folder)
+        if self.config.save_folder is not None:
+            # If the user specified a folder for the experiment we use that
+            save_folder = Path(self.config.save_folder)
+        else:
+            # Otherwise, if the user is restoring from a folder, we will save in the folder they are restoring from
+            if self.config.restore_file is not None:
+                save_folder = Path(
+                    self.config.restore_file
+                ).parent.parent.parent.resolve()
+            # Otherwise, the user is not restoring and did not specify a save_folder so we save in the hydra directory
+            # of the experiment or in the directory where the experiment was run (if hydra is not used)
             else:
                 if _has_hydra and HydraConfig.initialized():
-                    folder_name = Path(HydraConfig.get().runtime.output_dir)
+                    save_folder = Path(HydraConfig.get().runtime.output_dir)
                 else:
-                    folder_name = Path(os.getcwd())
+                    save_folder = Path(os.getcwd())
+
+        if self.config.restore_file is None:
             self.name = generate_exp_name(
                 f"{self.algorithm_name}_{self.task_name}_{self.model_name}", ""
             )
-            self.folder_name = folder_name / self.name
-            if (
-                len(self.config.loggers)
-                or self.config.checkpoint_interval > 0
-                or self.config.create_json
-            ):
-                self.folder_name.mkdir(parents=False, exist_ok=False)
+            self.folder_name = save_folder / self.name
+
         else:
-            self.folder_name = Path(self.config.restore_file).parent.parent.resolve()
-            self.name = self.folder_name.name
+            # If restoring, we use the name of the previous experiment
+            self.name = Path(self.config.restore_file).parent.parent.resolve().name
+            self.folder_name = save_folder / self.name
+
+        if (
+            len(self.config.loggers)
+            or self.config.checkpoint_interval > 0
+            or self.config.create_json
+        ):
+            self.folder_name.mkdir(parents=False, exist_ok=True)
 
     def _setup_logger(self):
         self.logger = Logger(
@@ -570,6 +578,15 @@ def run(self):
             self.close()
             raise err
 
+    def evaluate(self):
+        """Run just the evaluation loop once."""
+        self._evaluation_loop()
+        self.logger.commit()
+        print(
+            f"Evaluation results logged to loggers={self.config.loggers}"
+            f"{' and to a json file in the experiment folder.' if self.config.create_json else ''}"
+        )
+
     def _collection_loop(self):
         pbar = tqdm(
             initial=self.n_iters_performed,
@@ -883,6 +900,8 @@ def _save_experiment(self) -> None:
 
     def _load_experiment(self) -> Experiment:
         """Load trainer from checkpoint"""
-        loaded_dict: OrderedDict = torch.load(self.config.restore_file)
+        loaded_dict: OrderedDict = torch.load(
+            self.config.restore_file, map_location=self.config.restore_map_location
+        )
         self.load_state_dict(loaded_dict)
         return self

benchmarl/hydra_config.py

@@ -5,6 +5,7 @@
 #
 import importlib
 from dataclasses import is_dataclass
+from pathlib import Path
 
 from benchmarl.algorithms.common import AlgorithmConfig
 from benchmarl.environments import Task, task_config_registry
@@ -16,6 +17,7 @@
 _has_hydra = importlib.util.find_spec("hydra") is not None
 
 if _has_hydra:
+    from hydra import compose, initialize, initialize_config_dir
     from omegaconf import DictConfig, OmegaConf
 
 
@@ -121,3 +123,57 @@ def load_model_config_from_hydra(cfg: DictConfig) -> ModelConfig:
                 OmegaConf.to_container(cfg, resolve=True, throw_on_missing=True)
             )
         )
+
+
+def _find_hydra_folder(restore_file: str) -> str:
+    """Given the restore file, look for the .hydra folder max three levels above it."""
+    current_folder = Path(restore_file).parent.resolve()
+    for _ in range(3):
+        hydra_dir = current_folder / ".hydra"
+        if hydra_dir.exists() and hydra_dir.is_dir():
+            return str(hydra_dir)
+        current_folder = current_folder.parent
+    raise ValueError(
+        ".hydra folder not found (should be max 3 levels above checkpoint file"
+    )
+
+
+def reload_experiment_from_file(restore_file: str) -> Experiment:
+    """Reloads the experiment from a given restore file.
+
+    Requires a ``.hydra`` folder containing ``config.yaml``, ``hydra.yaml``, and ``overrides.yaml``
+    at max three directory levels higher than the checkpoint file. This should be automatically created by hydra.
+
+    Args:
+        restore_file (str): The checkpoint file of the experiment reload.
+
+    """
+    hydra_folder = _find_hydra_folder(restore_file)
+    with initialize(
+        version_base=None,
+        config_path="conf",
+    ):
+        cfg = compose(
+            config_name="config",
+            overrides=OmegaConf.load(Path(hydra_folder) / "overrides.yaml"),
+            return_hydra_config=True,
+        )
+        task_name = cfg.hydra.runtime.choices.task
+        algorithm_name = cfg.hydra.runtime.choices.algorithm
+    with initialize_config_dir(version_base=None, config_dir=hydra_folder):
+        cfg_loaded = dict(compose(config_name="config"))
+
+    for key in ("experiment", "algorithm", "task", "model", "critic_model"):
+        cfg[key].update(cfg_loaded[key])
+        cfg_loaded.pop(key)
+
+    cfg.update(cfg_loaded)
+    del cfg.hydra
+    cfg.experiment.restore_file = restore_file
+
+    print("\nReloaded experiment with:")
+    print(f"\nAlgorithm: {algorithm_name}, Task: {task_name}")
+    print("\nLoaded config:\n")
+    print(OmegaConf.to_yaml(cfg))
+
+    return load_experiment_from_hydra(cfg, task_name=task_name)

benchmarl/resume.py

@@ -0,0 +1,22 @@
+#  Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+#  This source code is licensed under the license found in the
+#  LICENSE file in the root directory of this source tree.
+#
+import argparse
+from pathlib import Path
+
+from benchmarl.hydra_config import reload_experiment_from_file
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Resumes the experiment from a checkpoint file."
+    )
+    parser.add_argument(
+        "checkpoint_file", type=str, help="The name of the checkpoint file"
+    )
+    args = parser.parse_args()
+    checkpoint_file = str(Path(args.checkpoint_file).resolve())
+
+    experiment = reload_experiment_from_file(checkpoint_file)
+    experiment.run()

docs/source/concepts/features.rst

@@ -38,18 +38,57 @@ Their checkpoints will be stored in a ``"checkpoints"`` folder within the experi
 
    python benchmarl/run.py task=vmas/balance algorithm=mappo experiment.max_n_iters=3 experiment.on_policy_collected_frames_per_batch=100 experiment.checkpoint_interval=100
 
+.. python_example_button::
+   https://github.com/facebookresearch/BenchMARL/blob/main/examples/checkpointing/reload_experiment.py
 
-To load from a checkpoint, pass the absolute checkpoint file name to ``experiment.restore_file``.
+Reloading
+---------
 
-.. code-block:: console
+To load from a checkpoint, you can do it in multiple ways:
 
-    python benchmarl/run.py task=vmas/balance algorithm=mappo experiment.max_n_iters=6 experiment.on_policy_collected_frames_per_batch=100 experiment.restore_file="/hydra/experiment/folder/checkpoint/checkpoint_300.pt"
+You can pass the absolute checkpoint file name to ``experiment.restore_file``.
 
+.. code-block:: console
 
+   python benchmarl/run.py task=vmas/balance algorithm=mappo experiment.max_n_iters=6 experiment.on_policy_collected_frames_per_batch=100 experiment.restore_file="/hydra/experiment/folder/checkpoint/checkpoint_300.pt"
 
 .. python_example_button::
    https://github.com/facebookresearch/BenchMARL/blob/main/examples/checkpointing/reload_experiment.py
 
+If you do not need to change the config, you can also just resume from the checkpoint file with:
+
+.. code-block:: console
+
+   python benchmarl/resume.py ../outputs/2024-09-09/20-39-31/mappo_balance_mlp__cd977b69_24_09_09-20_39_31/checkpoints/checkpoint_100.pt
+
+In Python, this is equivalent to:
+
+.. code-block:: python
+
+   from benchmarl.hydra_config import reload_experiment_from_file
+   experiment = reload_experiment_from_file(checkpoint_file)
+   experiment.run()
+
+
+Evaluating
+----------
+
+To evaluate an experiment, you can:
+
+.. code-block:: python
+
+   from benchmarl.hydra_config import reload_experiment_from_file
+   experiment = reload_experiment_from_file(checkpoint_file)
+   experiment.evaluate()
+
+This will run an iteration of evaluation, logging it to the experiment loggers (and to json if ``create_json==True``.
+
+There is a command line script which automates this:
+
+.. code-block:: console
+
+   python benchmarl/evaluate.py ../outputs/2024-09-09/20-39-31/mappo_balance_mlp__cd977b69_24_09_09-20_39_31/checkpoints/checkpoint_100.pt
+
 
 Callbacks
 ---------

examples/checkpointing/reload_experiment.py

@@ -38,13 +38,13 @@
     )
     experiment.run()
 
-    # Now we tell it where to restore  from
+    # Now we tell it where to restore from
     experiment_config.restore_file = (
         experiment.folder_name
         / "checkpoints"
         / f"checkpoint_{experiment.total_frames}.pt"
     )
-    # The experiment will be saved in the ame folder as the one it is restoring from
+    # The experiment will be saved in the same folder as the one it is restoring from
     experiment_config.save_folder = None
     # Let's do 3 more iters
     experiment_config.max_n_iters += 3

examples/checkpointing/reload_experiment.sh

@@ -7,4 +7,4 @@
 #
 
 python benchmarl/run.py task=vmas/balance algorithm=mappo experiment.max_n_iters=3 experiment.on_policy_collected_frames_per_batch=100 experiment.checkpoint_interval=100
-python benchmarl/run.py task=vmas/balance algorithm=mappo experiment.max_n_iters=6 experiment.on_policy_collected_frames_per_batch=100 experiment.restore_file="/hydra/experiment/folder/checkpoint/checkpoint_300.pt"
+python benchmarl/run.py task=vmas/balance algorithm=mappo experiment.max_n_iters=6 experiment.on_policy_collected_frames_per_batch=100 experiment.restore_file="/hydra_experiment_folder/checkpoint/checkpoint_300.pt"

examples/checkpointing/resume_experiment.sh

@@ -0,0 +1,10 @@
+#
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+#
+
+python benchmarl/run.py task=vmas/balance algorithm=mappo experiment.max_n_iters=3 experiment.on_policy_collected_frames_per_batch=100 experiment.checkpoint_interval=100
+python benchmarl/resume.py /hydra_experiment_folder/checkpoint/checkpoint_200.pt

examples/evaluating/evalaute_experiment.py

@@ -0,0 +1,26 @@
+#  Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+#  This source code is licensed under the license found in the
+#  LICENSE file in the root directory of this source tree.
+#
+
+from pathlib import Path
+
+from benchmarl.hydra_config import reload_experiment_from_file
+
+if __name__ == "__main__":
+
+    # Let's assume that we have run an experiment with
+    # `python benchmarl/run.py task=vmas/balance algorithm=mappo experiment.max_n_iters=2 experiment.on_policy_collected_frames_per_batch=100 experiment.checkpoint_interval=100`
+    # and we have obtained
+    # "outputs/2024-09-09/20-39-31/mappo_balance_mlp__cd977b69_24_09_09-20_39_31/checkpoints/checkpoint_100.pt""
+
+    # Now we tell it where to restore from
+    current_folder = Path(__file__).parent.absolute()
+    restore_file = (
+        current_folder
+        / "outputs/2024-09-09/20-39-31/mappo_balance_mlp__cd977b69_24_09_09-20_39_31/checkpoints/checkpoint_100.pt"
+    )
+
+    experiment = reload_experiment_from_file(str(restore_file))
+    experiment.evaluate()