Add evaluation timings

src/metatrain/cli/eval.py

@@ -1,6 +1,7 @@
 import argparse
 import itertools
 import logging
+import time
 from pathlib import Path
 from typing import Dict, List, Optional, Union
 
@@ -161,11 +162,14 @@ def _eval_targets(
     """Evaluates an exported model on a dataset and prints the RMSEs for each target.
     Optionally, it also returns the predictions of the model.
 
+    The total and per-atom timings for the evaluation are also printed.
+
     Wraps around metatrain.cli.evaluate_model.
     """
 
     if len(dataset) == 0:
         logger.info("This dataset is empty. No evaluation will be performed.")
+        return
 
     # Attach neighbor lists to the systems:
     # TODO: these might already be present... find a way to avoid recomputing
@@ -194,6 +198,10 @@ def _eval_targets(
     if return_predictions:
         all_predictions = []
 
+    # Set up timings:
+    total_time = 0.0
+    timings_per_atom = []
+
     # Evaluate the model
     for batch in dataloader:
         systems, batch_targets = batch
@@ -202,13 +210,21 @@ def _eval_targets(
             key: value.to(dtype=dtype, device=device)
             for key, value in batch_targets.items()
         }
+
+        start_time = time.time()
+
         batch_predictions = evaluate_model(
             model,
             systems,
             options,
             is_training=False,
             check_consistency=check_consistency,
         )
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+        end_time = time.time()
+
         batch_predictions = average_by_num_atoms(
             batch_predictions, systems, per_structure_keys=[]
         )
@@ -219,6 +235,10 @@ def _eval_targets(
         if return_predictions:
             all_predictions.append(batch_predictions)
 
+        time_taken = end_time - start_time
+        total_time += time_taken
+        timings_per_atom.append(time_taken / sum(len(system) for system in systems))
+
     # Finalize the RMSEs
     rmse_values = rmse_accumulator.finalize(not_per_atom=["positions_gradients"])
     # print the RMSEs with MetricLogger
@@ -229,6 +249,13 @@ def _eval_targets(
     )
     metric_logger.log(rmse_values)
 
+    # Log timings
+    mean_time_per_atom = sum(timings_per_atom) / len(timings_per_atom)
+    logger.info(
+        f"evaluation time: {total_time:.2f} s "
+        f"[{1000.0*mean_time_per_atom:.2f} ms per atom]"
+    )
+
     if return_predictions:
         # concatenate the TensorMaps
         all_predictions_joined = _concatenate_tensormaps(all_predictions)

tests/cli/test_eval_model.py

@@ -68,6 +68,8 @@ def test_eval(monkeypatch, tmp_path, caplog, model_name, options):
     log = "".join([rec.message for rec in caplog.records])
     assert "energy RMSE (per atom)" in log
     assert "dataset with index" not in log
+    assert "evaluation time" in log
+    assert "ms per atom" in log
 
     # Test file is written predictions
     frames = ase.io.read("foo.xyz", ":")