add profile frame and bf15/fp16 switch

README.md

@@ -84,6 +84,33 @@ python eval.py --config-name=pusht.yaml policy=pusht/lewm
 python eval.py --config-name=pusht.yaml policy=pusht/lewm_object.ckpt
 ```
 
+## Profiling
+
+`eval.py` now supports optional inference profiling with PyTorch's native profiler.
+
+Example:
+
+```bash
+python eval.py --config-name=pusht.yaml policy=pusht/lewm \
+  inference_precision=bf16 \
+  +profile.enabled=true \
+  +profile.with_stack=true \
+  +profile.record_shapes=true \
+  +profile.profile_memory=true
+```
+
+Supported inference precision modes:
+- `inference_precision=fp32`
+- `inference_precision=bf16`
+- `inference_precision=fp16`
+
+Outputs are written next to the evaluation results:
+- `torch_profile/key_averages.txt` for the aggregated operator table
+- `torch_profile/trace.json` for Chrome tracing
+- TensorBoard trace files under `torch_profile/`
+
+The trace includes custom regions such as `eval.world_evaluate_from_dataset`, `lewm.get_cost`, `lewm.rollout`, and `lewm.predict` to make the planning path easier to inspect.
+
 ## Pretrained Checkpoints
 
 Pre-trained checkpoints are available on [Google Drive](https://drive.google.com/drive/folders/1r31os0d4-rR0mdHc7OlY_e5nh3XT4r4e). Download the checkpoint archive and place the extracted files under `$STABLEWM_HOME/`.