在正式测试前添加warm up

config/eval/cube.yaml

@@ -38,6 +38,9 @@ eval:
   eval_budget: 50
   img_size: 224
   save_video: false
+  compile_warmup:
+    enabled: true
+    num_eval: 1
   dataset_name: ogbench/cube_single_expert
   callables:
     # -- set state

config/eval/pusht.yaml

@@ -33,6 +33,9 @@ eval:
   eval_budget: 50
   img_size: 224
   save_video: false
+  compile_warmup:
+    enabled: true
+    num_eval: 1
   dataset_name: pusht_expert_train
   callables:
     # -- set state

config/eval/reacher.yaml

@@ -32,6 +32,9 @@ eval:
   eval_budget: 50
   img_size: 224
   save_video: false
+  compile_warmup:
+    enabled: true
+    num_eval: 1
   dataset_name: dmc/reacher_random
   callables:
     # -- set state

config/eval/tworoom.yaml

@@ -32,6 +32,9 @@ eval:
   eval_budget: 50
   img_size: 224
   save_video: false
+  compile_warmup:
+    enabled: true
+    num_eval: 1
   dataset_name: tworoom
   callables:
     # -- set state

eval.py

@@ -7,6 +7,7 @@ import time
 import traceback
 from contextlib import nullcontext
 from pathlib import Path
+import tempfile
 
 import hydra
 import numpy as np
@@ -84,6 +85,17 @@ def get_compile_cfg(cfg):
     return compile_cfg
 
 
+def get_compile_warmup_cfg(cfg):
+    warmup_cfg = {
+        "enabled": True,
+        "num_eval": 1,
+    }
+    cfg_warmup = cfg.get("compile_warmup")
+    if cfg_warmup is not None:
+        warmup_cfg.update(OmegaConf.to_container(cfg_warmup, resolve=True))
+    return warmup_cfg
+
+
 def maybe_compile_inference_target(model, cfg, device):
     compile_cfg = get_compile_cfg(cfg)
     compile_target = "disabled"
@@ -363,23 +375,27 @@ def run_eval_subset(
 
     if str(device).startswith("cuda") and torch.cuda.is_available():
         torch.cuda.synchronize()
+
+    def evaluate_subset(episodes, start_indices, *, eval_cfg=local_cfg):
+        return world.evaluate_from_dataset(
+            dataset,
+            start_steps=list(start_indices),
+            goal_offset_steps=eval_cfg.eval.goal_offset_steps,
+            eval_budget=eval_cfg.eval.eval_budget,
+            episodes_idx=list(episodes),
+            callables=OmegaConf.to_container(
+                eval_cfg.eval.get("callables"), resolve=True
+            ),
+            save_video=bool(eval_cfg.eval.get("save_video", False)),
+            video_path=output_dir,
+        )
+
     start_time = time.time()
     with get_eval_grad_context(solver):
         with profiler_ctx as profiler:
             with inference_ctx:
                 with torch.profiler.record_function("eval.world_evaluate_from_dataset"):
-                    metrics = world.evaluate_from_dataset(
-                        dataset,
-                        start_steps=list(eval_start_idx),
-                        goal_offset_steps=local_cfg.eval.goal_offset_steps,
-                        eval_budget=local_cfg.eval.eval_budget,
-                        episodes_idx=list(eval_episodes),
-                        callables=OmegaConf.to_container(
-                            local_cfg.eval.get("callables"), resolve=True
-                        ),
-                        save_video=bool(local_cfg.eval.get("save_video", False)),
-                        video_path=output_dir,
-                    )
+                    metrics = evaluate_subset(eval_episodes, eval_start_idx)
     if str(device).startswith("cuda") and torch.cuda.is_available():
         torch.cuda.synchronize()
     evaluation_time = time.time() - start_time
@@ -396,6 +412,38 @@ def run_eval_subset(
     }
 
 
+def maybe_run_compile_warmup(cfg, eval_episodes, eval_start_idx):
+    warmup_cfg = get_compile_warmup_cfg(cfg)
+    if not warmup_cfg["enabled"]:
+        return
+
+    if get_multi_gpu_cfg(cfg)["enabled"]:
+        print("Skipping compile warmup because multi_gpu.enabled=true uses spawned workers.")
+        return
+
+    warmup_count = min(int(warmup_cfg["num_eval"]), len(eval_episodes))
+    if warmup_count < 1:
+        return
+
+    warmup_eval_cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=False))
+    warmup_eval_cfg.eval.num_eval = warmup_count
+    warmup_eval_cfg.eval.save_video = False
+    if warmup_eval_cfg.get("profile") is None:
+        warmup_eval_cfg.profile = OmegaConf.create({"enabled": False})
+    else:
+        warmup_eval_cfg.profile.enabled = False
+
+    with tempfile.TemporaryDirectory(prefix="lewm_compile_warmup_") as tmpdir:
+        run_eval_subset(
+            warmup_eval_cfg,
+            eval_episodes[:warmup_count].tolist(),
+            eval_start_idx[:warmup_count].tolist(),
+            Path(tmpdir),
+            enable_profile=False,
+        )
+
+
+
 def _multi_gpu_eval_worker(
     cfg_container,
     eval_episodes,
@@ -515,6 +563,8 @@ def run(cfg: DictConfig):
     output_dir = Path.cwd().resolve()
     profile_cfg = get_profile_cfg(cfg)
 
+    maybe_run_compile_warmup(cfg, eval_episodes, eval_start_idx)
+
     if get_multi_gpu_cfg(cfg)["enabled"]:
         if profile_cfg["enabled"]:
             raise ValueError("Profiling is not supported together with multi_gpu.enabled=true")