没啥用

.venv/lib/python3.10/site-packages/stable_worldmodel/policy.py

@@ -1,5 +1,6 @@
 from collections import deque
 from dataclasses import dataclass
+import inspect
 from pathlib import Path
 from typing import Any, Protocol
 from collections.abc import Callable
@@ -130,7 +131,37 @@ class BasePolicy:
                 info_dict[k] = v
         return info_dict
 
-    def _prepare_info(self, info_dict: dict) -> dict[str, torch.Tensor]:
+    def _prepare_info_for_device(
+        self,
+        info_dict: dict,
+        device: torch.device | str | None = None,
+    ) -> dict[str, torch.Tensor]:
+        if device is None:
+            return self._prepare_info(info_dict)
+
+        prepare_info = self._prepare_info
+        try:
+            signature = inspect.signature(prepare_info)
+        except (TypeError, ValueError):
+            return prepare_info(info_dict)
+
+        accepts_device = any(
+            param.kind == inspect.Parameter.VAR_KEYWORD
+            or (param.name == "device" and param.kind in {
+                inspect.Parameter.POSITIONAL_OR_KEYWORD,
+                inspect.Parameter.KEYWORD_ONLY,
+            })
+            for param in signature.parameters.values()
+        )
+        if accepts_device:
+            return prepare_info(info_dict, device=device)
+        return prepare_info(info_dict)
+
+    def _prepare_info(
+        self,
+        info_dict: dict,
+        device: torch.device | str | None = None,
+    ) -> dict[str, torch.Tensor]:
         """Pre-process and transform observations.
 
         Applies preprocessing (via `self.process`) and transformations (via `self.transform`)
@@ -145,6 +176,7 @@ class BasePolicy:
         Raises:
             ValueError: If an expected numpy array is missing for processing.
         """
+        target_device = torch.device(device) if device is not None else None
         for k, v in info_dict.items():
             is_numpy = isinstance(v, (np.ndarray | np.generic))
 
@@ -178,10 +210,20 @@ class BasePolicy:
                         )
                     v = torch.from_numpy(v)
                     is_numpy = False
+                moved_for_transform = False
+                if target_device is not None and target_device.type != "cpu":
+                    if v.device != target_device:
+                        v = v.to(target_device, non_blocking=True)
+                        moved_for_transform = True
                 if k.startswith('pixels') or k.startswith('goal'):
                     # Vectorized image transform on the full batch.
                     v = v.permute(0, 3, 1, 2)
-                v = self.transform[k](v)
+                try:
+                    v = self.transform[k](v)
+                except (NotImplementedError, RuntimeError):
+                    if not moved_for_transform:
+                        raise
+                    v = self.transform[k](v.cpu())
 
                 if shape is not None:
                     v = v.reshape(*shape[:2], *v.shape[1:])
@@ -189,7 +231,12 @@ class BasePolicy:
             if is_numpy and v.dtype.kind not in 'USO':
                 v = torch.from_numpy(v)
 
-            if torch.is_tensor(v) and v.device.type == "cpu" and not v.is_pinned():
+            if (
+                torch.cuda.is_available()
+                and torch.is_tensor(v)
+                and v.device.type == "cpu"
+                and not v.is_pinned()
+            ):
                 v = v.pin_memory()
 
             info_dict[k] = v
@@ -313,7 +360,9 @@ class FeedForwardPolicy(BasePolicy):
         assert 'goal' in info_dict, "'goal' must be provided in info_dict"
 
         # Prepare the info dict (transforms and normalizes inputs)
-        info_dict = self._prepare_info(info_dict)
+        info_dict = self._prepare_info_for_device(
+            info_dict, device=next(self.model.parameters()).device
+        )
 
         # Add goal_pixels key for GCBC model
         if 'goal' in info_dict:
@@ -422,7 +471,9 @@ class WorldModelPolicy(BasePolicy):
             assert 'pixels' in info_dict, "'pixels' must be provided in info_dict"
             assert 'goal' in info_dict, "'goal' must be provided in info_dict"
 
-            info_dict = self._prepare_info(info_dict)
+            info_dict = self._prepare_info_for_device(
+                info_dict, device=self.solver.device
+            )
             info_dict = self._move_info_to_device(info_dict, self.solver.device)
 
             outputs = self.solver(

eval.py

@@ -91,6 +91,10 @@ def get_compile_warmup_cfg(cfg):
         "num_eval": 1,
     }
     cfg_warmup = cfg.get("compile_warmup")
+    if cfg_warmup is None:
+        cfg_eval = cfg.get("eval")
+        if cfg_eval is not None:
+            cfg_warmup = cfg_eval.get("compile_warmup")
     if cfg_warmup is not None:
         warmup_cfg.update(OmegaConf.to_container(cfg_warmup, resolve=True))
     return warmup_cfg
@@ -464,6 +468,7 @@ def run_eval_subset(
     *,
     device_override: str | None = None,
     enable_profile: bool = True,
+    enable_compile_warmup: bool = False,
     before_evaluate=None,
 ):
     local_cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=False))
@@ -545,6 +550,8 @@ def run_eval_subset(
     with get_eval_grad_context(solver):
         with profiler_ctx as profiler:
             with inference_ctx:
+                if enable_compile_warmup:
+                    maybe_run_compile_warmup(cfg, eval_episodes, eval_start_idx)
                 with torch.profiler.record_function("eval.world_evaluate_from_dataset"):
                     metrics = evaluate_subset(eval_episodes, eval_start_idx)
     if str(device).startswith("cuda") and torch.cuda.is_available():
@@ -596,8 +603,8 @@ def maybe_run_compile_warmup(cfg, eval_episodes, eval_start_idx):
     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(),
+            list(eval_episodes[:warmup_count]),
+            list(eval_start_idx[:warmup_count]),
             Path(tmpdir),
             device_override=device_override,
             enable_profile=False,