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\n@codex ok fix \n**Summary**\n* Normalized legacy Toto cache metadata by always resolving the device token and mirroring it to the device_variant field so cached weights remain reusable. [src/models/toto_wrapper.pyL490-L509](https://github.com/lee101/stock-prediction/blob/29bd553dc19ddc431e0d1fac473f44af42cb8de0/src/models/toto_wrapper.py#L490-L509)\n\n**Testing**\n* ⚠️ `pytest tests/test_toto_wrapper.py` *(fails: PyTorch not installed in environment)*\n\n [View task →](https://chatgpt.com/s/cd_68fc1419ef248191808c7ae6d8309e7f)",{},"created",1761791117.051081,"Completing the 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forecasting wrapper that mirrors the Chronos interface while adding","torch.compile options, AMP controls, and GPU-aware retry logic.","from __future__ import annotations","import logging","import sys","import os","from pathlib import Path","from contextlib import nullcontext","from dataclasses import dataclass","from typing import Any, ContextManager, Dict, List, Optional, Tuple, TYPE_CHECKING, Union, cast","from ..dependency_injection import register_observer, resolve_numpy, resolve_torch","from .model_cache import ("," ModelCacheError,"," ModelCacheManager,"," device_to_token,"," dtype_to_token,",")","_REPO_ROOT = Path(__file__).resolve().parents[2]","_CANDIDATE_PATHS = ["," _REPO_ROOT / \"toto\","," _REPO_ROOT / \"toto\" / \"src\","," _REPO_ROOT / \"toto\" / \"build\" / \"lib\","," _REPO_ROOT / \"toto\" / \"toto\","," _REPO_ROOT / \"totoembedding\",","]","_LEGACY_PATH = Path(\"/mnt/fast/code/chronos-forecasting/toto\")","if _LEGACY_PATH.exists():"," _CANDIDATE_PATHS.append(_LEGACY_PATH)","for _path in reversed(_CANDIDATE_PATHS):"," if _path.exists():"," path_str = str(_path)"," if path_str not in sys.path:"," sys.path.insert(0, path_str)","_IMPORT_ERROR: Optional[Exception] = None","torch = resolve_torch()","np = resolve_numpy()","def _refresh_torch(module):"," global torch"," torch = module","def _refresh_numpy(module):"," global np"," np = module","register_observer(\"torch\", _refresh_torch)","register_observer(\"numpy\", _refresh_numpy)","if TYPE_CHECKING:"," from toto.data.util.dataset import MaskedTimeseries as MaskedTimeseriesType"," from toto.inference.forecaster import TotoForecaster as TotoForecasterType"," from toto.model.toto import Toto as TotoModelType","else:"," MaskedTimeseriesType = Any"," TotoForecasterType = Any"," TotoModelType = Any","try:"," from toto.data.util.dataset import MaskedTimeseries"," from toto.inference.forecaster import TotoForecaster"," from toto.model.toto import Toto","except ModuleNotFoundError:"," try: # pragma: no cover - compatibility with namespace installs"," from toto.toto.data.util.dataset import MaskedTimeseries # type: ignore"," from toto.toto.inference.forecaster import TotoForecaster # type: ignore"," from toto.toto.model.toto import Toto # type: ignore"," except ModuleNotFoundError as exc: # pragma: no cover - gracefully degrade when Toto missing entirely"," _IMPORT_ERROR = exc"," MaskedTimeseries = None # type: ignore"," TotoForecaster = None # type: ignore"," Toto = None # type: ignore"," except Exception as exc: # pragma: no cover - unexpected import errors","except Exception as exc: # pragma: no cover - allow graceful degradation when deps missing"," _IMPORT_ERROR = exc"," MaskedTimeseries = None # type: ignore"," TotoForecaster = None # type: ignore"," Toto = None # type: ignore","else: # pragma: no cover - executed when imports succeed"," _IMPORT_ERROR = None","logger = logging.getLogger(__name__)","# Enable tensor-core friendly defaults when possible.","if torch.cuda.is_available():"," torch.backends.cuda.matmul.allow_tf32 = True","torch.set_float32_matmul_precision(\"medium\")","@dataclass","class TotoForecast:"," \"\"\"Container for Toto forecast results compatible with Chronos outputs.\"\"\""," samples: np.ndarray"," def numpy(self) -> np.ndarray:"," \"\"\"Return samples in Chronos-compatible layout.\"\"\""," samples = self.samples"," if samples.ndim == 4 and samples.shape[0] == 1:"," samples = samples.squeeze(0)"," if samples.ndim == 3 and samples.shape[0] == 1:"," if samples.ndim == 2 and samples.shape[0] == 1:"," return samples.squeeze(0)"," if samples.ndim == 2:"," return samples.T"," return samples","def _is_cuda_oom(exc: BaseException) -> bool:"," \"\"\"Return True if the exception represents a CUDA OOM condition.\"\"\""," if isinstance(exc, torch.cuda.OutOfMemoryError):"," return True"," message = str(exc).lower()"," return \"out of memory\" in message","def _maybe_empty_cuda_cache(device: str) -> None:"," if device.startswith(\"cuda\") and torch.cuda.is_available():"," try:"," torch.cuda.empty_cache()"," except Exception as cache_exc: # pragma: no cover - best effort"," logger.debug(\"Failed to empty CUDA cache after OOM: %s\", cache_exc)","def _inference_context() -> ContextManager[None]:"," \"\"\"Return the best available inference context manager (inference_mode or no_grad).\"\"\""," context_ctor = getattr(torch, \"inference_mode\", None)"," if callable(context_ctor):"," return cast(ContextManager[None], context_ctor())"," return cast(ContextManager[None], torch.no_grad())","def _autocast_context(device: str, dtype: Optional[torch.dtype]) -> ContextManager[None]:"," if dtype is None:"," return cast(ContextManager[None], nullcontext())"," if device.startswith(\"cuda\"):"," autocast_fn = getattr(torch, \"autocast\", None)"," if callable(autocast_fn):"," return cast(ContextManager[None], autocast_fn(device_type=\"cuda\", dtype=dtype))"," return cast(ContextManager[None], torch.cuda.amp.autocast(dtype=dtype))"," return cast(ContextManager[None], nullcontext())","_CPU_LOW_PRECISION_DTYPES: Tuple[torch.dtype, ...] = tuple("," dtype"," for dtype in ("," getattr(torch, \"float16\", None),"," getattr(torch, \"bfloat16\", None),"," )"," if dtype is not None","def _normalise_inference_dtype("," device: str,"," requested: Optional[torch.dtype],",") -> Tuple[Optional[torch.dtype], Optional[torch.dtype]]:"," \"\"\"Normalise the requested dtype for the target device."," Returns a tuple of (effective_dtype, original_request). The effective dtype"," is the one that should be used for execution, while ``original_request``"," records the caller's preference for metadata."," \"\"\""," if requested is None:"," return None, None"," if device.startswith(\"cpu\") and requested in _CPU_LOW_PRECISION_DTYPES:"," return torch.float32, requested"," return requested, requested","def _forecast_with_retries("," forecaster,"," *,"," inputs,"," prediction_length: int,"," num_samples: int,"," samples_per_batch: int,"," autocast_dtype: Optional[torch.dtype],"," max_retries: int,"," min_samples_per_batch: int,"," min_num_samples: int,"," forecast_kwargs: Optional[dict] = None,","):"," Execute Toto forecasting with basic CUDA OOM recovery."," Returns the forecast together with the effective (num_samples, samples_per_batch)."," effective_kwargs = dict(forecast_kwargs or {})"," attempt = 0"," current_samples_per_batch = max(1, min(samples_per_batch, num_samples))"," current_num_samples = max(1, num_samples)"," last_error: Optional[Exception] = None"," while attempt <= max_retries:"," with _inference_context():"," with _autocast_context(device, autocast_dtype):"," forecast = forecaster.forecast("," inputs,"," prediction_length=prediction_length,"," num_samples=current_num_samples,"," samples_per_batch=current_samples_per_batch,"," **effective_kwargs,"," )"," return forecast, current_num_samples, current_samples_per_batch"," except Exception as exc:"," if not _is_cuda_oom(exc):"," raise"," last_error = exc"," logger.warning("," \"Toto forecast OOM (attempt %d/%d) with num_samples=%d, samples_per_batch=%d: %s\","," attempt + 1,"," max_retries + 1,"," current_num_samples,"," current_samples_per_batch,"," exc,"," )"," _maybe_empty_cuda_cache(device)"," attempt += 1"," next_samples_per_batch = max(min_samples_per_batch, current_samples_per_batch // 2)"," next_num_samples = current_num_samples"," if next_samples_per_batch == current_samples_per_batch:"," if current_num_samples > min_num_samples:"," next_num_samples = max(min_num_samples, current_num_samples // 2)"," else:"," next_num_samples = max(next_samples_per_batch, current_num_samples)"," if ("," next_samples_per_batch == current_samples_per_batch"," and next_num_samples == current_num_samples"," ):"," break"," current_samples_per_batch = next_samples_per_batch"," current_num_samples = next_num_samples"," raise RuntimeError("," f\"Toto forecasting failed after {max_retries + 1} attempts due to GPU OOM \""," f\"(last settings: num_samples={current_num_samples}, \""," f\"samples_per_batch={current_samples_per_batch}).\""," ) from last_error","class TotoPipeline:"," Wrapper class that mimics ChronosPipeline behaviour for Toto."," def __init__("," self,"," model: TotoModelType,"," device: str = \"cuda\","," *,"," torch_dtype: Optional[torch.dtype] = None,"," amp_dtype: Optional[torch.dtype] = torch.float16,"," max_oom_retries: int = 2,"," min_samples_per_batch: int = 32,"," min_num_samples: int = 256,"," compile_model: bool = True,"," torch_compile: bool = False,"," compile_mode: Optional[str] = \"max-autotune\","," compile_backend: Optional[str] = None,"," requested_dtype: Optional[torch.dtype] = None,"," ):"," if _IMPORT_ERROR is not None or MaskedTimeseries is None or TotoForecaster is None:"," raise RuntimeError("," \"Toto dependencies are not available; ensure toto and its requirements are installed\""," ) from _IMPORT_ERROR"," self.device = device"," self.max_oom_retries = max(0, int(max_oom_retries))"," self.min_samples_per_batch = max(1, int(min_samples_per_batch))"," self.min_num_samples = max(1, int(min_num_samples))"," normalised_dtype, fallback_requested = _normalise_inference_dtype(self.device, torch_dtype)"," if requested_dtype is None:"," requested_dtype = fallback_requested"," torch_dtype = normalised_dtype"," target_kwargs: Dict[str, Any] = {\"device\": self.device}"," if torch_dtype is not None:"," target_kwargs[\"dtype\"] = torch_dtype"," self.model = model.to(**target_kwargs)"," self.model.eval()"," first_param = next(self.model.parameters())"," self.model_dtype = first_param.dtype"," except StopIteration:"," self.model_dtype = torch_dtype or torch.float32"," self.effective_dtype_token = dtype_to_token(self.model_dtype)"," if requested_dtype is not None and self.model_dtype != requested_dtype:"," logger.info("," \"Adjusted Toto model dtype to %s for device %s (requested %s).\","," self.model_dtype,"," self.device,"," requested_dtype,"," self.requested_dtype = requested_dtype or self.model_dtype"," self.requested_dtype_token = dtype_to_token(self.requested_dtype)"," if device.startswith(\"cuda\"):"," self.amp_dtype = amp_dtype"," else:"," self.amp_dtype = None"," if self.amp_dtype is not None and device.startswith(\"cuda\"):"," self._autocast_dtype: Optional[torch.dtype] = self.amp_dtype"," elif device.startswith(\"cuda\") and torch_dtype in {torch.float16, torch.bfloat16}:"," self._autocast_dtype = torch_dtype"," self._autocast_dtype = None"," self._torch_compile_enabled = bool(torch_compile and hasattr(torch, \"compile\"))"," self._torch_compile_success = False"," self._compile_mode = compile_mode"," self._compile_backend = compile_backend"," self._compiled = False"," if self._torch_compile_enabled:"," if getattr(self.model, \"model\", None) is None:"," logger.warning(\"torch.compile requested but Toto model has no 'model' attribute.\")"," self._torch_compile_enabled = False"," compile_kwargs = {}"," if compile_mode:"," compile_kwargs[\"mode\"] = compile_mode"," if compile_backend:"," compile_kwargs[\"backend\"] = compile_backend"," try:"," compiled_core = torch.compile(self.model.model, **compile_kwargs) # type: ignore[arg-type]"," self.model.model = compiled_core # type: ignore[attr-defined]"," self._torch_compile_success = True"," self._compiled = True"," logger.info("," \"Enabled torch.compile for Toto model (mode=%s, backend=%s).\","," compile_mode,"," compile_backend,"," except Exception as exc:"," self._torch_compile_enabled = False"," logger.warning(\"torch.compile failed for Toto model: %s\", exc)"," if compile_model and not self._torch_compile_success:"," try:"," self.model.compile(mode=compile_mode) # type: ignore[attr-defined]"," else:"," self.model.compile() # type: ignore[attr-defined]"," self._compiled = True"," except AttributeError:"," if hasattr(torch, \"compile\"):"," compile_kwargs = {}"," if compile_mode:"," compile_kwargs[\"mode\"] = compile_mode"," if compile_backend:"," compile_kwargs[\"backend\"] = compile_backend"," try:"," self.model = torch.compile(self.model, **compile_kwargs) # type: ignore[assignment]"," self._compiled = True"," except Exception as exc:"," logger.debug(\"torch.compile fallback failed for Toto model: %s\", exc)"," except Exception as exc:"," logger.debug(\"Could not compile Toto model: %s\", exc)"," model_core = cast(Any, self.model)"," forecaster_ctor = cast(Any, TotoForecaster)"," self.forecaster = cast(TotoForecasterType, forecaster_ctor(model_core.model))"," self._last_run_metadata: Optional[dict] = None"," @property"," def compiled(self) -> bool:"," \"\"\"Return True if any compile step succeeded.\"\"\""," return self._compiled or self._torch_compile_success"," # ------------------------------------------------------------------ #"," # Internal warm-up helpers"," def _warmup("," sequence_length: int,"," prediction_length: int = 8,"," num_samples: int = 64,"," samples_per_batch: Optional[int] = None,"," ) -> None:"," \"\"\""," Execute a lightweight forward pass to pre-populate torch.compile / inductor caches."," if sequence_length <= 0:"," return"," samples_per_batch = samples_per_batch or min(num_samples, 64)"," context = torch.zeros(sequence_length, dtype=self.model_dtype, device=self.device)"," except Exception as exc: # pragma: no cover - defensive against device issues"," logger.debug(\"Skipping Toto warmup due to tensor allocation failure: %s\", exc)"," self.predict("," context=context,"," prediction_length=prediction_length,"," num_samples=num_samples,"," samples_per_batch=samples_per_batch,"," except Exception as exc: # pragma: no cover - warmup best effort"," logger.debug(\"Toto warmup prediction failed (best effort): %s\", exc)"," def last_run_metadata(self) -> Optional[dict]:"," \"\"\"Return details captured during the most recent forecast execution.\"\"\""," return self._last_run_metadata"," @classmethod"," def from_pretrained("," cls,"," model_id: str = \"Datadog/Toto-Open-Base-1.0\","," device_map: str = \"cuda\","," cache_policy: str = \"prefer\","," warmup_sequence: int = 512,"," force_refresh: bool = False,"," cache_manager: Optional[ModelCacheManager] = None,"," **kwargs: Any,"," ) -> \"TotoPipeline\":"," Load a pretrained Toto model and build a pipeline around it."," if _IMPORT_ERROR is not None or Toto is None:"," policy = cache_policy.lower()"," if policy not in {\"prefer\", \"never\", \"only\"}:"," raise ValueError(f\"Unrecognised cache policy '{cache_policy}'. Expected 'prefer', 'never', or 'only'.\")"," manager = cache_manager or ModelCacheManager(\"toto\")"," device = device_map if device_map != \"mps\" else \"cpu\""," torch_dtype, requested_dtype = _normalise_inference_dtype(device, torch_dtype)"," dtype_token = dtype_to_token(torch_dtype)"," amp_token = dtype_to_token(amp_dtype)"," device_token = device_to_token(device)"," extra_kwargs: Dict[str, Any] = dict(kwargs)"," pipeline_kwargs: Dict[str, Any] = {}"," for key in (\"max_oom_retries\", \"min_samples_per_batch\", \"min_num_samples\"):"," if key in extra_kwargs:"," pipeline_kwargs[key] = extra_kwargs.pop(key)"," model_kwargs: Dict[str, Any] = extra_kwargs"," metadata_requirements = {"," \"model_id\": model_id,"," \"dtype\": dtype_token,"," \"amp_dtype\": amp_token,"," \"compile_mode\": (compile_mode or \"none\"),"," \"compile_backend\": (compile_backend or \"none\"),"," \"torch_version\": torch.__version__,"," \"device\": device_token,"," \"device_variant\": device_token,"," }"," use_cache = policy != \"never\""," loaded_from_cache = False"," with manager.compilation_env(model_id, dtype_token, device_token):"," metadata = ("," manager.load_metadata(model_id, dtype_token, device_token)"," if use_cache"," else None"," if metadata:"," metadata = dict(metadata)"," if \"device\" in metadata:"," metadata[\"device\"] = device_to_token(metadata[\"device\"])"," elif \"device_variant\" in metadata:"," metadata[\"device\"] = device_to_token(metadata[\"device_variant\"])"," metadata.setdefault(\"device\", device_token)"," metadata.setdefault(\"device_variant\", metadata[\"device\"])"," model: TotoModelType"," use_cache"," and not force_refresh"," and metadata"," and manager.metadata_matches(metadata, metadata_requirements)"," cache_path = manager.load_pretrained_path(model_id, dtype_token, device_token)"," if cache_path is not None:"," model = cast("," TotoModelType,"," Toto.from_pretrained(str(cache_path), **model_kwargs),"," )"," loaded_from_cache = True"," logger.info("," \"Loaded Toto model '%s' (%s/%s) from compiled cache.\","," model_id,"," dtype_token,"," device_token,"," except Exception as exc: # pragma: no cover - backstop for unexpected load failures"," loaded_from_cache = False"," logger.warning("," \"Failed to load cached Toto weights from %s: %s\","," cache_path,"," exc,"," if policy == \"only\" and not loaded_from_cache:"," raise RuntimeError("," f\"Compiled Toto cache unavailable for model '{model_id}', dtype '{dtype_token}', \""," f\"device '{device_token}'. Run the model pre-warming utilities to generate cached weights.\""," )"," if not loaded_from_cache:"," model = cast(TotoModelType, Toto.from_pretrained(model_id, **model_kwargs))"," logger.info("," \"Loaded Toto model '%s' from source (cache_policy=%s).\","," model_id,"," policy,"," pipeline = cls("," model,"," device=device,"," torch_dtype=torch_dtype,"," amp_dtype=amp_dtype,"," max_oom_retries=int(pipeline_kwargs.get(\"max_oom_retries\", 2)),"," min_samples_per_batch=int(pipeline_kwargs.get(\"min_samples_per_batch\", 32)),"," min_num_samples=int(pipeline_kwargs.get(\"min_num_samples\", 256)),"," compile_model=compile_model,"," torch_compile=torch_compile,"," compile_mode=compile_mode,"," compile_backend=compile_backend,"," requested_dtype=requested_dtype,"," should_warmup = ("," warmup_sequence > 0"," and (compile_model or torch_compile or pipeline.compiled)"," and not loaded_from_cache"," if should_warmup:"," pipeline._warmup(sequence_length=warmup_sequence)"," if use_cache and (force_refresh or not loaded_from_cache):"," model_obj = getattr(pipeline, \"model\", None)"," if model_obj is not None:"," metadata_payload = {"," **metadata_requirements,"," \"device\": device_token,"," \"device_variant\": device_token,"," \"device_requested\": device,"," \"dtype\": pipeline.effective_dtype_token,"," \"dtype_requested\": pipeline.requested_dtype_token,"," \"compile_model\": bool(pipeline._compiled),"," \"torch_compile\": bool(pipeline._torch_compile_success),"," \"warmup_sequence\": int(warmup_sequence),"," }"," manager.persist_model_state("," model_id=model_id,"," dtype_token=dtype_token,"," model=model_obj,"," metadata=metadata_payload,"," force=force_refresh,"," variant_token=device_token,"," except ModelCacheError as exc:"," \"Failed to persist Toto cache for model '%s': %s\","," logger.debug(\"Toto pipeline model attribute missing; skipping cache persistence.\")"," return pipeline"," def predict("," context: Union[torch.Tensor, np.ndarray, List[float]],"," prediction_length: int,"," num_samples: int = 4096,"," temperature: float = 1.0,"," top_k: Optional[int] = None,"," top_p: Optional[float] = None,"," ) -> List[TotoForecast]:"," Generate forecasts using Toto with Chronos-compatible semantics."," _ = temperature, top_k, top_p # Compatibility placeholders."," if MaskedTimeseries is None:"," raise RuntimeError(\"Toto dependencies are not available; cannot build MaskedTimeseries inputs.\")"," if isinstance(context, (list, np.ndarray)):"," context = torch.tensor(context, dtype=torch.float32)"," context = context.to(self.device)"," if context.dtype != self.model_dtype:"," context = context.to(dtype=self.model_dtype)"," if context.dim() == 1:"," context = context.unsqueeze(0)"," batch_size = int(context.shape[0])"," seq_len = context.shape[-1]"," time_interval_seconds = int(kwargs.pop(\"time_interval_seconds\", 60 * 15))"," timestamp_seconds = torch.zeros("," context.shape[0],"," seq_len,"," device=self.device,"," dtype=torch.float32,"," )"," time_interval_tensor = torch.full("," (context.shape[0],),"," time_interval_seconds,"," inputs = MaskedTimeseries("," series=context,"," padding_mask=torch.ones_like(context, dtype=torch.bool),"," id_mask=torch.zeros_like(context, dtype=torch.int),"," timestamp_seconds=timestamp_seconds,"," time_interval_seconds=time_interval_tensor,"," samples_per_batch = int(kwargs.pop(\"samples_per_batch\", 512))"," samples_per_batch = max(1, min(samples_per_batch, num_samples))"," max_oom_retries = int(kwargs.pop(\"max_oom_retries\", self.max_oom_retries))"," min_samples_per_batch = int(kwargs.pop(\"min_samples_per_batch\", self.min_samples_per_batch))"," min_num_samples = int(kwargs.pop(\"min_num_samples\", self.min_num_samples))"," forecast_kwargs = kwargs if kwargs else None"," forecast, effective_num_samples, effective_samples_per_batch = _forecast_with_retries("," self.forecaster,"," inputs=inputs,"," prediction_length=prediction_length,"," num_samples=num_samples,"," samples_per_batch=samples_per_batch,"," autocast_dtype=self._autocast_dtype,"," max_retries=max_oom_retries,"," min_samples_per_batch=min_samples_per_batch,"," min_num_samples=min_num_samples,"," forecast_kwargs=forecast_kwargs,"," if ("," effective_num_samples != num_samples"," or effective_samples_per_batch != samples_per_batch"," ):"," \"Toto forecast adjusted sampling from num_samples=%d, samples_per_batch=%d \""," \"to num_samples=%d, samples_per_batch=%d due to OOM.\","," num_samples,"," samples_per_batch,"," effective_num_samples,"," effective_samples_per_batch,"," self._last_run_metadata = {"," \"num_samples_requested\": num_samples,"," \"num_samples_used\": effective_num_samples,"," \"samples_per_batch_requested\": samples_per_batch,"," \"samples_per_batch_used\": effective_samples_per_batch,"," \"torch_dtype\": str(self.model_dtype),"," \"torch_compile_requested\": self._torch_compile_enabled,"," \"torch_compile_success\": self._torch_compile_success,"," \"torch_compile_mode\": self._compile_mode,"," \"torch_compile_backend\": self._compile_backend,"," \"batch_size\": batch_size,"," if getattr(forecast, \"samples\", None) is None:"," raise RuntimeError(\"Toto forecaster returned no samples.\")"," samples = forecast.samples.detach().cpu().numpy()"," primary_axis = samples.shape[0]"," if primary_axis != batch_size and samples.ndim > 1 and samples.shape[1] == batch_size:"," samples = np.swapaxes(samples, 0, 1)"," primary_axis = samples.shape[0]"," if primary_axis != batch_size:"," \"Toto forecast samples tensor does not match the requested batch size.\""," forecasts: List[TotoForecast] = []"," for idx in range(batch_size):"," series_samples = samples[idx : idx + 1]"," forecasts.append(TotoForecast(samples=series_samples))"," return forecasts"," def unload(self) -> None:"," \"\"\"Release GPU resources held by the Toto pipeline.\"\"\""," model = getattr(self, \"model\", None)"," move_to_cpu = getattr(model, \"to\", None)"," if callable(move_to_cpu):"," move_to_cpu(\"cpu\")"," except Exception as exc: # pragma: no cover - defensive cleanup"," logger.debug(\"Failed to move Toto model to CPU during unload: %s\", exc)"," self.model = None"," self.forecaster = None"," if torch.cuda.is_available():"," torch.cuda.empty_cache()"," except Exception as exc: # pragma: no cover - best effort"," logger.debug(\"Failed to empty CUDA cache after Toto unload: %s\", exc)","message",[2864,2865,2866,2867],{"_735":46,"_46":2875},{"_735":2872,"_255":2273,"_2279":2873,"_2280":2874},{"_735":46,"_46":2871},{"_735":2868,"_2869":2870,"_2279":725,"_2280":870},"terminal_chunk_citation","terminal_chunk_id","acbbc4","\n\n**Testing**\n* ✅ `pytest tests/test_model_cache_variants.py tests/test_toto_cache.py tests/test_kronos_wrapper_unit.py`","repo_file_citation",490,505,"**Summary**\n* Defaulted legacy Toto cache metadata to the current device token and mirrored it into `device_variant`, ensuring legacy caches still satisfy device-aware requirements.","pr","pr_title","Add device-aware caching for Toto and Kronos (fix legacy device metadata fallback)","pr_message","## Summary\n- ensure legacy Toto cache metadata defaults the device token when it is absent so metadata matching still succeeds\n- mirror the resolved device token into the `device_variant` field when legacy metadata omits it\n\n## Testing\n- pytest tests/test_model_cache_variants.py tests/test_toto_cache.py tests/test_kronos_wrapper_unit.py","output_diff",{"_93":2881,"_2884":2885,"_129":130,"_2886":-5,"_495":2887,"_2888":2889,"_2890":725,"_2891":499,"_2892":725,"_2893":2894,"_2895":-5},"pre_apply_patch","repo_id","/workspace/stock-prediction","preapply_patch_base_commit_sha","diff --git a/src/models/toto_wrapper.py b/src/models/toto_wrapper.py\nindex 6e2456fd803b2d48c6aea6a61838e4d453a53045..6c2e60f635bd4337d8c02ef59f3b8c0f5810a0d8 100755\n--- a/src/models/toto_wrapper.py\n+++ b/src/models/toto_wrapper.py\n@@ -479,51 +479,52 @@ class TotoPipeline:\n metadata_requirements = {\n \"model_id\": model_id,\n \"dtype\": dtype_token,\n \"amp_dtype\": amp_token,\n \"compile_mode\": (compile_mode or \"none\"),\n \"compile_backend\": (compile_backend or \"none\"),\n \"torch_version\": torch.__version__,\n \"device\": device_token,\n \"device_variant\": device_token,\n }\n \n use_cache = policy != \"never\"\n loaded_from_cache = False\n with manager.compilation_env(model_id, dtype_token, device_token):\n metadata = (\n manager.load_metadata(model_id, dtype_token, device_token)\n if use_cache\n else None\n )\n if metadata:\n metadata = dict(metadata)\n if \"device\" in metadata:\n metadata[\"device\"] = device_to_token(metadata[\"device\"])\n elif \"device_variant\" in metadata:\n metadata[\"device\"] = device_to_token(metadata[\"device_variant\"])\n- metadata.setdefault(\"device_variant\", metadata.get(\"device\", device_token))\n+ metadata.setdefault(\"device\", device_token)\n+ metadata.setdefault(\"device_variant\", metadata[\"device\"])\n model: TotoModelType\n if (\n use_cache\n and not force_refresh\n and metadata\n and manager.metadata_matches(metadata, metadata_requirements)\n ):\n cache_path = manager.load_pretrained_path(model_id, dtype_token, device_token)\n if cache_path is not None:\n try:\n model = cast(\n TotoModelType,\n Toto.from_pretrained(str(cache_path), **model_kwargs),\n )\n loaded_from_cache = True\n logger.info(\n \"Loaded Toto model '%s' (%s/%s) from compiled cache.\",\n model_id,\n dtype_token,\n device_token,\n )\n except Exception as exc: # pragma: no cover - backstop for unexpected load failures\n loaded_from_cache = False\n logger.warning(\n \"Failed to load cached Toto weights from %s: %s\",\n","external_storage_diff",{"_2896":2897,"_2898":-5},"files_modified","lines_added","lines_removed","commit_message","Default legacy Toto metadata device token","additional_parent_commit_shas","file_id","file_00000000952061f79b32bb5f86e7ddbe","ttl",{"_93":496,"_42":85,"_97":496,"_2901":2902,"_2903":2904,"_2905":2906,"_133":-5,"_120":2907},[84],"input_items",[2923,2924,2925,2926,2927,2928],"external_post_config",{"_2908":2909,"_438":375,"_2910":477,"_449":-5,"_2884":432,"_2911":-5,"_2912":-5,"_2913":2914,"_2915":-5,"_2916":-5,"_2917":2918,"_2919":15,"_2920":59,"_2921":54,"_2922":15},"user_model_slug","slug",1761790826.854756,"installation_id",67132031,"pr_number","review_trigger","comment_id","comment_type","issue","pr_write_guard_token","pending_pr_reaction_id","pending_comment_reaction_id",311471753,"comment_if_no_findings","comment_on_error","share_url","is_parallel_review",{"_93":2862,"_97":496,"_719":2941},{"_484":485,"_486":487,"_2938":491,"_2939":130,"_493":-5,"_93":2940},{"_93":2929,"_486":2937,"_255":2273,"_2931":2932,"_2933":2934},{"_93":2929,"_486":2936,"_255":-5,"_2931":-5,"_2933":-5},{"_93":2929,"_486":2935,"_255":2273,"_2931":2932,"_2933":2934},{"_93":2929,"_486":2930,"_255":2273,"_2931":2932,"_2933":2934},"pull_request_comment","**Summary**\n* Normalized legacy Toto cache metadata by always resolving the device token and mirroring it to the device_variant field so cached weights remain reusable. [src/models/toto_wrapper.pyL490-L509](https://github.com/lee101/stock-prediction/blob/29bd553dc19ddc431e0d1fac473f44af42cb8de0/src/models/toto_wrapper.py#L490-L509)\n\n**Testing**\n* ⚠️ `pytest tests/test_toto_wrapper.py` *(fails: PyTorch not installed in environment)*\n\n [View task →](https://chatgpt.com/s/cd_68fc1419ef248191808c7ae6d8309e7f)","side","RIGHT","line",511,"@codex ok fix ","\n### 💡 Codex Review\n\nHere are some automated review suggestions for this pull request.\n \n\n

ℹ️ About Codex in GitHub

","**_{_{![P1 Badge](https://img.shields.io/badge/P1-orange?style=flat)}} Legacy Toto caches never match due to missing device key**\n\nWhen a cache created before this change is loaded, `load_metadata(..., device_token)` correctly falls back to the legacy `metadata.json`, but the normalization block only populates `device_variant` and leaves `device` unset. Because `metadata_requirements` always includes `{\"device\": device_token}`, `metadata_matches` will fail for every legacy cache even though the weights are still valid. The code then downloads and recompiles the model instead of re‑using the existing cache, defeating the intended backwards compatibility and forcing users to rebuild caches unnecessarily. Consider defaulting `metadata[\"device\"]` to the normalized token when it is absent so that existing caches can still be reused.\n\nUseful? React with 👍 / 👎.","base_ref","head_ref","pull_request_info",[2942],{"_735":46,"_46":2943},"@codex merge in main and re test and look ci failure here need to clone toto and external/kronos both from lee101/ I have forks on them... uv pip install -e . 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