fix(signal_v2-phase3b): use BaseChronosPipeline for new model architectures

ChronosPipeline (legacy T5) does not support amazon/chronos-2 or
chronos-bolt-* (input_patch_size). Switch to BaseChronosPipeline
which auto-detects variant and returns the appropriate sub-pipeline
(ChronosBoltPipeline / Chronos2Pipeline / ChronosPipeline).

Also handle the dtype kwarg deprecation: try newer `dtype=` first,
fall back to `torch_dtype=` for older versions.

Test mock_pipeline fixture updated to patch BaseChronosPipeline.

45/45 tests pass. Verified amazon/chronos-bolt-base loads on CUDA.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

signal_v2/chronos_predictor.py

@@ -24,16 +24,29 @@ class ChronosPredictor:
     """HuggingFace Chronos-2 zero-shot forecaster."""
 
     def __init__(self, model_name: str = "amazon/chronos-2", device: str | None = None):
-        from chronos import ChronosPipeline
+        # BaseChronosPipeline auto-detects model variant (Chronos / ChronosBolt / Chronos-2)
+        # and returns the appropriate sub-pipeline. ChronosPipeline only supports legacy T5.
         import torch
+        try:
+            from chronos import BaseChronosPipeline
+            pipeline_cls = BaseChronosPipeline
+        except ImportError:
+            from chronos import ChronosPipeline
+            pipeline_cls = ChronosPipeline
 
         self._device = device or ("cuda" if torch.cuda.is_available() else "cpu")
-        logger.info("Loading Chronos pipeline: %s on %s", model_name, self._device)
-        self._pipeline = ChronosPipeline.from_pretrained(
-            model_name,
-            device_map=self._device,
-            torch_dtype=torch.float16 if self._device == "cuda" else torch.float32,
-        )
+        dtype = torch.float16 if self._device == "cuda" else torch.float32
+        logger.info("Loading Chronos pipeline: %s on %s (cls=%s)",
+                    model_name, self._device, pipeline_cls.__name__)
+        # Try `dtype` (newer API) first, fall back to `torch_dtype` (older)
+        try:
+            self._pipeline = pipeline_cls.from_pretrained(
+                model_name, device_map=self._device, dtype=dtype,
+            )
+        except TypeError:
+            self._pipeline = pipeline_cls.from_pretrained(
+                model_name, device_map=self._device, torch_dtype=dtype,
+            )
         logger.info("Chronos pipeline loaded.")
 
     def predict_batch(