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@@ -6,10 +6,11 @@ ENV PORT=7860
 ENV CUDA_VISIBLE_DEVICES=""
 ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1
 ENV HF_HUB_DISABLE_TELEMETRY=1
-ENV TIMESFM_BACKEND=baseline_cpu
+ENV TIMESFM_BACKEND=hf_cpu
 ENV TIMESFM_MAX_CONTEXT_LENGTH=512
 ENV TIMESFM_MAX_HORIZON_STEP=288
 ENV TIMESFM_MIN_REQUIRED_POINTS=32
+ENV TIMESFM_ALLOW_BASELINE_FALLBACK=false
 ENV UV_SYSTEM_PYTHON=1
 
 WORKDIR /app

requirements.txt

@@ -1,3 +1,7 @@
 fastapi==0.115.12
 uvicorn==0.34.0
 pydantic==2.11.3
+numpy>=2.2.0
+torch>=2.6.0
+transformers>=4.56.0
+safetensors>=0.5.3

service.py

@@ -1,129 +1,230 @@
-from __future__ import annotations
-
-import math
-import os
-from statistics import mean
-from typing import Any
-
-from schemas import HealthResponse, PredictRequest, PredictResponse, PredictionItem
-
-
-class TimesFmService:
-    """CPU-first HF Space service wrapper.
-
-    HuggingFace free Spaces are CPU-only in our rollout, so this service keeps
-    the HTTP contract stable and intentionally avoids any GPU assumption. The
-    default backend is a deterministic CPU baseline that is cheap to start,
-    predictable for contract tests, and compatible with `tsf-bridge`.
-    """
-
-    def __init__(self) -> None:
-        self.model_id = "timesfm"
-        self.model_name = os.getenv(
-            "TIMESFM_MODEL_NAME",
-            "google/timesfm-2.5-200m-transformers",
-        )
-        self.backend = os.getenv("TIMESFM_BACKEND", "baseline_cpu").strip() or "baseline_cpu"
-        self.device = "cpu"
-        self.ready = True
-        self.max_context_length = int(os.getenv("TIMESFM_MAX_CONTEXT_LENGTH", "512"))
-        self.max_horizon_step = int(os.getenv("TIMESFM_MAX_HORIZON_STEP", "288"))
-        self.confidence_floor = float(os.getenv("TIMESFM_CONFIDENCE_FLOOR", "0.20"))
-        self.confidence_ceiling = float(os.getenv("TIMESFM_CONFIDENCE_CEILING", "0.85"))
-        self.min_required_points = int(os.getenv("TIMESFM_MIN_REQUIRED_POINTS", "32"))
-
-    def health(self) -> HealthResponse:
-        return HealthResponse(
-            status="ok",
-            model=self.model_name,
-            model_id=self.model_id,
-            backend=self.backend,
-            device=self.device,
-            ready=self.ready,
-            max_context_length=self.max_context_length,
-            max_horizon_step=self.max_horizon_step,
-        )
-
-    def predict(self, payload: PredictRequest) -> PredictResponse:
-        self._validate_request(payload)
-        closes = payload.close_prices[-payload.context_length :]
-
-        predictions = self._predict_with_baseline(closes, payload.horizons)
-        return PredictResponse(model_id=self.model_id, predictions=predictions)
-
-    def _validate_request(self, payload: PredictRequest) -> None:
-        if payload.context_length > self.max_context_length:
-            raise ValueError(
-                f"context_length {payload.context_length} exceeds "
-                f"TIMESFM_MAX_CONTEXT_LENGTH={self.max_context_length}"
-            )
-        if payload.context_length > len(payload.close_prices):
-            raise ValueError("context_length must not exceed len(close_prices)")
-        if len(payload.close_prices) < self.min_required_points:
-            raise ValueError(
-                f"at least {self.min_required_points} close prices are required "
-                "for CPU baseline stability"
-            )
-        if any(step > self.max_horizon_step for step in payload.horizons):
-            raise ValueError(
-                f"horizons contain values above TIMESFM_MAX_HORIZON_STEP={self.max_horizon_step}"
-            )
-
-    def _predict_with_baseline(
-        self, close_prices: list[float], horizons: list[int]
-    ) -> list[PredictionItem]:
-        last_price = close_prices[-1]
-        short_window = close_prices[-min(8, len(close_prices)) :]
-        long_window = close_prices[-min(32, len(close_prices)) :]
-
-        short_mean = mean(short_window)
-        long_mean = mean(long_window)
-        momentum = 0.0 if short_mean == 0 else (last_price - short_mean) / short_mean
-        regime_bias = 0.0 if long_mean == 0 else (short_mean - long_mean) / long_mean
-
-        predictions: list[PredictionItem] = []
-        for step in horizons:
-            damped_step = math.log(step + 1.0)
-            expected_return = momentum * 0.55 + regime_bias * 0.45
-            expected_return *= min(1.0, damped_step / 3.5)
-
-            pred_price = max(0.00000001, last_price * (1.0 + expected_return))
-            confidence = self._confidence(close_prices, step, abs(expected_return))
-            predictions.append(
-                PredictionItem(
-                    step=step,
-                    pred_price=round(pred_price, 8),
-                    pred_confidence=round(confidence, 4),
-                )
-            )
-        return predictions
-
-    def _confidence(
-        self, close_prices: list[float], step: int, expected_move_abs: float
-    ) -> float:
-        if len(close_prices) < 3:
-            return self.confidence_floor
-
-        changes: list[float] = []
-        for previous, current in zip(close_prices[:-1], close_prices[1:]):
-            if previous <= 0:
-                continue
-            changes.append(abs((current - previous) / previous))
-
-        realized_vol = mean(changes[-min(32, len(changes)) :]) if changes else 0.0
-        signal_to_noise = expected_move_abs / (realized_vol + 1e-9)
-        horizon_decay = 1.0 / (1.0 + math.log(step + 1.0))
-        raw = 0.25 + min(signal_to_noise, 2.0) * 0.25 + horizon_decay * 0.35
-        return max(self.confidence_floor, min(self.confidence_ceiling, raw))
-
-    def describe_runtime(self) -> dict[str, Any]:
-        return {
-            "model_id": self.model_id,
-            "model_name": self.model_name,
-            "backend": self.backend,
-            "device": self.device,
-            "ready": self.ready,
-            "max_context_length": self.max_context_length,
-            "max_horizon_step": self.max_horizon_step,
-            "min_required_points": self.min_required_points,
-        }
+from __future__ import annotations
+
+import math
+import os
+from statistics import mean
+from typing import Any
+
+from schemas import HealthResponse, PredictRequest, PredictResponse, PredictionItem
+
+
+class TimesFmService:
+    """HF Space service wrapper for TimesFM.
+
+    By default this service attempts real HuggingFace CPU inference. If model
+    loading fails and `TIMESFM_ALLOW_BASELINE_FALLBACK=true`, it falls back to
+    the deterministic baseline implementation.
+    """
+
+    def __init__(self) -> None:
+        self.model_id = "timesfm"
+        self.model_name = os.getenv(
+            "TIMESFM_MODEL_NAME",
+            "google/timesfm-2.5-200m-transformers",
+        )
+        self.backend = os.getenv("TIMESFM_BACKEND", "hf_cpu").strip() or "hf_cpu"
+        self.device = "cpu"
+        self.max_context_length = int(os.getenv("TIMESFM_MAX_CONTEXT_LENGTH", "512"))
+        self.max_horizon_step = int(os.getenv("TIMESFM_MAX_HORIZON_STEP", "288"))
+        self.confidence_floor = float(os.getenv("TIMESFM_CONFIDENCE_FLOOR", "0.20"))
+        self.confidence_ceiling = float(os.getenv("TIMESFM_CONFIDENCE_CEILING", "0.85"))
+        self.min_required_points = int(os.getenv("TIMESFM_MIN_REQUIRED_POINTS", "32"))
+        self.allow_baseline_fallback = os.getenv("TIMESFM_ALLOW_BASELINE_FALLBACK", "false").lower() == "true"
+
+        self.ready = False
+        self.load_error = ""
+        self._torch = None
+        self._model = None
+
+        self._initialize_backend()
+
+    def health(self) -> HealthResponse:
+        return HealthResponse(
+            status="ok" if self.ready else "degraded",
+            model=self.model_name,
+            model_id=self.model_id,
+            backend=self.backend,
+            device=self.device,
+            ready=self.ready,
+            max_context_length=self.max_context_length,
+            max_horizon_step=self.max_horizon_step,
+        )
+
+    def predict(self, payload: PredictRequest) -> PredictResponse:
+        self._validate_request(payload)
+        closes = payload.close_prices[-payload.context_length :]
+
+        if self.backend == "hf_cpu":
+            if not self.ready:
+                raise RuntimeError(self.load_error or "timesfm backend not ready")
+            predictions = self._predict_with_hf(closes, payload.horizons)
+        else:
+            predictions = self._predict_with_baseline(closes, payload.horizons)
+
+        return PredictResponse(model_id=self.model_id, predictions=predictions)
+
+    def _initialize_backend(self) -> None:
+        if self.backend == "baseline_cpu":
+            self.ready = True
+            return
+
+        if self.backend != "hf_cpu":
+            raise ValueError(f"unsupported TIMESFM_BACKEND={self.backend}")
+
+        try:
+            self._load_hf_model()
+            self.ready = True
+        except Exception as exc:
+            self.load_error = f"timesfm hf load failed: {exc}"
+            if self.allow_baseline_fallback:
+                self.backend = "baseline_cpu"
+                self.ready = True
+            else:
+                self.ready = False
+
+    def _load_hf_model(self) -> None:
+        import torch
+        from transformers import TimesFm2_5ModelForPrediction
+
+        self._torch = torch
+        torch.set_num_threads(max(1, int(os.getenv("TIMESFM_TORCH_THREADS", "2"))))
+        self._model = TimesFm2_5ModelForPrediction.from_pretrained(
+            self.model_name,
+            horizon_length=self.max_horizon_step,
+            torch_dtype=torch.float32,
+        )
+        self._model.to("cpu")
+        self._model.eval()
+
+    def _predict_with_hf(
+        self, close_prices: list[float], horizons: list[int]
+    ) -> list[PredictionItem]:
+        assert self._torch is not None
+        assert self._model is not None
+
+        torch = self._torch
+        context = close_prices[-self.max_context_length :]
+        past_values = [torch.tensor(context, dtype=torch.float32)]
+        freq = torch.tensor([0], dtype=torch.long)
+
+        with torch.inference_mode():
+            outputs = self._model(
+                past_values=past_values,
+                freq=freq,
+                forecast_context_len=len(context),
+                return_forecast_on_context=False,
+            )
+
+        dense_mean = outputs.mean_predictions[0].detach().cpu().tolist()
+        if len(dense_mean) < max(horizons):
+            raise RuntimeError(
+                f"TimesFM output horizon {len(dense_mean)} is shorter than requested {max(horizons)}"
+            )
+
+        dense_conf = self._timesfm_confidence(outputs, dense_mean)
+        predictions: list[PredictionItem] = []
+        for step in horizons:
+            predictions.append(
+                PredictionItem(
+                    step=step,
+                    pred_price=round(max(0.00000001, float(dense_mean[step - 1])), 8),
+                    pred_confidence=round(dense_conf[step - 1], 4),
+                )
+            )
+        return predictions
+
+    def _timesfm_confidence(self, outputs: Any, dense_mean: list[float]) -> list[float]:
+        full_predictions = getattr(outputs, "full_predictions", None)
+        if full_predictions is None:
+            return [self.confidence_floor for _ in dense_mean]
+
+        quantiles = full_predictions[0].detach().cpu()
+        confidence: list[float] = []
+        for idx, pred in enumerate(dense_mean):
+            if quantiles.ndim != 2 or idx >= quantiles.shape[0]:
+                confidence.append(self.confidence_floor)
+                continue
+            lower = float(quantiles[idx][0])
+            upper = float(quantiles[idx][-1])
+            band = abs(upper - lower) / max(abs(pred), 1e-6)
+            raw = 1.0 / (1.0 + band)
+            confidence.append(max(self.confidence_floor, min(self.confidence_ceiling, raw)))
+        return confidence
+
+    def _validate_request(self, payload: PredictRequest) -> None:
+        if payload.context_length > self.max_context_length:
+            raise ValueError(
+                f"context_length {payload.context_length} exceeds "
+                f"TIMESFM_MAX_CONTEXT_LENGTH={self.max_context_length}"
+            )
+        if payload.context_length > len(payload.close_prices):
+            raise ValueError("context_length must not exceed len(close_prices)")
+        if len(payload.close_prices) < self.min_required_points:
+            raise ValueError(
+                f"at least {self.min_required_points} close prices are required "
+                "for TimesFM stability"
+            )
+        if any(step > self.max_horizon_step for step in payload.horizons):
+            raise ValueError(
+                f"horizons contain values above TIMESFM_MAX_HORIZON_STEP={self.max_horizon_step}"
+            )
+
+    def _predict_with_baseline(
+        self, close_prices: list[float], horizons: list[int]
+    ) -> list[PredictionItem]:
+        last_price = close_prices[-1]
+        short_window = close_prices[-min(8, len(close_prices)) :]
+        long_window = close_prices[-min(32, len(close_prices)) :]
+
+        short_mean = mean(short_window)
+        long_mean = mean(long_window)
+        momentum = 0.0 if short_mean == 0 else (last_price - short_mean) / short_mean
+        regime_bias = 0.0 if long_mean == 0 else (short_mean - long_mean) / long_mean
+
+        predictions: list[PredictionItem] = []
+        for step in horizons:
+            damped_step = math.log(step + 1.0)
+            expected_return = momentum * 0.55 + regime_bias * 0.45
+            expected_return *= min(1.0, damped_step / 3.5)
+
+            pred_price = max(0.00000001, last_price * (1.0 + expected_return))
+            confidence = self._baseline_confidence(close_prices, step, abs(expected_return))
+            predictions.append(
+                PredictionItem(
+                    step=step,
+                    pred_price=round(pred_price, 8),
+                    pred_confidence=round(confidence, 4),
+                )
+            )
+        return predictions
+
+    def _baseline_confidence(
+        self, close_prices: list[float], step: int, expected_move_abs: float
+    ) -> float:
+        if len(close_prices) < 3:
+            return self.confidence_floor
+
+        changes: list[float] = []
+        for previous, current in zip(close_prices[:-1], close_prices[1:]):
+            if previous <= 0:
+                continue
+            changes.append(abs((current - previous) / previous))
+
+        realized_vol = mean(changes[-min(32, len(changes)) :]) if changes else 0.0
+        signal_to_noise = expected_move_abs / (realized_vol + 1e-9)
+        horizon_decay = 1.0 / (1.0 + math.log(step + 1.0))
+        raw = 0.25 + min(signal_to_noise, 2.0) * 0.25 + horizon_decay * 0.35
+        return max(self.confidence_floor, min(self.confidence_ceiling, raw))
+
+    def describe_runtime(self) -> dict[str, Any]:
+        return {
+            "model_id": self.model_id,
+            "model_name": self.model_name,
+            "backend": self.backend,
+            "device": self.device,
+            "ready": self.ready,
+            "load_error": self.load_error,
+            "max_context_length": self.max_context_length,
+            "max_horizon_step": self.max_horizon_step,
+            "min_required_points": self.min_required_points,
+        }