enable truncation of context len in ttm

pyproject.toml

@@ -31,7 +31,7 @@ packages = ["tsfm_public", "tsfmhfdemos"]
 [project.optional-dependencies]
 
 
-all = ["tsfm_public[notebooks,testing,dev]"]
+all = ["tsfm_public[notebooks,external,testing,dev]"]
 
 notebooks = [
     "jupyter",
@@ -42,7 +42,8 @@ notebooks = [
     "kaleido",
     "tensorboard",
 ]
-testing = ["pytest", "tsfm_public[notebooks]", "parameterized"]
+external = ["tsfm_public[notebooks]", "gluonts"]
+testing = ["pytest", "tsfm_public[external]", "parameterized"]
 dev = ["pre-commit", "tsfm_public[testing]", "ruff==0.4.4"]
 
 # ogv deployments will already have jupyter

tsfm_public/models/tinytimemixer/modeling_tinytimemixer.py

@@ -1802,6 +1802,15 @@ def forward(
         Returns:
 
         """
+        if past_values.dim() != 3:
+            raise ValueError(
+                "`past_values` must have 3 dimensions of shape `(batch_size, sequence_length, num_input_channels)`."
+            )
+        if past_values.shape[1] > self.config.context_length:
+            past_values = past_values[:, -self.config.context_length :, :]
+        elif past_values.shape[1] < self.config.context_length:
+            raise ValueError("Context length in `past_values` is shorter that TTM context_length.")
+
         if self.loss == "mse":
             loss = nn.MSELoss(reduction="mean")
         elif self.loss == "mae":

tsfm_public/toolkit/gluonts_data_wrapper.py

@@ -2,13 +2,14 @@
 from typing import Union
 
 import numpy as np
-import torch
 from gluonts.dataset.split import InputDataset, LabelDataset, TrainingDataset
 from gluonts.itertools import batcher
 from gluonts.transform.feature import LastValueImputation
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
+from tsfm_public.toolkit.dataset import _torch
+
 
 def impute_series(target):
     if np.isnan(target).any():
@@ -23,17 +24,6 @@ def impute_series(target):
     return target
 
 
-def np_to_torch(np):
-    if np.dtype == "float" or np.dtype == "float32":
-        return torch.from_numpy(np).float()
-    elif np.dtype == "int":
-        return torch.from_numpy(np)
-
-
-def _torch(*nps):
-    return tuple(np_to_torch(x) for x in nps)
-
-
 class StandardScalingGluonTSDataset:
     """
     TTM works best on standard scaled data, especially if fewshot