chronos-forecasting/test/util.py

from typing import Optional, Tuple

import torch


def validate_tensor(
    a: torch.Tensor, shape: Tuple[int, ...], dtype: Optional[torch.dtype] = None
) -> None:
    assert isinstance(a, torch.Tensor)
    assert a.shape == shape

    if dtype is not None:
        assert a.dtype == dtype
Return predictions in fp32 on CPU (#219) Issue #, if available: N/A Description of changes: This PR ensures that predictions are returned in FP32 and on the CPU device. This choice is now better because we have two types of models which have different types of forecasts (samples vs. quantiles). Furthermore, `int64` input_type (our README example is one such case) ran into issues with `predict_quantiles` before. This choice also fixes that. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de> 2024-11-29 15:54:21 +00:00			`from typing import Optional, Tuple`

			`import torch`


			`def validate_tensor(`
			`a: torch.Tensor, shape: Tuple[int, ...], dtype: Optional[torch.dtype] = None`
			`) -> None:`
			`assert isinstance(a, torch.Tensor)`
			`assert a.shape == shape`

			`if dtype is not None:`
Fix padding for int contexts (#227) Issue #, if available: On Linux, the final call to `.to` creates trouble when input tensors are integer. For example: ``` >>> a = torch.tensor([1]) >>> b = torch.stack([torch.full((1,), torch.nan), a]) >>> b tensor([[nan], [1.]]) >>> b.to(a) tensor([[-9223372036854775808], [ 1]]) ``` By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. 2024-12-04 15:46:17 +00:00			`assert a.dtype == dtype`