*Issue #, if available:*
*Description of changes:*
Rename all variables and methods that refer to "task" in `dataset.py` to
use `input` instead:
- `PreparedTask` → `PreparedInput`
- `self.tasks` → `self.inputs`
- `prepare_tasks` → `prepare_inputs`
- `validate_and_prepare_single_dict_task` →
`validate_and_prepare_single_dict_input`
- All `task_` prefixed local variables renamed (e.g., `task_target` →
`target`, `task_context` → `context`, `task_past_tensor` →
`past_tensor`, etc.)
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choice.
*Issue #, if available:*
This PR enables fine-tuning on datasets that don't fit into memory. The
main idea is to decouple the preprocessing logic from the
`Chronos2Dataset` class.
The workflow is as follows:
1. Preprocess raw data once using `prepare_tasks()` → save to any
`Sequence[PreparedTask]` container like `datasets.Dataset`
2. During training, load tasks lazily via memory-mapped Arrow files
3. Pass data to `pipeline.fit(..., convert_inputs=False)` to skip
redundant preprocessing
*Description of changes:*
- Added `PreparedTask` `TypedDict` defining the preprocessed task schema
(context, future_covariates, n_targets, n_covariates,
n_future_covariates as torch.Tensor/int)
- Extracted `prepare_tasks()` as a standalone function so it can be used
in preprocessing scripts
- Added `convert_inputs` parameter to `Chronos2Dataset` and
`Chronos2Pipeline.fit()` to toggle between raw input preprocessing
(default) and pre-processed input passthrough.
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copy, and redistribute this contribution, under the terms of your
choice.
*Issue #, if available:*
*Description of changes:* Adds support for custom callbacks after each
batch is processed during prediction. This allows for keeping track of
the time limit in AutoGluon.
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copy, and redistribute this contribution, under the terms of your
choice.
*Issue #, if available:*
*Description of changes:*
- Rename `predict_batches_jointly` to `cross_learning`
- Add deprecation warning
- Add cross_learning to predict_df docstring
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copy, and redistribute this contribution, under the terms of your
choice.
*Issue #, if available:* Fixes#403
*Description of changes:*
- Update the `future_df` validation logic to only check that
`prediction_length` values are provided for each item.
- Update unit tests for DF-based methods in `test_chronos2.py`
- Ignore fine-tuned checkpoint folders with `.gitignore`
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copy, and redistribute this contribution, under the terms of your
choice.
*Issue #, if available:*
*Description of changes:* This PR adds a `validate_inputs ` argument to
`predict_df` (defaults to `True`), which allows the user to disable
dataframe validation when they know that their dataframe is in the right
format. This reduces runtime by removing the input validation component,
e.g., when calling this method from
[AutoGluon](https://github.com/autogluon/autogluon/pull/5427), and also
handles series with shorter than 3 timesteps.
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copy, and redistribute this contribution, under the terms of your
choice.
*Issue #, if available:*
*Description of changes:* Adds support for LoRA fine-tuning.
- [x] Move peft/pandas dependency to an extra
- [x] Add tests for LoRA
- [x] Update notebook with LoRA info
- [x] Enable automatic recognition and loading of LoRA adapters
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copy, and redistribute this contribution, under the terms of your
choice.
*Issue #, if available:* #354
*Description of changes:* This PR adds `Chronos2Pipeline.embed` to
enable users to extract embeddings from the last encoder layer in an
easy way. The API and behavior is similar to what Chronos and
Chronos-Bolt provides.
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copy, and redistribute this contribution, under the terms of your
choice.
*Issue #, if available:*
*Description of changes:* This PR adds `predict_df` to the base pipeline
which enables pandas support for the univariate Chronos and Chronos-Bolt
models.
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copy, and redistribute this contribution, under the terms of your
choice.
*Issue #, if available:*
*Description of changes:*
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copy, and redistribute this contribution, under the terms of your
choice.
This pull request introduces configurable attention backends to the
Chronos-2 model, allowing users to select between eager, SDPA, and
FlashAttention-2 implementations.
---------
Co-authored-by: Oleksandr Shchur <oleks.shchur@gmail.com>
Co-authored-by: Abdul Fatir <Abdulfatirs@gmail.com>
*Issue #, if available:*
*Description of changes:* This PR adds the Chronos-2 model.
* Chronos-2 modeling and pipeline code, including tests.
* Updated `pyproject.toml`. Merge `training` and `evaluation` extras
into a single `dev` extra. This stuff is only relevant for the Chronos
models.
* Added `predict_fev` to `BaseChronosPipeline`.
* Changes to `InstanceNorm` for Chronos-Bolt to make it general and
compatible with Chronos-2.
* Minor renaming and polishing in the inference code for Chronos and
Chronos-Bolt.
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: Oleksandr Shchur <oleks.shchur@gmail.com>
