*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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*Issue #, if available:*
*Description of changes:*
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*Issue #, if available:*
*Description of changes:* Bump to 2.0
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⚠️ Must be merged after #319 and other related PRs.
*Issue #, if available:*
*Description of changes:* Exports `chronos.__version__` and uses it
dynamically in `pyproject.toml`. Bumps version to 2.0.0.
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*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.
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---------
Co-authored-by: Oleksandr Shchur <oleks.shchur@gmail.com>
*Issue #, if available:* Fixes#310 and closes#302
*Description of changes:* This PR fixes an issue related to the new
caching mechanism for T5 introduced in `transformers==4.54`. [Prior
versions
set](https://github.com/huggingface/transformers/blob/v4.53.3/src/transformers/models/t5/modeling_t5.py#L1328)
`encoder_config.is_encoder_decoder = False` when initializing encoder
and decoder. Following transformers, we also initialized Chronos-Bolt in
the same way. However, in v4.54 this line [has been
removed](3fd456b200/src/transformers/models/t5/modeling_t5.py (L1301))
and [new logic has been
added](3fd456b200/src/transformers/models/t5/modeling_t5.py (L494))
which relies on `is_encoder_decoder` [being
True](3fd456b200/src/transformers/models/t5/modeling_t5.py (L1007)).
This causes Chronos-Bolt to break as described in #310. This PR removes
`is_encoder_decoder = False` for both encoder and decoder which fixes
the issue. I re-ran our mini eval in the CI and got the same results for
v4.54 and v4.48 (our current lower bound).
This PR also bumps package versions.
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*Issue #, if available:*
*Description of changes:*
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*Issue #, if available:*
*Description of changes:*
Just increased the max version allowed. Tests pass.
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---------
Co-authored-by: Abdul Fatir <Abdulfatirs@gmail.com>
*Issue #, if available:* See example build
https://github.com/amazon-science/chronos-forecasting/actions/runs/14302765904/job/40313421985
*Description of changes:*
- Address type-checker complaints, where possible
- Bump bugfix version of the package
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*Issue #, if available:*
*Description of changes:*
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*Issue #, if available:* Solves #273
*Description of changes:*
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*Issue #, if available:*
*Description of changes:* Title.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
*Description of changes:* This PR updates project information and
workflows to allow for PyPi release.
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---------
Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
*Issue #, if available:* N/A
*Description of changes:* This PR adds support for Chronos-Bolt models.
TODOs:
- [x] Update evaluation script
- [x] Fix and add tests for Bolt
- [x] Update docstrings
- [x] Update README example and mention Chronos-Bolt
- [x] Update results bar plot in README
- [x] Add versions for libraries in `pyproject.toml`
- [x] Check that the training and eval scripts work
- [x] Change `autogluon` -> `amazon` in model names
Post Merge:
- [ ] Update Citation style in README, both Github and HuggingFace repos
- [ ] Remove note about AutoGluon
- [ ] Update READMEs of original Chronos models to refer to Chronos-Bolt
NOTE: To be merged after Chronos-Bolt models are available under the
`amazon` namespace on HF.
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---------
Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
Co-authored-by: Caner Turkmen <turkmen.ac@gmail.com>
Co-authored-by: Lorenzo Stella <stellalo@amazon.com>
Fixes https://github.com/amazon-science/chronos-forecasting/issues/181.
Chronos' tokenizer has a vocabulary size of `n_tokens`. Among these,
there are `n_special_tokens` reserved for EOS, PAD, etc. and `n_tokens -
n_special_tokens` allocated to numerical values. However, the provided
`MeanScaleUniformBins` tokenizer creates` n_tokens - n_special_tokens +
1` different buckets, resulting in a total of `n_tokens + 1` possible
tokens. This causes training and inference errors when one of the data
points gets allocated to the largest bucket, as the model requires 0 <=
token_id < n_tokens.
This PR modifies the `MeanScaleUniformBins` tokenizer, so that it
creates one less bucket for numerical values.
---
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---------
Co-authored-by: Lorenzo Stella <lorenzostella@gmail.com>
*Description of changes:* This PR adds configs and a script to evaluate
Chronos models in the same way as described in the paper.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.de>
*Description of changes:* This PR relaxes `torch` and `transformers`
versions to allow for older versions that were used during original
training. This is needed in light of recent `torch`/`transformers`
versions being slower with DDP.
Relevant issues (but the problem may be deeper than these):
- https://github.com/huggingface/transformers/issues/30840
- https://github.com/pytorch/pytorch/issues/127077
- https://github.com/NVIDIA/nccl/issues/1298
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.com>
*Issue #, if available:* #76
*Description of changes:* transformers 4.41 broke something for us, we
need to look into it deeper
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*Description of changes:* See title.
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Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.com>
*Description of changes:* This PR adds the script to generate synthetic
data from KernelSynth.
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---------
Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.com>
*Description of changes:* Add training script and config files. Can be
used for pre-training, or adapted for fine-tuning chronos models.
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---------
Co-authored-by: Abdul Fatir <Abdulfatirs@gmail.com>
*Description of changes:* Fix some type checking issues, add mypy to
github workflow, apply black
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