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* initial documentation * rename mask to attention_mask * smaller tests * fixup * fix copies * move to time series section * sort docs * isort fix * batch_size is not a configuration * rename to TimesFMModelForPrediction * initial script * add check_outputs * remove dropout_rate * works with torch.Tensor inputs * rename script * fix docstrings * fix freq when window_size is given * add loss * fix _quantile_loss * formatting * fix isort * add weight init * add support for sdpa and flash_attention_2 * fixes for flash_attention * formatting * remove flash_attention * fix tests * fix file name * fix quantile loss * added initial TimesFMModelIntegrationTests * fix formatting * fix import order * fix _quantile_loss * add doc for SDPA * use timesfm 2.0 * bug fix in timesfm decode function. * compare mean forecasts * refactor type hints, use CamelCase * consolidate decode func * more readable code for weight conversion * fix-copies * simpler init * renaem TimesFmMLP * use T5LayerNorm * fix tests * use initializer_range * TimesFmModel instead of TimesFmDecoder * TimesFmPositionalEmbedding takes config for its init * 2.0-500m-pytorch default configs * use TimesFmModel * fix formatting * ignore TimesFmModel for testing * fix docstring * override generate as its not needed * add doc strings * fix logging * add docstrings to output data classes * initial copy from t5 * added config and attention layers * add TimesFMPositionalEmbedding * calcuate scale_factor once * add more configs and TimesFMResidualBlock * fix input_dims * standardize code format with black * remove unneeded modules * TimesFM Model * order of imports * copy from Google official implementation * remove covariate forecasting * Adapting TimesFM to HF format * restructing in progress * adapted to HF convention * timesfm test * the model runs * fixing unit tests * fixing unit tests in progress * add post_init * do not change TimesFMOutput * fixing unit tests * all unit tests passed * remove timesfm_layers * add intermediate_size and initialize with config * initial documentation * rename mask to attention_mask * smaller tests * fixup * fix copies * move to time series section * sort docs * isort fix * batch_size is not a configuration * rename to TimesFMModelForPrediction * initial script * add check_outputs * remove dropout_rate * works with torch.Tensor inputs * rename script * fix docstrings * fix freq when window_size is given * add loss * fix _quantile_loss * formatting * fix isort * add weight init * add support for sdpa and flash_attention_2 * fixes for flash_attention * formatting * remove flash_attention * fix tests * fix file name * fix quantile loss * added initial TimesFMModelIntegrationTests * fix formatting * fix import order * fix _quantile_loss * add doc for SDPA * use timesfm 2.0 * bug fix in timesfm decode function. * compare mean forecasts * refactor type hints, use CamelCase * consolidate decode func * more readable code for weight conversion * fix-copies * simpler init * renaem TimesFmMLP * use T5LayerNorm * fix tests * use initializer_range * TimesFmModel instead of TimesFmDecoder * TimesFmPositionalEmbedding takes config for its init * 2.0-500m-pytorch default configs * use TimesFmModel * fix formatting * ignore TimesFmModel for testing * fix docstring * override generate as its not needed * add doc strings * fix logging * add docstrings to output data classes * add _CHECKPOINT_FOR_DOC * fix comments * Revert "fix comments" This reverts commit8deeb3e191. * add _prepare_4d_attention_mask * we do not have generative model classes * use Cache * return past_key_values * modules initialized with config only * update year * Update docs/source/en/model_doc/timesfm.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * add layer_idx to cache * modular timesfm * fix test * unwrap sequential class * fix toctree * remove TimesFmOnnxConfig * fix modular * remove TimesFmStackedDecoder * split qkv layer into individual layers * rename projection layers * use ALL_ATTENTION_FUNCTIONS * is_causal is True * rename config * does not support flash_attn_2 * formatting * fix typo in docsstring * rename inputs * add time series mapping * Update src/transformers/models/olmo2/modeling_olmo2.py * Update src/transformers/models/moonshine/modeling_moonshine.py * use updated arguments * fix class name * add MODEL_FOR_TIME_SERIES_PREDICTION_MAPPING * isort * consolidate _preprocess into forward * fix a typo * fix a typo * fix toc * fix modular * remove aaserts * use self.config._attn_implementation * move to _postprocess_output * remove timesfm_get_large_negative_number * use view unstead of multiple unsqueeze * make helpers static methods of the Model * use to_tuple * use to_tuple if not return_dict * remove unused intitialization block as its incorporated in nn.Linear * remove unused num_key_value_groups * use the same convention as the masking method * update modular * do not use unsqueeze * use view instead of unsqueeze * use buffer for inv_timescales * formatting * modular conversion * remove unneeded intialization * add missing docstrings * remove cache * use simple_eager_attention_forward * support tp_plan * support for flex and flash attention masks * Revert "support for flex and flash attention masks" This reverts commitdef36c4fcf. * fix device * fix tests on gpu * remove unsued large model test * removed unneeded comments * add example usage * fix style * add import * Update docs/source/en/model_doc/timesfm.md Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com> * inherit from LlamaRMSNorm * use can_return_tuple decorator * remvoe return_dict * fix year * Update docs/source/en/model_doc/timesfm.md Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com> * pretrained does not inherit from GenerationMixin * use model for integration test --------- Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com> Co-authored-by: Rajat Sen <rsen91@gmail.com> Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com> Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co>
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Python
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Python