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* added informer to gitignore * added informer to gitignore * WIP informer2020 * added checking that instantiate works * added config using gluonTS by kashif * WIP config * adding informeConfig. need to remove FeatureEmbedder * done InformerConfig, but need to change the names * Done informer model init. working on enc-dec * added things to address, after reading again enc-dec in the paper * done modeling - checking initialization work * added informer to gitignore * WIP informer2020 * added checking that instantiate works * added config using gluonTS by kashif * WIP config * adding informeConfig. need to remove FeatureEmbedder * done InformerConfig, but need to change the names * Done informer model init. working on enc-dec * added things to address, after reading again enc-dec in the paper * done modeling - checking initialization work * moved enc-dec init to InformerEncoder/Decoder init * added 'init_std' to config, now model init works! * WIP conversion script, and added code sources * WIP conversion script: loading original informer pth works * WIP conversion script: change defaults in the config * WIP conversion script: supporting Informer input embedding * WIP conversion script: added parameters for the informer embed * WIP conversion script: change dim_feedforward=2048 * WIP conversion script: remove unused args for loading checkpoint * just cleaning up * DataEmbedding removed, after thinking with Kashif * working on forward pass * WIP forward pass: trying to establish working batch for forward pass * cleaning and finalizing * adding HF names and docs * init after cleaning works * WIP in tests * added docs for the informer specific args * fix style * undo change * cleaning informer, now need to work only enc-dec * initial enc-dec classes * added encoder and decoder * added todo * add todos for conv_layers * added decoder docs from vanilla * added encoder docs from vanilla * remove encoder decoder from the original informer * removed AttentionLayer from the original paper * removed TriangularCausalMask, same as decoder_attention_mask * initial sparse attention * use conv_layers * fixed test_config test * fix parenthesis when itearting zip(layers, conv_layers) * error found in prob attention, added sizes as comments * fix sizes * added proposal for q_reduce indexing, and remove unused * WIP ProbMask, and changed factor=2 for testing * remove unused libs for this PR for creating the env * fix checking the attn_weights.size() after bmm * Q_reduce: changed from torch.gather to simple slicing * WIP calculate final attn_output * finish adding v_aggregated, attn_output ready * changed tgt_len to u in attention_mask, need to fix the size error * comment attention_mask for encoder, and fix if cond for v_agg * added ProbMask support (wip), removed old original code * finished ProbMask 😃 * Revert "remove unused libs for this PR for creating the env" This reverts commit11a081e09e. * fixes * make style * fix initial tests * fix more tests * dry * make style * remove unused files * style * added integration tests * fix num_static_real_features * fix header * remove unused function * fix example * fix docs * Update src/transformers/models/informer/configuration_informer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/informer/modeling_informer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/informer/configuration_informer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/informer/configuration_informer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/informer/configuration_informer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/informer/configuration_informer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * fixes for reviewer * use prediction_length from model * fix style * fixed informer.mdx * added to index * updated readme * undo * make fix-copies * typo * fix copy * added Informer to toctree * in order * fixed comments * remove unneeded new lines in docs * make static real and cat optional * fix use of distil conv layers * fixed integration test * added checkpoint for convlayer * make fix-copies * updated from time series model * make fix-copies * copy decoder * fix unit tests * updated scaling config * fix integration tests * IGNORE_NON_TESTED * IGNORE_NON_AUTO_CONFIGURED * IGNORE_NON_AUTO_CONFIGURED * updated check configs * fix formatting * undo change from time series * prediction_length should not be None * aliign with the blog: prettify ProbSparse and change attention_factor to sampling_factor * make style * make fix-copies * niels CR: update contributed by * niels CR: update configuration_informer.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * niels CR: update kashif -> huggingface Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * niels CR: `sampling_factor` only relevant when `attention_type`=prob * make style * fixed U_part: added multiplication by `L_Q` * fixed bug: remove `is not None` from `if config.distil` * fixed test: `decoder_seq_length` to `encoder_seq_length` in cross_attentions check * fix integration tests * updated model hub * do not shift as in training * undo * fix make-copies * make fix-copies * added `if prediction_length is None` * changed `ProbSparseAttention` to `InformerProbSparseAttention` * changed `V_sum` -> `v_mean_dim_time` * changed `ConvLayer` to `InformerConvLayer` and fixed `super()` * TimeSeriesTansformer->Informer in decoder's Copied from * more descriptive in ProbSparse * make style * fix coped from * Revert "added `if prediction_length is None`" This reverts commitb4cbddfa05. * fixed indent * use InformerSinusoidalPositionalEmbedding * make fix-style * fix from #21860 * fix name * make fix-copies * use time series utils * fix dec num_heads * docstring * added time series util doc * _import_structure * formatting * changes from review * make style * fix docs * fix doc * removed NegativeLogLikelihood --------- Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
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# Time Series Utilities
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This page lists all the utility functions and classes that can be used for Time Series based models.
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Most of those are only useful if you are studying the code of the time series models or you wish to add to the collection of distributional output classes.
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## Distributional Output
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[[autodoc]] time_series_utils.NormalOutput
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[[autodoc]] time_series_utils.StudentTOutput
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[[autodoc]] time_series_utils.NegativeBinomialOutput
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