"git@developer.sourcefind.cn:chenpangpang/parler-tts.git" did not exist on "ee12a8122643112ecce17498d4a8dc39236b9ceb"
- 19 Jun, 2024 1 commit
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Anton Vlasjuk authored
* `gpt2` sdpa support * fix (at least) one test, style, repo consistency * fix sdpa mask in forward --> fixes generation * test * test2 * test3 * test4 * simplify shapes for attn mask creation and small comments * hub fail test * benchmarks * flash attn 2 mask should not be inverted on enc-dec setup * fix comment * apply some suggestion from code review - only save _attn_implentation once - remove unnecessary comment * change elif logic * [run-slow] gpt2 * modify `test_gpt2_sample_max_time` to follow previous assertion patterns
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- 11 Jun, 2024 1 commit
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amyeroberts authored
* Draft fast image processors * Draft working fast version * py3.8 compatible cache * Enable loading fast image processors through auto * Tidy up; rescale behaviour based on input type * Enable tests for fast image processors * Smarter rescaling * Don't default to Fast * Safer imports * Add necessary Pillow requirement * Woops * Add AutoImageProcessor test * Fix up * Fix test for imagegpt * Fix test * Review comments * Add warning for TF and JAX input types * Rearrange * Return transforms * NumpyToTensor transformation * Rebase - include changes from upstream in ImageProcessingMixin * Safe typing * Fix up * convert mean/std to tesnor to rescale * Don't store transforms in state * Fix up * Update src/transformers/image_processing_utils_fast.py Co-authored-by:
Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/auto/image_processing_auto.py Co-authored-by:
Pavel Iakubovskii <qubvel@gmail.com> * Add equivalence and speed tests * Fix up --------- Co-authored-by:
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- 10 Jun, 2024 1 commit
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Pavel Iakubovskii authored
* Fix do_reduce_labels for maskformer image processor * Deprecate reduce_labels in favor to do_reduce_labels * Deprecate reduce_labels in favor to do_reduce_labels (segformer) * Deprecate reduce_labels in favor to do_reduce_labels (oneformer) * Deprecate reduce_labels in favor to do_reduce_labels (maskformer) * Deprecate reduce_labels in favor to do_reduce_labels (mask2former) * Fix typo * Update mask2former test * fixup * Update segmentation examples * Update docs * Fixup * Imports fixup * Add deprecation decorator draft * Add deprecation decorator * Fixup * Add deprecate_kwarg decorator * Validate kwargs decorator * Kwargs validation (beit) * fixup * Kwargs validation (mask2former) * Kwargs validation (maskformer) * Kwargs validation (oneformer) * Kwargs validation (segformer) * Better message * Fix oneformer processor save-load test * Update src/transformers/utils/deprecation.py Co-authored-by:
amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/utils/deprecation.py Co-authored-by:
Pablo Montalvo <39954772+molbap@users.noreply.github.com> * Update src/transformers/utils/deprecation.py Co-authored-by:
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- 05 Jun, 2024 1 commit
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Vaibhav Srivastav authored
* doc: add info about wav2vec2 bert in older wav2vec2 models. * apply suggestions from review. * forward contrib credits from review --------- Co-authored-by:Sanchit Gandhi <sanchit-gandhi@users.noreply.github.com>
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- 04 Jun, 2024 1 commit
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Younes Belkada authored
* deprecate blip * mention deprecation on docs
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- 31 May, 2024 2 commits
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Pavel Iakubovskii authored
* Initial setup * Metrics * Overfit on two batches * Train 40 epochs * Memory leak debugging * Trainer fine-tuning * Draft * Fixup * Trained end-to-end * Add requirements * Rewrite evaluator * nits * Add readme * Add instance-segmentation to the table * Support void masks * Remove sh * Update docs * Add pytorch test * Add accelerate test * Update examples/pytorch/instance-segmentation/README.md * Update examples/pytorch/instance-segmentation/run_instance_segmentation.py * Update examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py * Update examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py * Update examples/pytorch/instance-segmentation/run_instance_segmentation.py * Fix consistency oneformer * Fix imports * Fix imports sort * Apply suggestions from code review Co-authored-by:
NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update examples/pytorch/instance-segmentation/run_instance_segmentation.py Co-authored-by:
Sangbum Daniel Choi <34004152+SangbumChoi@users.noreply.github.com> * Add resources to docs * Update examples/pytorch/instance-segmentation/README.md Co-authored-by:
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Asif Ajrof authored
`mask` variable is not defined. probably a writing mistake. it should be `segmentation_map`. `segmentation_map` should be a `1` channel image rather than `RGB`. [on a different note, the `mask_url` is the same as `raw_image`. could provide a better example.
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- 28 May, 2024 2 commits
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amyeroberts authored
* Deprecate models - graphormer - time_series_transformer - xlm_prophetnet - qdqbert - nat - ernie_m - tvlt - nezha - mega - jukebox - vit_hybrid - x_clip - deta - speech_to_text_2 - efficientformer - realm - gptsan_japanese * Fix up * Fix speech2text2 imports * Make sure message isn't indented * Fix docstrings * Correctly map for deprecated models from model_type * Uncomment out * Add back time series transformer and x-clip * Import fix and fix-up * Fix up with updated ruff
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NielsRogge authored
* Update docs * Add PaliGemma resources * Address comment * Update docs
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- 27 May, 2024 1 commit
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Eitan Turok authored
* Fix link in dbrx.md * remove "though this may not be up to date" --------- Co-authored-by:Lysandre Debut <hi@lysand.re>
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- 23 May, 2024 1 commit
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Aritra Roy Gosthipaty authored
* chore: initial commit * chore: adding imports and inits * chore: adding the causal and classification code * chore: adding names to the layers * chore: using single self attn layer * chore: built the model and layers * chore: start with testing * chore: docstring change, transpose fix * fix: rotary embedding * chore: adding cache implementation * remove unused torch * chore: fixing the indexing issue * make fix-copies * Use modeling_tf_utils.keras * make fixup * chore: fixing tests * chore: adding past key value logic * chore: adding multi label classfication test * fix: switching on the built parameters in the layers * fixing repo consistency * ruff formats * style changes * fix: tf and pt equivalence * removing returns from docstrings * fix docstrings * fix docstrings * removing todos * fix copies * fix docstring * fix docstring * chore: using easier rotate_half * adding integration tests * chore: addressing review related to rotary embedding layer * review changes * [run-slow] mistral * skip: test save load after resize token embedding * style --------- Co-authored-by:Matt <rocketknight1@gmail.com>
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- 22 May, 2024 2 commits
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Vaibhav Srivastav authored
[doc] Add references to the fine-tuning blog and distil-whisper to Whisper doc.
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Raushan Turganbay authored
* update video-llava * Update docs/source/en/model_doc/video_llava.md Co-authored-by:
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- 21 May, 2024 1 commit
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NielsRogge authored
* Update ignore index * Update docs * Update docs
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- 20 May, 2024 2 commits
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Raushan Turganbay authored
* update docs with batch ex * Update docs/source/en/model_doc/llava_next.md Co-authored-by:
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Joseph Enguehard authored
* Add MistralForTokenClassification * Add tests and docs * Add token classification for Mixtral and Qwen2 * Save llma for token classification draft * Add token classification support for Llama, Gemma, Persimmon, StableLm and StarCoder2 * Formatting * Add token classification support for Qwen2Moe model * Add dropout layer to each ForTokenClassification model * Add copied from in tests * Update src/transformers/models/llama/modeling_llama.py Co-authored-by:
Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * Propagate suggested changes * Style --------- Co-authored-by:
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- 16 May, 2024 3 commits
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Raushan Turganbay authored
fix model id in docs
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NielsRogge authored
* Add resources * Address comment * Address comments * Update docs/source/en/model_doc/idefics2.md Co-authored-by:
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hyenal authored
remove blank line (+1 squashed commit) Squashed commits: [24ccd2061] [run-slow]vit_msn,vision_encoder_decoder (+24 squashed commits) Squashed commits: [08bd27e7a] [run-slow]vit_msn,vision_encoder_decoder [ec96a8db3] [run-slow]vit_msn [ead817eca] fix vit msn multi gpu [d12cdc8fd] [run-slow]audio_spectrogram_transformer,deit,vision_encoder_decoder,vision_text_dual_encoder,vit,vit_hybrid,vit_mae,vit_msn,videomae,yolos [3fdbfa88f] doc [a3ff33e4a] finish implementation [e20b7b7fb] Update test_modeling_common.py [e290c5810] Update test_modeling_flax_common.py [d3af86f46] comment [ff7dd32d8] more comments [59b137889] suggestion [7e2ba6d67] attn_implementation as attribute of the class [fe66ab71f] minor [38642b568] Apply suggestions from code review Accept comments Co-authored-by:
Liubov Yaronskaya <luba.yaronskaya@gmail.com>
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- 15 May, 2024 1 commit
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Raushan Turganbay authored
* add model draft * update docstring * add tests * support image and video as input * update for better handling of mixed input and clean-up a bit * bug when mixed inputs & add tests * Update README.md Co-authored-by:
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- 14 May, 2024 3 commits
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Pablo Montalvo authored
* add new model like * add state dict slicing + new model config * update palma config and weights, passes vision activations * fix * update * reorder loading/unpacking * clean up * add debug statements * change device * fix * debugging * fix noncausal mask * fixup sdpa + causal mask * fix activation function * remove debug before changing modeling file * add variants * debug attention mask in generate * revert to non-debug sdpa * revert gemma modifications * add custom language modeling * use Processor * add language modeling file to init * try thin wrapper around generate * Update * update mask * breakpoints galore * remove conflict * switch to left-padding * add incomplete model doc * add paligemma global files * batch rename paligemma * make generation match outputs and captioning * style * style * remove copied from + doc * remove more copied from * remove copy from projector * minor fix * update config and style * add readme - dummy * CORRECT image captioning * moving to args * add siglip proper + fix merging image + text features * take update_causal_mask from upstream * remove breakpoint * leverage AutoModel * fix input_ids slicing * make siglip head conditional * remove encoder_decoder value * remove unneeded modeling file * add commented 4d attention mask * FIXED generation with 4D mask * Update src/transformers/models/siglip/modeling_siglip.py Co-authored-by:
Merve Noyan <merveenoyan@gmail.com> * simplify conversion script * add copied from * add precision to conversion script * Update src/transformers/models/paligemma/modeling_paligemma.py Co-authored-by:
Pedro Cuenca <pedro@huggingface.co> * clean up * Shift attention mask from `1:` After discussion with @molbap * add docs, fix quality * quality, tied weights inheritance, and logits/label alignment * fix more tests * pass attn_implementation to language model correctly * add SiglipVisionTransformer to no split modules * skip paligemma test for sdpa dispatch to flash * skip incompatible tests * quality * [broken archive maps] * Apply suggestions - remove archive lists - style - take shape of inputs_embeds for batch Co-authored-by:
ArthurZucker <arthur.zucker@gmail.com> Co-authored-by:
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Yikang Shen authored
* init jetmoe code * update archive maps * remove flax import * fix import error * update README * ruff fix * update readme * fix * update config * fix issue * merge files * fix model bug * fix test * auto fix * model size * add comments * fix form * add flash attention support * fix attention head number * fix init * fix support list * sort auto mapping * fix test * fix docs * update test * fix test * fix test * change variable name * fix config * fix init * update format * clean code * fix config * fix config * change default config * update config * fix issues * update formate * update config argument * update format * Update src/transformers/models/jetmoe/modeling_jetmoe.py Co-authored-by:
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Jacky Lee authored
fix: owlv2 doc
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- 13 May, 2024 1 commit
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Alazar authored
* Initial commit * Just a copy of modeling_idefics.py that will be ported to TF * - Prepend TF to the name of all classes - Convert pytorch ops to TF (not all operations are converted yet) * Add TF imports * Add autotranslated files * Add TF classes to model_tf_auto.py * Add the TF classes in model_doc * include auto-translated code * Adopted from auto-translated version * Add a forgotten super().build * Add test code for TF version. * Fix indentation and load pytorch weights for now * Some fixes. Many tests are still failing but some are passing now. - I have added TODO's for some of the hacks I made to unblock me and I will address them soon - I have the processing_idefics.py hacked in my view to support TF temporarily * Add ALL_LAYERNORM_LAYERS to match pytorch * Revert "Add ALL_LAYERNORM_LAYERS to match pytorch" This reverts commit 7e0a35119b4d7a6284d04d8c543fba1b29e573c9 as it is not needed in the tf implementation. * Fix freeze_relevant_params() * Some more fixes * Fix test_attention_outputs * Add tf stuff to processing_idefics.py processing_idefics.py supports both pytorch and tf now. test_processor_idefics.py for pytorch is passing, so i didn't break anything but still some issues with tf. I also need to add tf tests in test_processor_idefics.py. * Pass return_tensors to image processing code and fix test * Pass return_tensors to the image processor __init__ * Fix several test cases - Make input to some of the forward pass of type `TFModelInputType` - Decorate main layer forward pass with `@unpack_inputs` - Decorate main layer with `@keras_serializable` - Pass `inputs` to TFIdeficsModel * Some more fixes forgotten in last commit * Fix processing code and vision_tf.py * Fix perceiver bug * Import from * Auto-add build() methods + style pass * Fix build() errors due to `None` being passed as shape to some layers * Change name in TFIdeficsForVisionText2Text to attribute in IdeficsForVisionText2Text * Fix pytorch weights load for tf2 There were a lot of `name=` missing in weight initialization code. * Attempt to fix CI * Add back accidently removed line * Remove torch-specific stuff from the TF test file * make fix-copies, make style, remove autotranslated files * Fixes to imports/docstrings * Let's try the from future import in desperation * Fix the core random_attention_mask fn to match the torch/flax behaviour * Clean random_attention_mask up correctly * Remove torch-only test * Fix loss shape, couple of nits * make style * Don't test for OOB embeddings because IDEFICS uses those deliberately * Fix loss computation to handle masking * Fix test failures when flattening * Fix some test failures - Add cross attention gate which was missing and wasn't being passed arround - Fix overwriting of image_attention_mask due to hack I had for dummy inputs * Add a proper stateless scaled_dot_product_attention * make style * Adding missing attribute from the PyTorch version * Small cleanups to decoupledlinearlayer in case that helps * Pass epsilon to LayerNormalization * Attemp to fix pytorch weight cross-loading for TFIdeficsEmbedding * Fix a bug in TFIdeficsGatedCrossAttentionLayer * Patching up build() methods * Constant self.inv_freq * Constant self.inv_freq * First working version The TF implementation works now, there was a bug in the TFIdeficsDecoupledLinear where the weights were mis-intialized (in_features,out_features) when it should be: (out_features, in_features) I have tested this so far with tiny-random and idefics-9b-instruct and gives correct output. I also dumped the final outputs for both pytorch and TF and they are identical. * Fix some test failures * remove print statement * Fix return_tensors * Fix CI test failure check_code_quality * Attempt to fix CI failures by running `make fixup` The hardcoded IDs in test_modeling_tf_idefics.py are for the integration test and makes that file unreadable and should probably be moved to a seperate file. * Attempt to fix tests_pr_documentation_tests * Fix a test failure in test_image_processing_idefics.py * Fix test test_pt_tf_model_equivalence * Fix a few failures * Tiny fix * Some minor fixes * Remove a duplicate test * Override a few test failures for IDEFICS - `test_keras_save_load` is passing now - `test_compile_tf_model` is still failing * Fix processing_idefics.py after rebase * Guard import keras with is_tf_available * fix check code quality * fix check code quality * Minor fixes * Skip test_save_load temporarily This test passed on my local box but fails on the CI, skipping for now to see if there are other remaining failures on the CI. * Run `ruff format tests src utils` * Fix last failing test, `test_compile_tf_model` * Add fixes for vision_tf.py I forgot to add this file in last commit. * Minor fixes * Replace "<<<" with "<<" for doc tests IDEFICS-9B is too big for doctest runner, so don't run it there * Make code more readable * Fix bug after code review I added a layer_norm_eps to IdeficsConfig but I don't even need it since the vision config has a layer_norm_eps. * Fix after code review Use original code tokenizer.convert_tokens_to_ids * Keep PyTorch as the default return_tensors * Fixes to modeling_tf after code review * Fixes from code review - Remove all references of `TF_IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST` - Pass 1e-5 to LayerNormalization in perceiver * Run ruff * Undo a change * Refactor processing code after Matt's suggestion * Remove TODO's that aren't needed anymore * For pytorch, Use original pytorch processing code from main Since this PR is a TF port it shouldn't make any modifications to pytorch IDEFICS code. This changes undo's the pytorch processing modifications I made and uses original code from main. * Update tests/models/idefics/test_modeling_idefics.py * Update tests/models/idefics/test_modeling_tf_idefics.py * Add missing imports for is_pt_tf_cross_test * [DO NOT MERGE]: This is a commit for debugging and will be reverted The cross test `test_pt_tf_model_equivalence` passes locally but fails when running on the CI. This commit is to help debug that and will be reverted. * Revert "[DO NOT MERGE]: This is a commit for debugging and will be reverted" This reverts commit 8f0d709ec5bd46685fb0b4259d914ffee794875b. * [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted * [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted * Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted" This reverts commit 998cc38b8c3d313bf5e5eb55a7f5b7b881897b89. * Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted" This reverts commit 1c695ac4219c4ae4d39b330b01744dc27deb7dd4. * Don't skip test_save_load IIRC test_save_load was also failing on the CI but not on my local box, it might be easier to debug that on the CI first than the cross tests * Debugging commit, will be reverted * Revert "Debugging commit, will be reverted" This reverts commit 8eafc8e41e20c4e95a3a90834f06a6e9f445e2d5. * Override `test_save_load` and push model to save Maybe this will help me repro this weird bug * pass my repo_id * add endpoint * Pass a temp (write) token just for this CI * Undo last few commits, still pushing to hub for model debugging The issue seems to be with save_pretrained(), when I looked at the model saved from the CI test failure it is basically empty and has no weights. `self.save_weights(..)` seems to be failing in save_pretrained but needs more debugging * Add logging to modeling tf utils, will be reverted just for debugging * Debugging, will revert * Revert "Debugging, will revert" This reverts commit 9d0d3075fb7c82d8cde3a5c76bc8f3876c5c55d3. * Revert "Add logging to modeling tf utils, will be reverted just for debugging" This reverts commit 774b6b7b1c17b3ce5d7634ade768f2f686cee617. * Remove `test_save_load` The CI failures are gone after my latest rebase, no idea why but I was still saving the model to my hub on HF and the tf_model.h5 file now has everything. * Run make fix-copies * Run ruff format tests src utils * Debugging commit, will be reverted * Run ruff, also trigger CI run * Run ruff again * Undo debugging commit --------- Co-authored-by:
Matt <Rocketknight1@users.noreply.github.com>
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- 10 May, 2024 1 commit
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Sungkyun Chang authored
Update llama3.md fix typo again
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- 09 May, 2024 1 commit
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Omar Sanseviero authored
* Update CodeLlama references * Update slow_documentation_tests.txt * Update slow_documentation_tests.txt
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- 08 May, 2024 1 commit
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Pavel Iakubovskii authored
* Training script for object detection * Evaluation script for object detection * Training script for object detection with eval loop outside trainer * Trainer DETR finetuning * No trainer DETR finetuning * Eval script * Refine object detection example with trainer * Remove commented code and enable telemetry * No trainer example * Add requirements for object detection examples * Add test for trainer example * Readme draft * Fix uploading to HUB * Readme improvements * Update eval script * Adding tests for object-detection examples * Add object-detection example * Add object-detection resources to docs * Update README with custom dataset instructions * Update year * Replace valid with validation * Update instructions for custom dataset * Remove eval script * Remove use_auth_token * Add copied from and telemetry * Fixup * Update readme * Fix id2label * Fix links in docs * Update examples/pytorch/object-detection/run_object_detection.py Co-authored-by:
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- 06 May, 2024 1 commit
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Sungkyun Chang authored
Update llama3.md fix typo
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- 02 May, 2024 1 commit
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amitportnoy authored
* phi3 chat_template does not support system role * fix doc test error
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- 26 Apr, 2024 3 commits
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Eitan Turok authored
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Eduardo Pacheco authored
* Fixed SegGptImageProcessor to handle 2D and 3D prompt mask inputs * Added new test to check prompt mask equivalence * New proposal * Better proposal * Removed unnecessary method * Updated seggpt docs * Introduced do_convert_rgb * nits
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JB (Don) authored
* Adding SDPA support for BERT * Using the proper input name for testing model input in inference() * Adding documentation for SDPA in BERT model page * Use the stable link for the documentation * Adding a gate to only call .contiguous() for torch < 2.2.0 * Additions and fixes to the documentation * Minor updates to documentation * Adding extra requirements needed for the contiguous() bug * Adding "Adapted from" in plcae of the "Copied from" * Add benchmark speedup tables to the documentation * Minor fixes to the documentation * Use ClapText as a replacemenet for Bert in the Copied-From * Some more fixes for the fix-copies references * Overriding the test_eager_matches_sdpa_generate in bert tests to not load with low_cpu_mem_usage [test all] * Undo changes to separate test * Refactored SDPA self attention code for KV projections * Change use_sdpa to attn_implementation * Fix test_sdpa_can_dispatch_on_flash by preparing input (required for MultipleChoice models)
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- 24 Apr, 2024 2 commits
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Gustavo de Rosa authored
* chore(root): Initial commit of Phi-3 files. * fix(root): Fixes Phi-3 missing on readme. * fix(root): Ensures files are consistent. * fix(phi3): Fixes unit tests. * fix(tests): Fixes style of phi-3 test file. * chore(tests): Adds integration tests for Phi-3. * fix(phi3): Removes additional flash-attention usage, .e.g, swiglu and rmsnorm. * fix(phi3): Fixes incorrect docstrings. * fix(phi3): Fixes docstring typos. * fix(phi3): Adds support for Su and Yarn embeddings. * fix(phi3): Improves according first batch of reviews. * fix(phi3): Uses up_states instead of y in Phi3MLP. * fix(phi3): Uses gemma rotary embedding to support torch.compile. * fix(phi3): Improves how rotary embedding classes are defined. * fix(phi3): Fixes inv_freq not being re-computed for extended RoPE. * fix(phi3): Adds last suggestions to modeling file. * fix(phi3): Splits inv_freq calculation in two lines.
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Arthur authored
* nuke * add co-author * add co-author * update card * fixup and fix copies to please our ci * nit fixup * super small nits * remove tokenizer_path from call to `write_model` * always safe serialize by default --------- Co-authored-by:
pcuenca <pcuenca@users.noreply.github.com> Co-authored-by:
xenova <xenova@users.noreply.github.com>
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- 22 Apr, 2024 2 commits
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Kamil Akesbi authored
* add sdpa to wav2vec. Co-authored-by:
kamilakesbi <kamil@huggingface.co> Co-authored-by:
jp1924 <jp42maru@gmail.com> * add fa2 to wav2vec2 * add tests * fix attention_mask compatibility with fa2 * minor dtype fix * replace fa2 slow test * fix fa2 slow test * apply code review + add fa2 batch test * add sdpa and fa2 to hubert * sdpa and fa2 to data2vec_audio * sdpa and fa2 to Sew * sdpa to unispeech + unispeech sat * small fix * attention mask in tests Co-authored-by:
Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * add_speedup_benchmark_to_doc --------- Co-authored-by:
kamil@huggingface.co <kamil.akesbi@gmail.com> Co-authored-by:
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Merve Noyan authored
* Update llava_next.md * Update seggpt.md
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- 19 Apr, 2024 2 commits
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NielsRogge authored
* Add resources * Address comments * Apply suggestions from code review Co-authored-by:
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Jo茫o David authored
* Duplicate swiftformer * Convert SwiftFormerPatchEmbedding * Convert SwiftFormerEmbeddings * Convert TFSwiftFormerMlp * Convert TFSwiftFormerConvEncoder * Convert TFSwiftFormerLocalRepresentation * convert TFSwiftFormerEncoderBlock * Convert SwiftFormerStage * Convert SwiftFormerEncoder * Add TFSWiftFormerPreTrainedModel * Convert SwiftFormerForImageClassification * Add kwargs and start drop path * Fix syntax * Change Model class name * Add TFSwiftFormer to __init__ * Duplicate test_modeling_swiftformer * First test conversions * Change require_torch to require_tf * Add exports to swiftformer __init__ * Add TFSwiftFormerModel wrapper * Fix __init__ and run black * Remove docstring from MainLayer, fix padding * Use keras.layers.Activation on keras.Sequential * Fix swiftformer exports * Fix activation layer from config * Remove post_inits * Use tf.keras.layers.ZeroPadding2D * Convert torch normalize * Change tf test input shape * Fix softmax and reduce_sum * Convert expand_dims and repeat * Add missing reshape and tranpose * Simplify TFSwiftFormerEncoderBlock.call * Fix mismatch in patch embeddings * Fix expected output shape to match channels last * Fix swiftformer typo * Disable test_onnx * Fix TFSwiftFormerForImageClassification call * Add unpack inputs * Convert flatten(2).mean(-1) * Change vision dummy inputs (to be reviewed) * Change test_forward_signature to use .call * Fix @unpack_inputs * Set return_tensors="tf" and rename class * Rename wrongly named patch_embeddings layer * Add serving_output and change dummy_input shape * Make dimensions BCHW and transpose inside embedding layer * Change SwiftFormerEncoderBlock * Fix ruff problems * Add image size to swiftformer config * Change tranpose to MainLayer and use -1 for reshape * Remove serving_outputs and dummy_inputs * Remove test_initialization test from tf model * Make Sequential component a separate layer * Fix layers' names * Tranpose encoder outputs * Fix tests and check if hidden states is not None * Fix TFSwiftFormerForImageClassification * Run make fixup * Run make fix-copies * Update modeling_tf_auto * Update docs * Fix modeling auto mapping * Update modelint_tf_swiftformer docs * Fill image_size doc and type * Add reduction=None to loss computation * Update docs * make style * Debug: Delete the tip to see if that changes anything * Re-add tip * Remove add_code_sample_docstrings * Remove unused import * Get the debug to actually tell us the problem it has with the docs * Try a substitution to match the PyTorch file? * Add swiftformer to ignore list * Add build() methods * Update copyright year Co-authored-by:
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- 18 Apr, 2024 2 commits
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Abhi Venigalla authored
* wip * fix __init__.py * add docs * Apply suggestions from code review Co-authored-by:
Wing Lian <wing.lian@gmail.com> * default to using router_aux_loss_coef over ffn_config[moe_loss_weight] * fix _flash_attn_uses_top_left_mask and is_causal * fix tests path * don't use token type IDs * follow Llama and remove token_type_ids from test * init ConfigTester differently so tests pass * remove multiple choice test * remove question + answer test * remove sequence classification test * remove token classification test * copy Llama tests and remove token_type_ids from test inputs * do not test pruning or headmasking; style code * add _tied_weights_keys parameter to pass test * add type hints * fix type check * update config tester * remove masked_lm test * remove encoder tests * initialize DbrxModelTester with correct params * style * torch_dtype does not rely on torch * run make fixup, fix-copies * use https://huggingface.co/v2ray/dbrx-base-fixed/blob/main/modeling_dbrx.py * add copyright info * fix imports and DbrxRotaryEmbedding * update DbrxModel docstring * use copies * change model path in docstring * use config in DbrxFFN * fix flashattention2, sdpaattention * input config to DbrXAttention, DbrxNormAttentionNorm * more fixes * fix * fix again! * add informative comment * fix ruff? * remove print statement + style * change doc-test * fix doc-test * fix docstring * delete commented out text * make defaults match dbrx-instruct * replace `router_aux_loss_coef` with `moe_loss_weight` * is_decoder=True * remove is_decoder from configtester * implement sdpa properly * make is_decoder pass tests * start on the GenerationTesterMixin tests * add dbrx to sdpa documentation * skip weight typing test * style * initialize smaller model Co-authored-by:
Abhi Venigalla <abhi.venigalla@databricks.com> Co-authored-by:
Eitan Turok <eitan.turok@databricks.com> Co-authored-by:
Eitan Turok <150733043+eitanturok@users.noreply.github.com> Co-authored-by:
Eitan Turok <eitanturok@gmail.com> Co-authored-by:
Your Name <you@example.com> Co-authored-by:
Mihir Patel <mihir.v.patel7@gmail.com> Co-authored-by:
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tomeras91 authored
* Add jamba arch * apply "make fix-copies" changes * fix link to model in JambaConfig docstring * Add n_ctx in modeling file because repo-consistency wants that * Add jamba to flash attention and sdpa documentation * mamba dt_proj quant fix now works for LoRA as well * override test_left_padding_compatibility and use a more permissive tolerance. left padding numerical difference are accentuated by mamba layers * add jamba to tokenization auto * fix comments of shape (PR #24 in the model page: https://huggingface.co/ai21labs/Jamba-v0.1/discussions/24) * simple PR fixes * remove unnecessary kwargs from JambaAttentionDecoderLayer and JambaMambaDecoderLayer * remove the LoRA hack for the mamba dt_proj bias. It was solved in huggingface/peft#1530 (https://github.com/huggingface/peft/pull/1530) * Add copied comment on JambaMLP (it's the same as MixtralMLP) * remove padding_mask warnings. It's not supported anymore * fix docstring. Float instead of int * A few more minor PR fixes * (1) lowercase names for mamba layernorms (2) remove _apply_inner_layernorms and do it directly in the forward pass * Return None attention weights from mamba layers. Append to all attentions only if not None. * remove some leftover jamba archive lists * Better separation between expert vs non-expert layers. non-expert layers return None as router_logits, and it is not concatenated to all_router_logits returned from JambaModel * no need to take router_logits at config.expert_layer_offset anymore. result.router_logits now holds results only for expert layers * Add Jamba paper on READMEs * (1) rename n_ctx -> max_position_embeddings (2) don't use it in the modeling file since it's not needed (set it as an exception to check_config_attributes) * Add copied from comment * remove the code path for apply_inner_layernorms=False. Jamba always has the inner mamba layernorms * clearer docstring for _convert_to_standard_cache * style fixes * Change calc_logits_for_entire_prompt (bool) to num_logits_to_keep (int). Adapt assisted decoding code tp use it. Also small change in low memory beam search decoding path to support this new int value in model_inputs * rename test so it still overrides what its meant to override * draft * oups * nit * remove more complexe logic * fix names used in config * fix fix fix * style * fix some more failing tests * generate did not init the cache
馃檭 * more small nits * typo * config.mamba_expand * config.hidden_size for the intermediate size of the mamba shapes * fix init of pkv with torch.tensor() * empty tensor * fix some init issues * stupid changes required by generate because it does not even support it's own DynamicCache class * more fixes * fix general assisted gen cache_position bug * tests passing * Add offsets and periods as SPECIAL_CASES_TO_ALLOW in check_config_attributes.py * fix reorder_cache to reorder mamba states and override some more functions in HybridMambaAttentionDynamicCache * no need to override test_past_key_values_format() and _check_past_key_values_for_generate() in tests anymore * fix docstrings and typehints for past_key_values * style fixes * fix docs * change typehint due to copy from Mixtral * forgot import * import order * Add configuration_jamba and modeling_jamba to not_doctested because the model is too big to download (in docstring of JambaForCausalLM.forward) * Add integration test with tiny tandom Jamba model on hub * fix flash attention cache shapes * bring back forgotten hidden states * rename HybridMambaAttentionDynamicCache.seqlen_offset to has_previous_state (and make bool) and bugfix - it should be set to True after a finished forward pass of the entire model * align integration test after modeling fixes * bugfix - mamba can use precomputed states only of forward pass is on a single token * bugfix - mamba can use precomputed states only if they match the batch size * typo * remove making _prepare_4d_causal_attention_mask a leaf function * stop using past_seq_len.get_seq_length(). Use cache positions instead. Adjust test (test_decoder_model_past_with_large_inputs) accordingly --------- Co-authored-by:Joao Gante <joao@huggingface.co>
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