- 14 May, 2024 1 commit
-
-
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:
Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/jetmoe/modeling_jetmoe.py Co-authored-by:
Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * Update src/transformers/models/jetmoe/modeling_jetmoe.py Co-authored-by:
-
- 13 May, 2024 4 commits
-
-
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@gmail.com> Co-authored-by:
Matt <Rocketknight1@users.noreply.github.com>
-
Poedator authored
* 4d mask fixes * Update custom 4D mask logic * test moved to mixin * extra tests 4d mask * upd 4d mask and StaticCache handling * added Mask4DTestHard to mistral tests * post-rebase fixes * test fixes for StaticCache * make fix-copies * upd 1 after #30476 * fix common tests * rm elif attention_mask.dim() == 4: * tests combined, fixed, mixtral supported * bigbird style chg reverted * rm if attention_mask.dim() == 2 * modeling_llama formatting chg --------- Co-authored-by:Joao Gante <joao@huggingface.co>
-
Nilabhra Roy Chowdhury authored
* remove unrelated changes * remove unrelated changes on phi and stable LM * add: Test for Falcon 10B * fix: formatting * fix: loading the falcon 10B in 8 bit precision using bitsanbytes. * fix: device placement * fix: broken tests. * fix: backwards compatibility for falcon 1B architecture. * chore: updated test. * chore: test_modeling_falcon.py to use the 11B model. * chore: minor edit * chore: formating. --------- Co-authored-by:
Pablo Montalvo <39954772+molbap@users.noreply.github.com> Co-authored-by:
ArthurZucker <arthur.zucker@gmail.com>
-
Zafir Stojanovski authored
* blip with interpolated pos encoding * feat: Add interpolate_pos_encoding option to other models from `BLIP` family. * include check for textual generated content in tests
-
- 09 May, 2024 5 commits
-
-
Joao Gante authored
* tmp commit * [test_all] mvp * missing not * [test_all] final test fixes * fix musicgen_melody and rag * [test_all] empty commit * PR comments * Update src/transformers/generation/utils.py Co-authored-by:
-
Jacky Lee authored
* feat: add note about owlv2 * fix: post processing coordinates * remove: workaround document * fix: extra quotes * update: owlv2 docstrings * fix: copies check * feat: add unit test for resize * Update tests/models/owlv2/test_image_processor_owlv2.py Co-authored-by:
amyeroberts <22614925+amyeroberts@users.noreply.github.com> --------- Co-authored-by:
-
Lysandre Debut authored
* [test_all] Remove all imports Remove remaining ARCHIVE MAPS Remove remaining PRETRAINED maps * review comments * [test_all] empty commit to trigger tests
-
Jacky Lee authored
* feat: enable dynamic resolution for vivit * fix: formatting * remove: print statement for testing * Update src/transformers/models/vivit/modeling_vivit.py Co-authored-by:
-
David Xue authored
* Add interpolate positional encoding to siglip * Change # of patches for siglip interpolation test * fix formatting * Apply nit suggestions from code review Co-authored-by:
-
- 08 May, 2024 1 commit
-
-
Joao Gante authored
-
- 07 May, 2024 1 commit
-
-
JB (Don) authored
* Adding _tie_weights() to prediction heads to support low_cpu_mem_usage=True * Testing for the non-safe-tensors case, since the default is safe-tensors already * Running fixup/fix-copies * Adding accelerate annotations to tests
-
- 06 May, 2024 1 commit
-
-
Arthur authored
* change cis * nits * update * minor updates * [push-ci-image] * nit [push-ci-image] * nitsssss * [build-ci-image] * [push-ci-image] * [push-ci-image] * both * [push-ci-image] * this? * [push-ci-image] * pypi-kenlm needs g++ * [push-ci-image] * nit * more nits [push-ci-image] * nits [push-ci-image] * [push-ci-image] * [push-ci-image] * [push-ci-image] * add vision * [push-ci-image] * [push-ci-image] * add new dummy file but will need to update them [push-ci-image] * [push-ci-image] * show package size as well * [push-ci-image] * potentially ignore failures * workflow updates * nits [push-ci-image] * [push-ci-image] * fix consistency * clean nciida triton * also show big packages [push-ci-image] * nit * update * another one * line escape? * add accelerate [push-ci-image] * updates [push-ci-image] * nits to run tests, no push-ci * try to parse skip reason to make sure nothing is skipped that should no be skippped * nit? * always show skipped reasons * nits * better parsing of the test outputs * action="store_true", * failure on failed * show matched * debug * update short summary with skipped, failed and errors * nits * nits * coolu pdates * remove docbuilder * fix * always run checks * oups * nits * don't error out on library printing * non zero exi codes * no warning * nit * WAT? * format nit * [push-ci-image] * fail if fail is needed * [push-ci-image] * sound file for torch light? * [push-ci-image] * order is important [push-ci-image] * [push-ci-image] reduce even further * [push-ci-image] * use pytest rich ! * yes [push-ci-image] * oupsy * bring back the full traceback, but pytest rich should help * nit * [push-ci-image] * re run * nit * [push-ci-image] * [push-ci-image] * [push-ci-image] * empty push to trigger * [push-ci-image] * nit? [push-ci-image] * empty * try to install timm with no deps * [push-ci-image] * oups [push-ci-image] * [push-ci-image] * [push-ci-image] ? * [push-ci-image] open ssh client for git checkout fast * empty for torch light * updates [push-ci-image] * nit * @v4 for checkout * [push-ci-image] * [push-ci-image] * fix fetch tests with parallelism * [push-ci-image] * more parallelism * nit * more nits * empty to re-trigger * empty to re-trigger * split by timing * did not work with previous commit * junit.xml * no path? * mmm this? * junitxml format * split by timing * nit * fix junit family * now we can test if the xunit1 is compatible! * this? * fully list tests * update * update * oups * finally * use classname * remove working directory to make sure the path does not interfere * okay no juni should have the correct path * name split? * sort by classname is what make most sense * some testing * naem * oups * test something fun * autodetect * 18? * nit * file size? * uip * 4 is best * update to see versions * better print * [push-ci-image] * [push-ci-image] * please install the correct keras version * [push-ci-image] * [push-ci-image] * [push-ci-image] * [push-ci-image] * [push-ci-image] * uv is fucking me up * [push-ci-image] * [push-ci-image] * [push-ci-image] * nits * [push-ci-image] * [push-ci-image] * install issues an pins * tapas as well * nits * more paralellism * short tb * soundfile * soundfile * [push-ci-image] * [push-ci-image] * [push-ci-image] * oups * [push-ci-image] * fix some things * [push-ci-image] * [push-ci-image] * [push-ci-image] * [push-ci-image] * use torch-light for hub * small git lfs for hub job * [push-ci-image] * [push-ci-image] * [push-ci-image] * [push-ci-image] * fix tf tapas * [push-ci-image] * nits * [push-ci-image] * don't update the test * [push-ci-image] * [push-ci-image] * [push-ci-image] * no use them * [push-ci-image] * [push-ci-image] * [push-ci-image] * [push-ci-image] * update tf proba * [push-ci-image] * [push-ci-image] * woops * [push-ci-image] * [push-ci-image] * [push-ci-image] * [push-ci-image] * [push-ci-image] * [push-ci-image] * test with built dockers * [push-ci-image] * skip annoying tests * revert fix copy * update test values * update * last skip and fixup * nit * ALL GOOOD * quality * Update tests/models/layoutlmv2/test_image_processing_layoutlmv2.py * Update docker/quality.dockerfile Co-authored-by:
Lysandre Debut <hi@lysand.re> * Update src/transformers/models/tapas/modeling_tf_tapas.py Co-authored-by:
-
- 02 May, 2024 2 commits
-
-
Jonghwan Hyeon authored
* Output `None` as attention when layer is skipped * Add test for output_attentions
-
Richard Brown authored
* Update image_processing_vitmatte.py * add test * [run-slow]vitmatte
-
- 01 May, 2024 3 commits
-
-
Fraser Mince authored
* Ensure input_embeds and image_features are the same dtype in autocast * Fix nans in half precision llava-next and fix autocasting behavior. * Fix styling issues. * fix randn newline instantiation * fix broken slow llava test * Fix llava next init. * fix styling issues * [run-slow]llava,llava_next * fix styling issues
-
Raushan Turganbay authored
* move scaling to nn.Module * let the test be here for now (need to fix) * failing tests * last failing models * Revert commit 4c14817f38 * clean-up * oops forgot * codestyle * raise NotImplemented when possible * Update tests/test_modeling_common.py Co-authored-by:
-
Raushan Turganbay authored
use text config's vocab size
-
- 30 Apr, 2024 3 commits
-
-
Yih-Dar authored
* fix * add test --------- Co-authored-by:ydshieh <ydshieh@users.noreply.github.com>
-
Jiarui Xu authored
* add_blip_get_multimodal_feautres * Fix docstring error * reimplement get_multimodal_features * fix error * recheck code quality * add new necessary tests
-
Joao Gante authored
-
- 26 Apr, 2024 3 commits
-
-
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
-
amyeroberts authored
* Enable instantiating model with pretrained backbone weights * Clarify pretrained import * Use load_backbone instead * Add backbone_kwargs to config * Fix up * Add tests * Tidy up * Enable instantiating model with pretrained backbone weights * Update tests so backbone checkpoint isn't passed in * Clarify pretrained import * Update configs - docs and validation check * Update src/transformers/utils/backbone_utils.py Co-authored-by:
-
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)
-
- 25 Apr, 2024 3 commits
-
-
Raushan Turganbay authored
-
Yoach Lacombe authored
* first modeling code * make repository * still WIP * update model * add tests * add latest change * clean docstrings and copied from * update docstrings md and readme * correct chroma function * correct copied from and remove unreleated test * add doc to toctree * correct imports * add convert script to notdoctested * Add suggestion from Sanchit Co-authored-by:
Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * correct get_uncoditional_inputs docstrings * modify README according to SANCHIT feedback * add chroma to audio utils * clean librosa and torchaudio hard dependencies * fix FE * refactor audio decoder -> audio encoder for consistency with previous musicgen * refactor conditional -> encoder * modify sampling rate logics * modify license at the beginning * refactor all_self_attns->all_attentions * remove ignore copy from causallm generate * add copied from for from_sub_models * fix make copies * add warning if audio is truncated * add copied from where relevant * remove artefact * fix convert script * fix torchaudio and FE * modify chroma method according to feedback-> better naming * refactor input_values->input_features * refactor input_values->input_features and fix import fe * add input_features to docstrigs * correct inputs_embeds logics * remove dtype conversion * refactor _prepare_conditional_hidden_states_kwargs_for_generation ->_prepare_encoder_hidden_states_kwargs_for_generation * change warning for chroma length * Update src/transformers/models/musicgen_melody/convert_musicgen_melody_transformers.py Co-authored-by:
-
amyeroberts authored
-
- 24 Apr, 2024 4 commits
-
-
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.
-
Eduardo Pacheco authored
* Fixed main train issues * Added loss test * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by:
-
Arthur authored
* nit * nit and fmt skip * fixup * Update src/transformers/convert_slow_tokenizer.py Co-authored-by:
-
Pavel Iakubovskii authored
* Add test for square image that fails * Fix for square images * Extend test cases * Fix resizing in tests * Style fixup
-
- 23 Apr, 2024 3 commits
-
-
Arthur authored
* push legacy to fast as well * super strange * Update src/transformers/convert_slow_tokenizer.py * make sure we are BC * fix Llama test * nit * revert * more test * style * update * small update w.r.t tokenizers * nit * don't split * lol * add a test for `add_prefix_space=False` * fix gemma tokenizer as well * update * fix gemma * nicer failures * fixup * update * fix the example for legacy = False * use `huggyllama/llama-7b` for the PR doctest * nit * use from_slow * fix llama
-
Fanli Lin authored
* add cuda flag * check for sdpa * add bitsandbytes
-
Eduardo Pacheco authored
* Added cross attention support * Fixed dtypes * Fixed assumption * Moved to decoder
-
- 22 Apr, 2024 3 commits
-
-
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:
kamil@huggingface.co <kamil.akesbi@gmail.com> Co-authored-by:
-
Pavel Iakubovskii authored
* Add class_embed to tied weights for DETA * Fix test_tied_weights_keys for DETA model * Replace error raise with assert statement
-
Joao Gante authored
fix test
-
- 19 Apr, 2024 3 commits
-
-
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:
-
Raushan Turganbay authored
* remove seq length from generation tests * style and quality * [test_all] & PR suggestion Co-authored-by:
Joao Gante <joaofranciscocardosogante@gmail.com> * Update tests/generation/test_utils.py Co-authored-by:
-
Sanchit Gandhi authored
* fix tests * style * more fixes * move model to device * move logits to cpu * update expected values * use ungated dataset * fix * fix * update --------- Co-authored-by:
-
