- 22 Dec, 2023 1 commit
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Younes Belkada authored
* add 4-bit serialization docs * up * up
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- 20 Dec, 2023 3 commits
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Joao Gante authored
Co-authored-by:Merve Noyan <merveenoyan@gmail.com>
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Steven Liu authored
* fsdp, debugging, gpu selection * fix hfoption * fix
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Sourab Mangrulkar authored
* fix fa2 * fix FA2 for popular models * improve warning and add Younes as co-author Co-Authored-By:
Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * Update src/transformers/modeling_utils.py Co-authored-by:
Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix the warning * Add Tip * typo fix * nit --------- Co-authored-by:
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- 19 Dec, 2023 1 commit
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Aaron Jimenez authored
Fix mistral link in mixtral.md
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- 18 Dec, 2023 4 commits
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Aaron Jimenez authored
Fix token link
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Steven Liu authored
* doc fix friday * deprecated objects * update not_doctested * update toctree
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Rockerz authored
Update semantic_segmentation.md
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Aeneas Stankowski authored
Update mixtral.md correct minor typo in overview
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- 15 Dec, 2023 4 commits
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Steven Liu authored
* mps docs * toctree
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Steven Liu authored
* first draft * add to toctree * edits * feedback
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Younes Belkada authored
* Update vipllava.md * Update modeling_vipllava.py
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Cylis authored
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- 14 Dec, 2023 1 commit
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Sanchit Gandhi authored
* [Seamless] Fix links in docs * apply suggestions from code review
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- 13 Dec, 2023 3 commits
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Rockerz authored
* upfaste * Update * Update docs/source/ja/model_doc/deformable_detr.md Co-authored-by:
Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/ja/model_doc/data2vec.md Co-authored-by:
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Aaron Jimenez authored
* Add glossary to es/_toctree.yml * Add glossary.md to es/ * A section translated * B and C section translated * Fix typo in en/glossary.md C section * D section translated | Add a extra line in en/glossary.md * E and F section translated | Fix typo in en/glossary.md * Fix words preentrenado * H and I section translated | Fix typo in en/glossary.md * L section translated * M and N section translated * P section translated * R section translated * S section translated * T section translated * U and Z section translated | Fix TensorParallel link in both files * Fix word
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Younes Belkada authored
* v1 * add-new-model-like * revert * fix forward and conversion script * revert * fix copies * fixup * fix * Update docs/source/en/index.md * Apply suggestions from code review * push * fix * fixes here and there * up * fixup and fix tests * Apply suggestions from code review * add docs * fixup * fixes * docstring * add docstring * fixup * docstring * fixup * nit * docs * more copies * fix copies * nit * update test
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- 12 Dec, 2023 1 commit
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Stas Bekman authored
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- 11 Dec, 2023 7 commits
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Anthony Susevski authored
* fixed typos (issue 27919) * Update docs/source/en/tasks/knowledge_distillation_for_image_classification.md Co-authored-by:
amyeroberts <22614925+amyeroberts@users.noreply.github.com> --------- Co-authored-by:
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Steven Liu authored
streamline
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NielsRogge authored
Update formats
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Timon K盲ch authored
fix parameter count in readme
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Merve Noyan authored
* Initial commit for AutoBackbone & Backbone * Added timm and clarified out_indices * Swapped the example to out_indices * fix toctree * Update autoclass_tutorial.md * Update backbones.md * Update autoclass_tutorial.md * Add dummy torch input instead * Add dummy torch input * Update autoclass_tutorial.md * Update backbones.md * minor fix * Update docs/source/en/main_classes/backbones.md Co-authored-by:
Maria Khalusova <kafooster@gmail.com> * Update docs/source/en/autoclass_tutorial.md Co-authored-by:
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Arthur authored
* up * up * test * logits ok * up * up * few fixes * conversion script * up * nits * nits * update * nuke * more updates * nites * fix many issues * nit * scatter * nit * nuke megablocks * nits * fix conversion script * nit * remove * nits * nit * update * oupsssss * change * nits device * nits * fixup * update * merge * add copied from * fix the copy mentions * update tests * more fixes * nits * conversion script * add parts of the readme * Update tests/models/mixtral/test_modeling_mixtral.py Co-authored-by:
Lysandre Debut <hi@lysand.re> * Update docs/source/en/model_doc/mixtral.md Co-authored-by:
younesbelkada <younesbelkada@gmail.com> Co-authored-by:
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NielsRogge authored
* More improvements * Improve variable names * Update READMEs, improve docs
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- 08 Dec, 2023 3 commits
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fxmarty authored
* add sdpa * wip * cleaning * add ref * yet more cleaning * and more :) * wip llama * working llama * add output_attentions=True support * bigcode sdpa support * fixes * gpt-bigcode support, require torch>=2.1.1 * add falcon support * fix conflicts falcon * style * fix attention_mask definition * remove output_attentions from attnmaskconverter * support whisper without removing any Copied from statement * fix mbart default to eager renaming * fix typo in falcon * fix is_causal in SDPA * check is_flash_attn_2_available in the models init as well in case the model is not initialized through from_pretrained * add warnings when falling back on the manual implementation * precise doc * wip replace _flash_attn_enabled by config.attn_implementation * fix typo * add tests * style * add a copy.deepcopy on the config in from_pretrained, as we do not want to modify it inplace * obey to config.attn_implementation if a config is passed in from_pretrained * fix is_torch_sdpa_available when torch is not installed * remove dead code * Update src/transformers/modeling_attn_mask_utils.py Co-authored-by:
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Aaron Jimenez authored
* Add pad_truncation to es/_toctree.yml * Add pad_truncation.md to es/ * Translated first two paragraph * Translated paddig argument section * Translated truncation argument section * Translated final paragraphs * Translated table * Fixed typo in the table of en/pad_truncation.md * Run make style | Fix a word * Add Padding (relleno) y el Truncation (truncamiento) in the final paragraphs * Fix relleno and truncamiento words
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Tom Aarsen authored
* Draft version of new KV Caching This should allow Attention Sinks (https://github.com/tomaarsen/attention_sinks) / StreamingLLM (https://arxiv.org/abs/2309.17453) to be easily implemented in a third-party or in transformers directly * Address numerous PR suggestions 1. Move layer_idx from cache to ...Attention. Removes confusing set_layer_idx magic. 2. Always convert past_key_values to Cache instance at the start of ...Attention, removes all other isinstance calls. 3. Remove __bool__ and __getitem__ magic as they're confusing. 4. past_key_values.update(key, value, idx) now returns key, value. 5. Add use_legacy_cache flag, defaults to None, i.e. Falsey. This breaks generate for now, until 1) the cache is used is generate() or 2) use_legacy_cache is defaulted to True in generate() until we change it in another PR. 6. Separate key_cache and value_cache. Some work is still needed to see if the SinkCache can conveniently be implemented with just one update method. * Implement the SinkCache through backward+forward rotations * Integrate (Sink)Cache with Llama FA2 * Set use_legacy_cache=True as default, allows for test passes * Move from/to_legacy_cache to ...Model class * Undo unnecessary newline change * Remove copy utility from deprecated OpenLlama * Match import style * manual rebase with main * Cache class working with generate (#1) * Draft version of new KV Caching This should allow Attention Sinks (https://github.com/tomaarsen/attention_sinks) / StreamingLLM (https://arxiv.org/abs/2309.17453 ) to be easily implemented in a third-party or in transformers directly * Address numerous PR suggestions 1. Move layer_idx from cache to ...Attention. Removes confusing set_layer_idx magic. 2. Always convert past_key_values to Cache instance at the start of ...Attention, removes all other isinstance calls. 3. Remove __bool__ and __getitem__ magic as they're confusing. 4. past_key_values.update(key, value, idx) now returns key, value. 5. Add use_legacy_cache flag, defaults to None, i.e. Falsey. This breaks generate for now, until 1) the cache is used is generate() or 2) use_legacy_cache is defaulted to True in generate() until we change it in another PR. 6. Separate key_cache and value_cache. Some work is still needed to see if the SinkCache can conveniently be implemented with just one update method. * Integrate (Sink)Cache with Llama FA2 * Move from/to_legacy_cache to ...Model class * Undo unnecessary newline change * Match import style * working generate * Add tests; Simplify code; Apply changes to Mistral and Persimmon * fix rebase mess * a few more manual fixes * last manual fix * propagate changes to phi * upgrade test * add use_legacy_cache docstring; beef up tests * reintroduce unwanted deletes --------- Co-authored-by:
Tom Aarsen <Cubiegamedev@gmail.com> * move import * add default to model_kwargs.get('use_legacy_cache') * correct failing test * Apply suggestions from code review Co-authored-by:
Patrick von Platen <patrick.v.platen@gmail.com> * apply PR suggestions * fix failing test * Apply suggestions from code review Co-authored-by:
Tom Aarsen <37621491+tomaarsen@users.noreply.github.com> * PR comments * tmp commit * add docstrings * more tests, more docstrings, add to docs * derp * tmp commit * tmp dbg * more dbg * fix beam search bug * cache can be a list of tuples in some models * fix group beam search * all but sinkcache integration tests * fix sink cache and add hard integration test * now also compatible with input_embeds input * PR comments * add Cache support to Phi+FA2 * make fixup --------- Co-authored-by:
Joao Gante <joao@huggingface.co> Co-authored-by:
Joao Gante <joaofranciscocardosogante@gmail.com> Co-authored-by:
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- 07 Dec, 2023 8 commits
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Rockerz authored
* Add models * Add more models * Update docs/source/ja/model_doc/convnextv2.md Co-authored-by:
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Dina Suehiro Jones authored
Updates the distributed CPU training documentation to add instructions for running on a Kubernetes cluster (#27780) * Updates the Distributed CPU documentation to add a Kubernetes example * Small edits * Fixing link * Adding missing new lines * Minor edits * Update to include Dockerfile snippet * Add comment about tuning env var * Updates based on review comments
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Steven Liu authored
* custom dataset * fix link * feedback
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Joao Gante authored
Co-authored-by: -
Younes Belkada authored
* add model like * logits match * minor fixes * fixes * up * up * add todo * llava processor * keep the processor simple * add conversion script * fixup * fix copies * up * add to index * fix config + logits * fix * refactor * more refactor * more refactor * fix copies * add authors * v1 tests * add `LlavaProcessor` in init * remove unneeded import * up * up * docs * up * fix CI * fix CI * add attention mask in test * make fixup * remove the vision model * that' s the dirty way to do it * nits * nits * updates * add more tests * add input tests * fixup * more styling * nits * updates amd cleanup * fixup the generation expected results * fix the testing script * some cleanup and simplification which does not work yet but almost there! * make correct dispatch operations * vectorize works for batch of images and text * last todos * nits * update test and modeling code * remove useless function for now * fix few issues * fix generation * some nits * add bakllava * nits * remove duplicated code * finis merge * cleanup * missed this line * fill the todos * add left padding offset * add left and rignt padding logic * bool to properly index * make sure * more cleanups * batch is fixed
馃槈 * add correct device for tensor creation * fix some dtype missmatch * ruff * update conversion script * Update src/transformers/__init__.py * fa 2 support + fix conversion script * more * correct reshaping * fix test dict * fix copies by ignoring * fix nit * skip clip vision model * fixup * fixup * LlavaForVisionText2Text -> LlavaForCausalLM * update * fix * raise correct errors * fix * docs * nuke for now * nits here and there * fixup * fix remaining tests * update LlavaForConditionalGeneration instead of CausalLM * fixups * pipeline support * slow and piepline tests * supports batch * nits * cleanup * fix first integration tests * add pad token where needed * correct etsts * fixups * update pipeline testr * fix quality * nits * revert unneeded change * nit * use BatchFeature * from ...feature_extraction_utils import BatchFeature * nits * nits * properly update * more f*** nits * fix copies * comment * keep slow test slow * Update src/transformers/models/llava/processing_llava.py Co-authored-by:Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by:
shauray8 <shauray8@users.noreply.github.com> Co-authored-by:
haotian-liu <haotian-liu@users.noreply.github.com> Co-authored-by:
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Susnato Dhar authored
* add FA and modify doc file * test_flash_attn_2_generate_padding_right test overwritten * comment * modify persimmon modeling file * added speedup graph * more changes
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Nolwenn Bernard authored
* Translation of autoclass tutorial * Update totree to keep only tutorial section * Translate title toctree * Fix typos * Update review comments
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Alex McKinney authored
* Copies `modeling_flax_gpt_neo.py` to start * MLP Block. WIP Attention and Block * Adds Flax implementation of `LlamaMLP` Validated with in-file test. Some slight numeric differences, but assuming it isn't an issue * Adds `FlaxLlamaRMSNorm` layer `flax.linen` includes `RMSNorm` layer but not necessarily in all versions. Hence, we add in-file. * Adds FlaxLlamaAttention Copied from GPT-J as it has efficient caching implementation as well as rotary embeddings. Notice numerically different, but not by a huge amount. Needs investigating * Adds `FlaxLlamaDecoderLayer` numerically inaccurate, debugging.. * debugging rotary mismatch gptj uses interleaved whilst llama uses contiguous i think they match now but still final result is wrong. maybe drop back to just debugging attention layer? * fixes bug with decoder layer still somewhat numerically inaccurate, but close enough for now * adds markers for what to implement next the structure here diverges a lot from the PT version. not a big fan of it, but just get something working for now * implements `FlaxLlamaBlockCollection`] tolerance must be higher than expected, kinda disconcerting * Adds `FlaxLlamaModule` equivalent PyTorch model is `LlamaModel` yay! a language model
馃 * adds `FlaxLlamaForCausalLMModule` equivalent to `LlamaForCausalLM` still missing returning dict or tuple, will add later * start porting pretrained wrappers realised it probably needs return dict as a prereq * cleanup, quality, style * readds `return_dict` and model output named tuples * (tentatively) pretrained wrappers work馃敟 * fixes numerical mismatch in `FlaxLlamaRMSNorm` seems `jax.lax.rsqrt` does not match `torch.sqrt`. manually computing `1 / jax.numpy.sqrt` results in matching values. * [WIP] debugging numerics * numerical match I think issue was accidental change of backend. forcing CPU fixes test. We expect some mismatch on GPU. * adds in model and integration tests for Flax Llama summary of failing: - mul invalid combination of dimensions - one numerical mismatch - bf16 conversion (maybe my local backend issue) - params are not FrozenDict * adds missing TYPE_CHECKING import and `make fixup` * adds back missing docstrings needs review on quality of docstrings, not sure what is required. Furthermore, need to check if `CHECKPOINT_FOR_DOC` is valid. See TODO * commenting out equivalence test as can just use common * debugging * Fixes bug where mask and pos_ids were swapped in pretrained models This results in all tests passing now馃敟 * cleanup of modeling file * cleanup of test file * Resolving simpler review comments * addresses more minor review comments * fixing introduced pytest errors from review * wip additional slow tests * wip tests need to grab a GPU machine to get real logits for comparison otherwise, slow tests should be okay * `make quality`, `make style` * adds slow integration tests - checking logits - checking hidden states - checking generation outputs * `make fix-copies` * fix mangled function following `make fix-copies` * adds missing type checking imports * fixes missing parameter checkpoint warning * more finegrained 'Copied from' tags avoids issue of overwriting `LLAMA_INPUTS_DOCSTRING` * swaps import guards ??? how did these get swapped initially? * removing `inv_freq` again as pytorch version has now removed * attempting to get CI to pass * adds doc entries for llama flax models * fixes typo in __init__.py imports * adds back special equivalence tests these come from the gpt neo flax tests. there is special behaviour for these models that needs to override the common version * overrides tests with dummy to see if CI passes need to fill in these tests later * adds my contribution to docs * `make style; make quality` * replaces random masking with fixed to work with flax version * `make quality; make style` * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by:Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by:
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- 06 Dec, 2023 2 commits
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Rockerz authored
* Add models * Add models and update `_toctree.yml` * Update docs/source/ja/model_doc/chinese_clip.md Co-authored-by:
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Younes Belkada authored
* add flash-attn-2 support for GPT-neo-x * fixup * add comment * revert * fixes * update docs * comment * again * fix copies * add plot + fix copies * Update docs/source/en/model_doc/gpt_neox.md
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- 05 Dec, 2023 2 commits
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Younes Belkada authored
Update quantization.md
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Aaron Jimenez authored
* Copy perplexity.md file to es/ folder * Adding perplexity to es/_toctree.yml * Translate first section * Calculating PPL section translate * Example section translate * fix translate of log-likehood * Fix title translate * Fix \ in second paragraph * Change verosimilitud for log-likelihood * Run 'make style'
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