- 11 Dec, 2023 8 commits
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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:
Arthur <48595927+ArthurZucker@users.noreply.github.com> * new test + conversion script * Apply suggestions from code review 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:
Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by:
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Arthur authored
* Skip nn.Module.reset_parameters * Actually skip * Check quality * Maybe change all inits * Fix init issues: only modify public functions * Add a small test for now * Style * test updates * style * nice tes * style * make it even faster * one more second * remove fx icompatible * Update tests/test_modeling_common.py Co-authored-by:
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fxmarty authored
fix sdpa dispatch
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NielsRogge authored
* More improvements * Improve variable names * Update READMEs, improve docs
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Yoach Lacombe authored
change dtype of some integration tests
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Yih-Dar authored
fix Co-authored-by:ydshieh <ydshieh@users.noreply.github.com>
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NielsRogge authored
Fix test
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Sangbum Daniel Choi authored
* [DETA] fix freeze/unfreeze function * Update src/transformers/models/deta/modeling_deta.py Co-authored-by:
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- 09 Dec, 2023 3 commits
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Brendan Fahy authored
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Justin Yu authored
* fix tune integration for ray 2.7+ Signed-off-by:
Justin Yu <justinvyu@anyscale.com> * add version check for ray tune backend availability Signed-off-by:
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Joshua Lochner authored
* [CLAP] Replace hard-coded batch size to enable dynamic ONNX export * Add back docstring
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- 08 Dec, 2023 18 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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Joao Gante authored
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zhc7 authored
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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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Zach Mueller authored
* Fuffill request * Add test * Better test * Apply suggestions from code review Co-authored-by:
amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by:
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Arthur authored
* fix llava * nits * attention_mask was forgotten * nice * :) * fixup
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Pedro Cuenca authored
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Yih-Dar authored
* fix * fix * fix * fix * fix --------- Co-authored-by: -
Yih-Dar authored
* fix * fix --------- Co-authored-by: -
Yih-Dar authored
fix Co-authored-by: -
Yoach Lacombe authored
* update converting script * make style
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Xin Qiu authored
* Fix issues in add and is_done for BeamHypotheses * make newly added arguments optional for better compatibility * Directly use cur_len as generated_len, add note for retrocompatibility * update test expectation * make cur_len represents the length of the entire sequence including the decoder prompt * remove redundant if/else in testing
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Xin Qiu authored
* Fix beam score calculation issue for tensorflow version * fix transition score computation error * make cur_len represent the entire sequence length including decoder prompt
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Jonathon Belotti authored
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Charbel Abi Daher authored
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Saibo-creator authored
Fix: Raise informative exception when `prefix_allowed_tokens_fn` return empty set of tokens (#27797) Co-authored-by: -
fxmarty authored
[
⚠ ️ removed a default argument] Make `AttentionMaskConverter` compatible with `torch.compile(..., fullgraph=True)` (#27868) * remove bugged torch.float32 default * add test * fix tests * fix test * fix doc -
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 11 commits
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Rockerz authored
* Add models * Add more models * Update docs/source/ja/model_doc/convnextv2.md Co-authored-by:
Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/ja/model_doc/convbert.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: -
Matt authored
* Un-skip tests * Add aliasing support to tf_to_pt_weight_rename * Refactor tf-to-pt weight rename for simplicity * Patch mobilebert * Let us pray that the transfo-xl one works * Add XGLM rename * Expand the test to see if we can get more models to break * Expand the test to see if we can get more models to break * Fix MPNet (it was actually an unrelated bug) * Fix MPNet (it was actually an unrelated bug) * Add speech2text fix * Update src/transformers/modeling_tf_pytorch_utils.py Co-authored-by:
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Yih-Dar authored
* fix --------- Co-authored-by: -
fxmarty authored
fix device of mask in tests
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Hz, Ji authored
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Sourab Mangrulkar authored
* update `create_model_card` to properly save peft details when using Trainer with PEFT * nit * Apply suggestions from code review Co-authored-by:
Benjamin Bossan <BenjaminBossan@users.noreply.github.com> --------- Co-authored-by:
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Yih-Dar authored
* fix --------- Co-authored-by:
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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
😉 * 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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