- 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 2 commits
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Omar Sanseviero authored
* Update CodeLlama references * Update slow_documentation_tests.txt * Update slow_documentation_tests.txt
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Joao Gante authored
* min_p * more relaxed test to avoid numerical issues * Update src/transformers/generation/logits_process.py Co-authored-by:
menhguin <minh1228@gmail.com> * Update src/transformers/generation/configuration_utils.py Co-authored-by:
amyeroberts <22614925+amyeroberts@users.noreply.github.com> * make fixup --------- Co-authored-by:
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- 08 May, 2024 2 commits
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Pavel Iakubovskii authored
* Object detection guide * Minor update * Minor updates, links * Fix typo * Wording, add albu space * Add missing part * Update docs/source/en/tasks/object_detection.md Co-authored-by:
NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update docs/source/en/tasks/object_detection.md Co-authored-by:
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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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- 07 May, 2024 1 commit
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Aymeric Roucher authored
* Create CodeAgent and ReactAgent * Fix formatting errors * Update documentation for agents * Add custom errors, improve logging * Support variable usage in ReactAgent * add messages * Add message passing format * Create React Code Agent * Update * Refactoring * Fix errors * Improve python interpreter * Only non-tensor inputs should be sent to device * Calculator tool slight refactor * Improve docstrings * Refactor * Fix tests * Fix more tests * Fix even more tests * Fix tests by replacing output and input types * Fix operand type issue * two small fixes * EM TTS * Fix agent running type errors * Change text to speech tests to allow changed outputs * Update doc with new agent types * Improve code interpreter * If max iterations reached, provide a real answer instead of an error * Add edge case in interpreter * Add safe imports to the interpreter * Interpreter tweaks: tuples and listcomp * Make style * Make quality * Add dictcomp to interpreter * Rename ReactJSONAgent to ReactJsonAgent * Misc changes * ToolCollection * Rename agent's logger to self.logger * Add while loops to interpreter * Update doc with new tools. still need to mention collections * Add collections to the doc * Small fixes on logs and interpretor * Fix toolbox return type * Docs + fixup * Skip doctests * Correct prompts with improved examples and formatting * Update prompt * Remove outdated docs * Change agent to accept Toolbox object for tools * Remove calculator tool * Propagate removal of calculator in doc * Fix 2 failing workflows * Simplify additional argument passing * AgentType audio * Minor changes: function name, types * Remove calculator tests * Fix test * Fix torch requirement * Fix final answer tests * Style fixes * Fix tests * Update docstrings with calculator removal * Small type hint fixes * Update tests/agents/test_translation.py Co-authored-by:
Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update tests/agents/test_python_interpreter.py Co-authored-by:
Cyril Kondratenko <kkn1993@gmail.com> Co-authored-by:
Lysandre <lysandre@huggingface.co> Co-authored-by:
Lysandre <lysandre.debut@reseau.eseo.fr> 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 4 commits
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mobicham authored
* update HQQ transformers integration * push import_utils.py * add force_hooks check in modeling_utils.py * fix | with Optional * force bias as param * check bias is Tensor * force forward for multi-gpu * review fixes pass * remove torch grad() * if any key in linear_tags fix * add cpu/disk check * isinstance return * add multigpu test + refactor tests * clean hqq_utils imports in hqq.py * clean hqq_utils imports in quantizer_hqq.py * delete hqq_utils.py * Delete src/transformers/utils/hqq_utils.py * ruff init * remove torch.float16 from __init__ in test * refactor test * isinstance -> type in quantizer_hqq.py * cpu/disk device_map check in quantizer_hqq.py * remove type(module) nn.linear check in quantizer_hqq.py * add BaseQuantizeConfig import inside HqqConfig init * remove hqq import in hqq.py * remove accelerate import from test_hqq.py * quant config.py doc update * add hqqconfig to main_classes doc * make style * __init__ fix * ruff __init__ * skip_modules list * hqqconfig format fix * hqqconfig doc fix * hqqconfig doc fix * hqqconfig doc fix * hqqconfig doc fix * hqqconfig doc fix * hqqconfig doc fix * hqqconfig doc fix * hqqconfig doc fix * hqqconfig doc fix * test_hqq.py remove mistral comment * remove self.using_multi_gpu is False * torch_dtype default val set and logger.info * hqq.py isinstance fix * remove torch=None * torch_device test_hqq * rename test_hqq * MODEL_ID in test_hqq * quantizer_hqq setattr fix * quantizer_hqq typo fix * imports quantizer_hqq.py * isinstance quantizer_hqq * hqq_layer.bias reformat quantizer_hqq * Step 2 as comment in quantizer_hqq * prepare_for_hqq_linear() comment * keep_in_fp32_modules fix * HqqHfQuantizer reformat * quantization.md hqqconfig * quantization.md model example reformat * quantization.md # space * quantization.md space }) * quantization.md space }) * quantization_config fix doc Co-authored-by:
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Joao Gante authored
* add missing docstrings to docs * Update src/transformers/generation/stopping_criteria.py Co-authored-by:
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Joao Gante authored
* does this work? * like this? * fix the other generate links * missing these
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amitportnoy authored
* phi3 chat_template does not support system role * fix doc test error
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- 01 May, 2024 2 commits
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NielsRogge authored
* Add improvements * Address comment
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amyeroberts authored
Fix image segmentation example - don't repoen image
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- 30 Apr, 2024 2 commits
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Joao Gante authored
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Joao Gante authored
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- 29 Apr, 2024 1 commit
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clinty authored
Co-authored-by:Clint Adams <clint@debian.org>
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- 26 Apr, 2024 4 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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amyeroberts authored
Fix GroundingDINO, DPR after BET SDPA update
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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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- 25 Apr, 2024 3 commits
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Matt authored
* Draft tutorial for talking to chat models * Reformat lists and text snippets * Cleanups and clarifications * Finish up remaining TODOs * Correct section link * Small fix * Add proper quantization examples * Add proper quantization examples * Add proper quantization examples * Update docs/source/en/conversations.md Co-authored-by:
Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/conversations.md Co-authored-by:
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manju rangam authored
* Fix issue #29817 Video Classification Task Guide Using Undeclared Variables * Update docs/source/en/tasks/video_classification.md updated with review comments Co-authored-by:
manju-rangam <Manju1@Git> Co-authored-by:
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Alexander Visheratin authored
* Added WSD scheduler. * Added tests. * Fixed errors. * Fix formatting. * CI fixes.
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- 24 Apr, 2024 4 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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Lysandre Debut authored
* Remove add-new-model in favor of add-new-model-like * nits
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Lysandre Debut authored
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- 23 Apr, 2024 2 commits
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Steven Basart authored
torch.run does not exist anywhere as far as I can tell.
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Matt authored
* Remove old TF port guide * repo-consistency * Remove some translations as well for consistency * Remove some translations as well for consistency
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- 22 Apr, 2024 4 commits
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Steven Liu authored
* first draft * feedback * static cache snippet * feedback * feedback
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zhong zhuang authored
* [FEAT]: EETQ quantizer support * Update quantization.md * Update docs/source/en/main_classes/quantization.md Co-authored-by:
Marc Sun <57196510+SunMarc@users.noreply.github.com> * Update docs/source/en/quantization.md Co-authored-by:
Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * add copyright * Update quantization.md * Update docs/source/en/quantization.md Co-authored-by:
Marc Sun <marc@huggingface.co> Co-authored-by:
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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 3 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:
Matt <Rocketknight1@users.noreply.github.com> * Raise error if drop_path_rate > 0.0 * Apply suggestions from code review Replace (self.dim), with self.dim, Co-authored-by:
Matt <rocketknight1@gmail.com> Co-authored-by:
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Matt authored
* initial commit, remove warnings on default chat templates * stash commit * Raise a much sterner warning for default chat templates, and prepare for depreciation * Update the docs
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- 18 Apr, 2024 4 commits
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Zach Mueller authored
* Alias * Note alias * Tests and src * Rest * Clean * Change typing? * Fix tests * Deprecation versions
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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:Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by:
Joao Gante <joao@huggingface.co>
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Alexander Visheratin authored
* Added flash attention 2. * Fixes. * Fix inheritance. * Fixed init. * Remove stuff. * Added documentation. * Add FA2 to M2M100 documentation. * Add test. * Fixed documentation. * Update src/transformers/models/m2m_100/modeling_m2m_100.py Co-authored-by:
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