Initialize _tqdm_active with hf_hub_utils.are_progress_bars_disabled() to respect HF_HUB_DISABLE_PROGRESS_BARS
It seems like enable_progress_bar() and disable_progress_bar() sync up with huggingface_hub, but the initial value is always True. This changes will make sure the user's preference is respected implicity on initialization.
The documentation says "We refer to this Model parallelism as “Vertical” because of how models are typically visualized.", but then visualizes the model horizontally. This change visualizes the model indeed vertically.
fix typo:
from:
"model = TFAutoModelForQuestionAnswering("distilbert-base-uncased")"
to:
model = TFAutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
* Changed type hinting for all attention inputs to 'Optional[Tuple[torch.FloatTensor,...]] = None'
* Fixed the ruff formatting issue
* fixed type hinting for all hidden_states to 'Optional[Tuple[torch.FloatTensor, ...]] = None'
* Changed type hinting in these 12 scripts modeling_dpr.py,modeling_nat.py,idefics/vision.py,modeling_tf_dpr.py,modeling_luke.py,modeling_swin.py,modeling_tf_swin.py,modeling_blip.py,modeling_tf_blip.py,modeling_donut_swin.py,modeling_dinat.py,modeling_swinv2.py
* test fail update
* fixed type hinting for these 15 scripts modeling_xlnet.py,modeling_tf_xlnet.py,modeling_led.py,modeling_tf_led.py,modleing_rwkv.py,modeling_dpt.py,modeling_tf_cvt.py,modeling_clip.py,modeling_flax_clip.py,modeling_tf_clip.py,modeling_longformer.py,modeling_tf_longformer.py,modeling_siglip.py,modeling_clap.py,modeling_git.py
* Changed type hinting in these 12 scripts modeling_dpr.py,modeling_nat.py,idefics/vision.py,modeling_tf_dpr.py,modeling_luke.py,modeling_swin.py,modeling_tf_swin.py,modeling_blip.py,modeling_tf_blip.py,modeling_donut_swin.py,modeling_dinat.py,modeling_swinv2.py
* test fail update
* Removed the myvenv file
* Fixed type hinting for these 8 scripts modeling_tvlt.py,modeling_sam.py,modeling_tf_sam.py,modeling_tvp.py,modeling_rag.py,modeling_tf_rag.py,modeling_tf_xlm.py,modeling_xlm.py
* fix the function load_balancing_loss_func in Mixtral_Moe to include attention_mask
* format code using black and ruff
* skip computing mask if attention_mask=None
* add tests for load balancing loss Mixtral-Moe
* fix assert loss is different in mixtral_test
* fix pad_leng
* use assertNotAlmostEqual and print to debug
* remove print for debug
* minor updates
* reduce rtol and atol
* fix a hidden bug of GenerationConfig
* keep `sort_keys=True` to maintain visibility
* Update src/transformers/generation/configuration_utils.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update configuration_utils.py
in case `obj` is a list, check the items in the list
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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add dataloader prefetch factor in training args and trainer
* remove trailing spaces
* prevent dataloader_num_workers == 0 and dataloader_prefetch_factor != None
dataloader_prefetch_factor works only when data is loaded in a different process as the main one. This commit adds the necessary checks to avoid having prefetch_factor set when there is no such process.
* Remove whitespaces in empty line
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/training_args.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
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Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Enable instantiating model with pretrained backbone weights
* Update tests so backbone checkpoint isn't passed in
* Remove doc updates until changes made in modeling code
* Clarify pretrained import
* Update configs - docs and validation check
* Update src/transformers/utils/backbone_utils.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Clarify exception message
* Update config init in tests
* Add test for when use_timm_backbone=True
* Small test updates
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update convert_llava_weights_to_hf.py script
* Remove config update of adding padding to `vocab_size` and `text_config.vocab_size` which causes `ValueError` exception.
* Remove keys that ends with `inv_freq` from the state dict.
* Add examples and instructions for creating `model_state_dict.bin` that can be used by the script.
* Update convert_llava_weights_to_hf.py
* Update convert_vipllava_weights_to_hf.py
* [DETA] fix freeze/unfreeze function
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* add freeze/unfreeze test case in DETA
* fix type
* fix typo 2
* fix : enable aux and enc loss in training pipeline
* Add unsynced variables from original DETA for training
* modification for passing CI test
* make style
* make fix
* manual make fix
* change deta_modeling_test of configuration 'two_stage' default to TRUE and minor change of dist checking
* remove print
* divide configuration in DetaModel and DetaForObjectDetection
* image smaller size than 224 will give topk error
* pred_boxes and logits should be equivalent to two_stage_num_proposals
* add missing part in DetaConfig
* Update src/transformers/models/deta/modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add docstring in configure and prettify TO DO part
* change distribute related code to accelerate
* Update src/transformers/models/deta/configuration_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/deta/test_modeling_deta.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* protect importing accelerate
* change variable name to specific value
* wrong import
* fix aux_loss in conditional_detr
* add test aux_loss
* add aux_loss test in deta and table_transformer
* fix yolos since it doesn't have auxiliary function
* fix maskformer auxiliary_loss related code
* make style
* change param 'auxiliary_loss' to 'use_auxiliary_loss'
* change param 'auxiliary_loss' to 'use_auxiliary_loss' in tests
* make style & fix-copies, also revert yolos related parameter
* revert variable name 'use_auxiliary_loss' to 'auxiliary_loss' due to DetrConfig
* revert variable name in yolos
* revert maskformer
* add aux_loss test in maskformer
* make style
* Update src/transformers/models/yolos/configuration_yolos.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Allow non-special tokens to be added
* Add test, fix token adding code
* Revert changes to id_to_token and token_to_id
* Update the ESM tokenizer to be a bit more standardized
* Update src/transformers/models/esm/tokenization_esm.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
