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- 27 Mar, 2023 11 commits
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NielsRogge authored
* First draft * Fix integration test * Remove script * Fix test and typos * Fix one more test * Skip tied embeddings test * Remove line * Address comments
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Sylvain Gugger authored
* Report safetensors version in transformers-cli env * Styling * Trigger CI maybe
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Younes Belkada authored
for rg to be `False`
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Joao Gante authored
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Nathan Fradet authored
* seq2seq trainer and training arguments accepting GenerationConfig arg * seq2seq Trainer and training arguments docstring fixes * Update training_args_seq2seq.py docstring Co-authored-by:
Joao Gante <joaofranciscocardosogante@gmail.com> * Fixing trainer_seq2seq.py docstring Co-authored-by:
Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * seq2seq trainer: legacy gen args back & GenerationConfig created at init * Seq2seq trainer: fix in case gen_config.max_new_tokens is None Co-authored-by:
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Vladislav Sokolovskii authored
* Wav2Vec2ProcessorWithLM can return N best hypotheses now Signed-off-by:
Vladislav Sokolovskii <vladislav@parrothq.com> * Wav2Vec2ProcessorWithLM n_best cannot be None Co-authored-by:
amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Batch decoding can return N best hypotheses now batch_decode was extended with the same functionality as decode function, N best hypotheses per sample can be returned Signed-off-by:
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кѳѳsнī authored
balanced 8bit memory
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Sylvain Gugger authored
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Nicola Procopio authored
* updated toctree * added and translated mdx documents
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Charlie-Bell authored
Edited one line in src/transormers/generation/utils.py. Changed dist.world_size() to dist.get_world_size() since world_size() doesn't exist in pytorch.dist.
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Joao Gante authored
* missing cmake * more cmake
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- 24 Mar, 2023 12 commits
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Stas Bekman authored
* [safetensors] don't use in pt<1.10 * better fix
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Sylvain Gugger authored
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Stas Bekman authored
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Shubhamai authored
* [WIP] flax resnet * added pretrained flax models, results reproducible * Added pretrained flax models, results reproducible * working on tests * no real code change, just some comments * [flax] adding support for batch norm layers * fixing bugs related to pt+flax integration * removing loss from modeling flax output class * fixing classifier tests * fixing comments, model output * cleaning comments * review changes * review changes * Apply suggestions from code review Co-authored-by:
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Joao Gante authored
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Samuel Bubán authored
* Improve error message * Fix consistency
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Sylvain Gugger authored
* Pin tensorflow-text to go with tensorflow * Make it more convenient to pin TensorFlow * setup don't like f-strings
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Yih-Dar authored
* update docker files to use official torch 2.0.0 --------- Co-authored-by:ydshieh <ydshieh@users.noreply.github.com>
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Mitch Naylor authored
* add mega file structure and plain pytorch version of mega source code * added config class with old naming conventions * filled in mega documentation * added config class and embeddings with optional token types * updated notes * starting the conversion process, deleted intermediate and added use_cache back to config * renamed config attributes in modeling_mega.py * checkpointing before refactoring incremental decoding functions * removed stateful incremental key/values for EMA and self-attention * refactored MovingAverageGatedAttention to remove stateful k/v history and use unified attention mask * MovingAverageGatedAttention works with incremental decoding + past values, added sequence length enforcement * more comments in MovingAverageGatedAttention + checkpointing before GatedCrossAttention * bug fix in attention mask handling in MovingAverageGatedAttention * removed incremental state from GatedCrossAttention and removed IncrementalState class * finished gated cross attention and got MegaLayer working * fixed causal masking in mega decoder * fixed how padding and causal masks are passed through MegaLayer with and without k/v caching * finished MegaModel; tested with encoder, decoder-only, and cross-attention type inputs; started work on downstream classes; removed mentions of position_ids * added optional dense hidden layer for masked and causal LM classes * docstring updates in MultiHeadEMA and GatedCrossAttention, removed unnecessary inputs in cross-attention * removed before_attn_fn in Mega class and updated docstrings and comments up to there * bug fix in MovingAverageGatedAttention masking * working conversion of MLM checkpoint in scratchpad script -- perfect matches * moved arg for hidden dense layer in LM head to config; discovered issue where from_pretrained is renaming gamma and beta parameters * renamed gamma and beta parameters to avoid HF renaming when loading from checkpoint * finished checkpoint conversion script * cleanup old class in mega config script * removed 'copied from' statements and passing integration tests * added num_attention_heads=1 to config for integration compatibility, decoder tests working, generation tests failing * fixed tuple output of megamodel * all common tests passing after fixing issues in decoder, gradient retention, and initialization * added mega-specific tests, ready for more documentation and style checks * updated docstrings; checkpoint before style fixes * style and quality checks, fixed initialization problem in float_tensor, ready for PR * added mega to toctree * removed unnecessary arg in megaconfig * removed unused arg and fixed code samples with leftover roberta models * Apply suggestions from code review Applied all suggestions except the one renaming a class, as I'll need to update that througout Co-authored-by:
Arthur <48595927+ArthurZucker@users.noreply.github.com> * fixed issue where .view breaks batch dimension, conversion script fixed with absolute imports, updated readme with Mega->MEGA * removed asserts in Mega code, renamed sequencenorm, gatedcrossattention, and NFFN, replaced get_activation_fn with ACTFN, and added sequencenorm to layer norms * reformatted .forward() docstrings to match style and removed unused mask input in cross-attention * removed all reset_parameters() methods and rolled into MegaPreTrainedModel._init_weights() * renamed all single-letter variables and improved readability in tensor size comments, Mega->MEGA in 2 documentation files * variable names in NFFN * manual Mega->MEGA changes in docs * Mega->MEGA in config auto * style and quality fixes * Apply suggestions from code review Co-authored-by:
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Joao Gante authored
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Ashwin Mathur authored
Fix typo in greedy search docs
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James Reed authored
* [HFTracer] Make embeddings ops take on the dtype of the weight * fix bug
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- 23 Mar, 2023 13 commits
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Yih-Dar authored
* Automatically create or update tiny models * Skip failed tests * update workflow file * use revision --------- Co-authored-by: -
кѳѳsнī authored
* Llama - Move target tokens to final pipeline device if needed * Update src/transformers/models/llama/modeling_llama.py Co-authored-by:
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Joao Gante authored
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jeffhataws authored
This PR fixes the "RuntimeError: No CUDA GPUs are available" when running with --bf16 option on Neuron. Related PRs: https://github.com/huggingface/transformers/pull/20684 https://github.com/huggingface/transformers/pull/22300
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Batese2001 authored
* Added type hints to TFDeiTModel * make style --------- Co-authored-by:Matt <rocketknight1@gmail.com>
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Samuel Larkin authored
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Sylvain Gugger authored
* Fix various imports * Fix copies * Fix import
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Quentin Lhoest authored
* Mention why one needs to specify max_steps in Trainer * dummy change to trigger CI
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mollerup23 authored
* Fixed gradient checkpoint bug for this model * Updating PR indentation (maintainer feedback) * make fixup --------- Co-authored-by:younesbelkada <younesbelkada@gmail.com>
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Younes Belkada authored
add `accelerate` support for MBart
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Stas Bekman authored
* [gptj] support older pytorch version * contributor * contributor * make copies --------- Co-authored-by:
Michael Wyatt <michaelwyatt@microsoft.com> Co-authored-by:
Nick Hill <nickhill@us.ibm.com>
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Sylvain Gugger authored
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Sylvain authored
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- 22 Mar, 2023 4 commits
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Stas Bekman authored
* [deepspeed zero3] need generate(synced_gpus=True, ...) * fix * rework per Sylvain's suggestion * Apply suggestions from code review Co-authored-by:
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Yih-Dar authored
* check what tests fail * Skip failing tests * Skip failing tests * Skip failing tests * Skip failing tests * clean up * clean up --------- Co-authored-by: -
Luc CAILLIAU authored
* Chunkable classification pipeline The TokenClassificationPipeline is now able to process sequences longer than 512. No matter the framework, the model, the tokenizer. We just have to pass process_all=True and a stride number (optional). The behavior remains the same if you don't pass these optional parameters. For overlapping parts when using stride above 0, we consider only the max scores for each overlapped token in all chunks where the token is. * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * update with latest black format * update black format * Update token_classification.py * Update token_classification.py * format correction * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update comments * Update src/transformers/pipelines/token_classification.py Co-authored-by:
Nicolas Patry <patry.nicolas@protonmail.com> * Update token_classification.py Correct spaces, remove process_all and keep only stride. If stride is provided, the pipeline is applied to the whole text. * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update chunk aggregation Update the chunk aggregation strategy based on entities aggregation. * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py Remove unnecessary pop from outputs dict * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update token_classification.py * Update src/transformers/pipelines/token_classification.py Co-authored-by:
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Tom Aarsen authored
Resolve incorrect type typo in trainer methods
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