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- 19 Apr, 2024 14 commits
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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:
amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Remove FIXME comment * Remove from_pt * 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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hoshi-hiyouga authored
* Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py
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Raushan Turganbay authored
* remove seq length from generation tests * style and quality * [test_all] & PR suggestion Co-authored-by:
Joao Gante <joaofranciscocardosogante@gmail.com> * Update tests/generation/test_utils.py Co-authored-by:
Arthur <48595927+ArthurZucker@users.noreply.github.com> * [test all] remove unused variables --------- Co-authored-by:
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Marc Sun authored
* Use unwrap with the one in accelerate * oups * update unwrap * fix * wording * raise error instead * comment * doc * Update src/transformers/modeling_utils.py Co-authored-by:
Zach Mueller <muellerzr@gmail.com> * style * put else --------- Co-authored-by:
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Yoach Lacombe authored
* fix Parameter dtype in audio models * restore casting of masked_spec_embed * restore casting of masked_spec_embed
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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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Lysandre Debut authored
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Yih-Dar authored
limit Co-authored-by:ydshieh <ydshieh@users.noreply.github.com>
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Sanchit Gandhi authored
* fix tests * style * more fixes * move model to device * move logits to cpu * update expected values * use ungated dataset * fix * fix * update --------- Co-authored-by: -
Raushan Turganbay authored
fix again
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Sanchit Gandhi authored
fixes
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Bharat Ramanathan authored
* feat: upgrade wandb callback with new features * fix: ci issues with imports and run fixup
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Jacky Lee authored
* feat: multidevice for resnet * feat: yes! resnet * fix: compare all elements in tuple * feat: support for regnet * feat: support for convnextv2 * feat: support for bit * feat: support for cvt * feat: add support for focalnet * feat: support for yolos * feat: support for glpn * feat: support for imagegpt * feat: support for levit * feat: support for mgp_str * feat: support for mobilnet_v1 * feat: support for mobilnet_v2 * feat: support for mobilevit * feat: support for mobilevitv2 * feat: support for poolformer * fix: copies * fix: code quality check * update: upstream changes from main * fix: consistency check * feat: support for sam * feat: support for switchformer * feat: support for swin * feat: support for swinv2 * feat: support for timesformer * feat: suport for trocr * feat: support for upernet * fix: check copies * update: rerun CI * update: rerun again, maybe * update: one more rerun --------- Co-authored-by:Jacky Lee <jackylee328@gmail.com>
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NielsRogge authored
* Add special tokens * Add special tokens * Use fmt * Uncomment code * Add test * Remove scripts * Address comments * Improve tests * Address comment * Remove flag
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- 18 Apr, 2024 19 commits
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Yih-Dar authored
* fix * fix * fix * update comments --------- Co-authored-by: -
Yih-Dar authored
fix Co-authored-by: -
Zach Mueller authored
* Alias * Note alias * Tests and src * Rest * Clean * Change typing? * Fix tests * Deprecation versions
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Albert Villanova del Moral authored
* Fix test with exif_transpose image * Replace datasets with PIL to load image in tests
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Zizhao Chen authored
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Yoach Lacombe authored
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Raushan Turganbay authored
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Yih-Dar authored
fix Co-authored-by: -
Lysandre authored
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Younes Belkada authored
FIX: Fixes unexpected behaviour for Llava / LLama & AWQ Fused modules + revert #30070 at the same time (#30317) * Update awq.py * style * revert felix PR * fix * add felix comments
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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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fxmarty authored
* overlooked * style * cleaner
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Marc Sun authored
* Switch to non persistant buffer * fix device mismatch issue due to cache * style
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fxmarty authored
atol for sliding window test
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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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Yih-Dar authored
* fix * fix --------- Co-authored-by: -
Pavel Iakubovskii authored
* Fix multiline processing * Update test for token2json
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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:
Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * Update docs/source/en/model_doc/nllb.md Co-authored-by:
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- 17 Apr, 2024 7 commits
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fxmarty authored
fix olmo
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fxmarty authored
* tentatively re-enable FA2 + SDPA * better comment * _ignore_causal_mask_sdpa as staticmethod * type hints * use past_seen_tokens instead * enable copied from for sdpa * ruff * llama simplifications on review * remove unnecessary self.is_causal check * fix copies * cleaning * precise message * better doc * add test * simplify * Update src/transformers/models/llama/modeling_llama.py Co-authored-by:
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Shane A authored
* Add OLMo using add-new-model-like with Llama * Fix incorrect tokenizer for OLMo * Copy-paste relevant OLMo methods and their imports * Add OLMo config * Modify OLMo config to follow HF conventions * Remove unneeded Llama code from OLMo model * Add ability for OLMo model to output attentions * Add OLMoPreTrainedModel and OLMoModel * Add OLMoForCausalLM * Minor fixes to OLMo model for style and missing functions * Implement OLMo tokenizer * Implement OLMo to HF conversion script * Add tests for OLMo model * Add tests for OLMo fast tokenizer * Add auto-generated dummy objects * Remove unimplemented OLMo classes from auto and init classes and re-format * Add README and associated auto-generated files * Use OLMo names for common properties * Run make fixup * Remove `|` from OLMo typing * Remove unneeded tokenization_olmo.py * Revert model, config and converter to add-new-model-like Llama * Move logic for adding bos/eos token into GPTNeoxTokenizerFast * Change OLMoConfig defaults to match OLMo-7B * Use GPTNeoXToknizerFast in OLMo tokenizer tests * Modify auto-generated OLMoModelTests to work for OLMo * Add non-parametric layer norm OLMoLayerNorm * Update weight conversion script for OLMo * Fix __init__ and auto structure for OLMo * Fix errors from make fixup * Remove OLMoTokenizerFast from documentation * Add missing 'Copied from' for OLMoModel._update_causal_mask * Run make fix-copies * Rearrange string replacements in OLMoForCausalLM Copied from * Move OLMo and Llama CausalLM.forward example into global constants * Fix OLMO_GENERATION_EXAMPLE doc string typo * Add option for qkv clipping to OLMo * Rearrange OLMoConfig kwargs in convert_olmo_weights_to_hf * Add clip_qkv to OLMoConfig in convert_olmo_weights_to_hf * Fix OLMo tokenization bug using conversion script * Keep model in full precision after conversion * Do not add eos token automatically * Update references to OLMo model in HF Hub * Do not add eos token during encoding by default * Fix Llama generation example * Run make fixup * OLMo 7B integration test fix * Remove unneeded special case for OLMoConfig * OLMo 7B Twin 2T integration test fix * Fix test_model_7b_greedy_generation * Remove test_compile_static_cache * Fix OLMo and Llama generation example * Run make fixup * Revert "OLMo 7B integration test fix" This reverts commit 4df56a4b150681bfa559846f40e9b7b7f97d7908. * Revert "OLMo 7B Twin 2T integration test fix" This reverts commit 9ff65a4a294ace89ab047b793ca55e623a9ceefc. * Ungate 7B integration tests and fix greedy generation test * Add retries for flaky test_eager_matches_sdpa_generate * Fix output of doc example for OLMoForCausalLM.forward * Downsize OLMo doc test for OLMoForCausalLM.forward to 1B model * Try fix incorrect characters in OLMoForCausalLM.forward doct test * Try fix incorrect characters in OLMoForCausalLM.forward doc test using end quotes * Remove pretraining_tp from OLMo config and model * Add missing 'Copied from' instances * Remove unneeded causal_mask from OLMoModel * Revert Llama changes * Ignore copy for OLMoForCausalLM.forward * Change 'OLMo' to 'Olmo' in classes * Move minimal OLMo tokenization tests to model tests * Add missed 'Copied from' for repeat_kv
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Nicolas Patry authored
* [DO NOT MERGE] Testing tokenizers 0.19.0rc0 * Accounting for the breaking change. * Ruff. * Upgrading to tokenizers `0.19` (new release with preprend_scheme fixed and new surface for BPE tiktoken bug).
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Pavel Iakubovskii authored
* Add evaluation loop container for interm. results * Add tests for EvalLoopContainer * Formatting * Fix padding_index in test and typo * Move EvalLoopContainer to pr_utils to avoid additional imports * Fix `eval_do_concat_batches` arg description * Fix EvalLoopContainer import
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st81 authored
* Add create token type ids to CodeGenTokenizer * Fix inconsistent length of token type ids * Format source codes * Fix inconsistent order of methods * Update docstring * add test_tokenizer_integration test * Format source codes * Add `copied from` comment to CodeGenTokenizerFast * Add doc of create_token_type_ids_from_sequences * Make return_token_type_ids False by default * Make test_tokenizer_integration as slow test * Add return_token_type_ids to tokenizer init arg * Add test for tokenizer's init return_token_type_ids * Format source codes
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Younes Belkada authored
Update push-important-models.yml
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