"tests/funnel/test_modeling_funnel.py" did not exist on "3e3e41ae20be8e0fd9dd077b61be724f1098d407"
- 21 Jun, 2024 1 commit
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Sangbum Daniel Choi authored
* fill out docs string in configuration https://github.com/huggingface/transformers/pull/29077/files/75dcd3a0e82cca36f12178b65bbd071ab7b25088#r1506391856 * reduce the input image size for the tests * remove the unappropriate tests * only 5 failes exists * make style * fill up missed architecture for object detection in docs * fix auto modeling * simple fix in missing import * major change including backbone refactor and objectdetectionoutput refactor * minor fix only 4 fails left * intermediate fix * revert __init__.py * revert __init__.py * make style * fixes in pr_docs * intermediate fix * make style * two fixes * pass doctest * only one fix left * intermediate commit * all fixed * Update src/transformers/models/rt_detr/image_processing_rt_detr.py Co-authored-by:
amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/rt_detr/convert_rt_detr_original_pytorch_checkpoint_to_pytorch.py Co-authored-by:
Sounak Dey <dey.sounak@gmail.com> * change last_hidden_state to first index * all pass fix TO DO: minor update in comments * make fix-copies * remove deepcopy * pr_document fix * revert deepcopy due to the issue of unexpceted behavior in decoderlayer * add atol in final * add no_split_module * _no_split_modules = None * device transfer for model parallelism * minor fix * make fix-copies * fix typo * add test_image_processor with post_processing * Update src/transformers/models/rt_detr/configuration_rt_detr.py Co-authored-by:
NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/rt_detr/configuration_rt_detr.py Co-authored-by:
Jason Wu <jasonkit@users.noreply.github.com> * enable all the rtdetr model :) * finish except CI * add RTDetrResNetBackbone * make fix-copies * fix TO DO: CI enable * make style * rename test * add docs * add special fix * revert resnet * Update src/transformers/models/rt_detr/modeling_rt_detr_resnet.py Co-authored-by:
ChoiSangBum <choisangbum@ChoiSangBumui-MacBookPro.local>
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- 23 May, 2024 1 commit
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Aritra Roy Gosthipaty authored
* chore: initial commit * chore: adding imports and inits * chore: adding the causal and classification code * chore: adding names to the layers * chore: using single self attn layer * chore: built the model and layers * chore: start with testing * chore: docstring change, transpose fix * fix: rotary embedding * chore: adding cache implementation * remove unused torch * chore: fixing the indexing issue * make fix-copies * Use modeling_tf_utils.keras * make fixup * chore: fixing tests * chore: adding past key value logic * chore: adding multi label classfication test * fix: switching on the built parameters in the layers * fixing repo consistency * ruff formats * style changes * fix: tf and pt equivalence * removing returns from docstrings * fix docstrings * fix docstrings * removing todos * fix copies * fix docstring * fix docstring * chore: using easier rotate_half * adding integration tests * chore: addressing review related to rotary embedding layer * review changes * [run-slow] mistral * skip: test save load after resize token embedding * style --------- Co-authored-by:Matt <rocketknight1@gmail.com>
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- 15 May, 2024 1 commit
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Raushan Turganbay authored
* add model draft * update docstring * add tests * support image and video as input * update for better handling of mixed input and clean-up a bit * bug when mixed inputs & add tests * Update README.md Co-authored-by:
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- 14 May, 2024 2 commits
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Pablo Montalvo authored
* add new model like * add state dict slicing + new model config * update palma config and weights, passes vision activations * fix * update * reorder loading/unpacking * clean up * add debug statements * change device * fix * debugging * fix noncausal mask * fixup sdpa + causal mask * fix activation function * remove debug before changing modeling file * add variants * debug attention mask in generate * revert to non-debug sdpa * revert gemma modifications * add custom language modeling * use Processor * add language modeling file to init * try thin wrapper around generate * Update * update mask * breakpoints galore * remove conflict * switch to left-padding * add incomplete model doc * add paligemma global files * batch rename paligemma * make generation match outputs and captioning * style * style * remove copied from + doc * remove more copied from * remove copy from projector * minor fix * update config and style * add readme - dummy * CORRECT image captioning * moving to args * add siglip proper + fix merging image + text features * take update_causal_mask from upstream * remove breakpoint * leverage AutoModel * fix input_ids slicing * make siglip head conditional * remove encoder_decoder value * remove unneeded modeling file * add commented 4d attention mask * FIXED generation with 4D mask * Update src/transformers/models/siglip/modeling_siglip.py Co-authored-by:
Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix left padding detection * shuffle order of verifications * fix missing labels for training * fix * vectorize merging of features, improve slicing * improve testing before conversion * handle merging in processor * image token index depends on checkpoint * add variants, save processor too * save processors, base tokenizer off spm file * expand model embeddings due to additional image token * pass image processing args * add convert rgb to siglip processor * add \n token separately * fix tokenizer and prompts * fix docstrings * change to camel * fix casing * debug pos_ids and sdpa * pass and use cache_position * add flag for newline tokenization * Update src/transformers/models/paligemma/processing_paligemma.py Co-authored-by:
Merve Noyan <merveenoyan@gmail.com> * simplify conversion script * add copied from * add precision to conversion script * Update src/transformers/models/paligemma/modeling_paligemma.py Co-authored-by:
Pedro Cuenca <pedro@huggingface.co> * clean up * Shift attention mask from `1:` After discussion with @molbap * add docs, fix quality * quality, tied weights inheritance, and logits/label alignment * fix more tests * pass attn_implementation to language model correctly * add SiglipVisionTransformer to no split modules * skip paligemma test for sdpa dispatch to flash * skip incompatible tests * quality * [broken archive maps] * Apply suggestions - remove archive lists - style - take shape of inputs_embeds for batch Co-authored-by:
ArthurZucker <arthur.zucker@gmail.com> Co-authored-by:
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Yikang Shen authored
* init jetmoe code * update archive maps * remove flax import * fix import error * update README * ruff fix * update readme * fix * update config * fix issue * merge files * fix model bug * fix test * auto fix * model size * add comments * fix form * add flash attention support * fix attention head number * fix init * fix support list * sort auto mapping * fix test * fix docs * update test * fix test * fix test * change variable name * fix config * fix init * update format * clean code * fix config * fix config * change default config * update config * fix issues * update formate * update config argument * update format * Update src/transformers/models/jetmoe/modeling_jetmoe.py Co-authored-by:
Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * Update src/transformers/models/jetmoe/modeling_jetmoe.py Co-authored-by:
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- 13 May, 2024 1 commit
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Alazar authored
* Initial commit * Just a copy of modeling_idefics.py that will be ported to TF * - Prepend TF to the name of all classes - Convert pytorch ops to TF (not all operations are converted yet) * Add TF imports * Add autotranslated files * Add TF classes to model_tf_auto.py * Add the TF classes in model_doc * include auto-translated code * Adopted from auto-translated version * Add a forgotten super().build * Add test code for TF version. * Fix indentation and load pytorch weights for now * Some fixes. Many tests are still failing but some are passing now. - I have added TODO's for some of the hacks I made to unblock me and I will address them soon - I have the processing_idefics.py hacked in my view to support TF temporarily * Add ALL_LAYERNORM_LAYERS to match pytorch * Revert "Add ALL_LAYERNORM_LAYERS to match pytorch" This reverts commit 7e0a35119b4d7a6284d04d8c543fba1b29e573c9 as it is not needed in the tf implementation. * Fix freeze_relevant_params() * Some more fixes * Fix test_attention_outputs * Add tf stuff to processing_idefics.py processing_idefics.py supports both pytorch and tf now. test_processor_idefics.py for pytorch is passing, so i didn't break anything but still some issues with tf. I also need to add tf tests in test_processor_idefics.py. * Pass return_tensors to image processing code and fix test * Pass return_tensors to the image processor __init__ * Fix several test cases - Make input to some of the forward pass of type `TFModelInputType` - Decorate main layer forward pass with `@unpack_inputs` - Decorate main layer with `@keras_serializable` - Pass `inputs` to TFIdeficsModel * Some more fixes forgotten in last commit * Fix processing code and vision_tf.py * Fix perceiver bug * Import from * Auto-add build() methods + style pass * Fix build() errors due to `None` being passed as shape to some layers * Change name in TFIdeficsForVisionText2Text to attribute in IdeficsForVisionText2Text * Fix pytorch weights load for tf2 There were a lot of `name=` missing in weight initialization code. * Attempt to fix CI * Add back accidently removed line * Remove torch-specific stuff from the TF test file * make fix-copies, make style, remove autotranslated files * Fixes to imports/docstrings * Let's try the from future import in desperation * Fix the core random_attention_mask fn to match the torch/flax behaviour * Clean random_attention_mask up correctly * Remove torch-only test * Fix loss shape, couple of nits * make style * Don't test for OOB embeddings because IDEFICS uses those deliberately * Fix loss computation to handle masking * Fix test failures when flattening * Fix some test failures - Add cross attention gate which was missing and wasn't being passed arround - Fix overwriting of image_attention_mask due to hack I had for dummy inputs * Add a proper stateless scaled_dot_product_attention * make style * Adding missing attribute from the PyTorch version * Small cleanups to decoupledlinearlayer in case that helps * Pass epsilon to LayerNormalization * Attemp to fix pytorch weight cross-loading for TFIdeficsEmbedding * Fix a bug in TFIdeficsGatedCrossAttentionLayer * Patching up build() methods * Constant self.inv_freq * Constant self.inv_freq * First working version The TF implementation works now, there was a bug in the TFIdeficsDecoupledLinear where the weights were mis-intialized (in_features,out_features) when it should be: (out_features, in_features) I have tested this so far with tiny-random and idefics-9b-instruct and gives correct output. I also dumped the final outputs for both pytorch and TF and they are identical. * Fix some test failures * remove print statement * Fix return_tensors * Fix CI test failure check_code_quality * Attempt to fix CI failures by running `make fixup` The hardcoded IDs in test_modeling_tf_idefics.py are for the integration test and makes that file unreadable and should probably be moved to a seperate file. * Attempt to fix tests_pr_documentation_tests * Fix a test failure in test_image_processing_idefics.py * Fix test test_pt_tf_model_equivalence * Fix a few failures * Tiny fix * Some minor fixes * Remove a duplicate test * Override a few test failures for IDEFICS - `test_keras_save_load` is passing now - `test_compile_tf_model` is still failing * Fix processing_idefics.py after rebase * Guard import keras with is_tf_available * fix check code quality * fix check code quality * Minor fixes * Skip test_save_load temporarily This test passed on my local box but fails on the CI, skipping for now to see if there are other remaining failures on the CI. * Run `ruff format tests src utils` * Fix last failing test, `test_compile_tf_model` * Add fixes for vision_tf.py I forgot to add this file in last commit. * Minor fixes * Replace "<<<" with "<<" for doc tests IDEFICS-9B is too big for doctest runner, so don't run it there * Make code more readable * Fix bug after code review I added a layer_norm_eps to IdeficsConfig but I don't even need it since the vision config has a layer_norm_eps. * Fix after code review Use original code tokenizer.convert_tokens_to_ids * Keep PyTorch as the default return_tensors * Fixes to modeling_tf after code review * Fixes from code review - Remove all references of `TF_IDEFICS_PRETRAINED_MODEL_ARCHIVE_LIST` - Pass 1e-5 to LayerNormalization in perceiver * Run ruff * Undo a change * Refactor processing code after Matt's suggestion * Remove TODO's that aren't needed anymore * For pytorch, Use original pytorch processing code from main Since this PR is a TF port it shouldn't make any modifications to pytorch IDEFICS code. This changes undo's the pytorch processing modifications I made and uses original code from main. * Update tests/models/idefics/test_modeling_idefics.py * Update tests/models/idefics/test_modeling_tf_idefics.py * Add missing imports for is_pt_tf_cross_test * [DO NOT MERGE]: This is a commit for debugging and will be reverted The cross test `test_pt_tf_model_equivalence` passes locally but fails when running on the CI. This commit is to help debug that and will be reverted. * Revert "[DO NOT MERGE]: This is a commit for debugging and will be reverted" This reverts commit 8f0d709ec5bd46685fb0b4259d914ffee794875b. * [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted * [DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted * Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted" This reverts commit 998cc38b8c3d313bf5e5eb55a7f5b7b881897b89. * Revert "[DO NOT MERGE]: This commit is for debugging a CI failure and will be reverted" This reverts commit 1c695ac4219c4ae4d39b330b01744dc27deb7dd4. * Don't skip test_save_load IIRC test_save_load was also failing on the CI but not on my local box, it might be easier to debug that on the CI first than the cross tests * Debugging commit, will be reverted * Revert "Debugging commit, will be reverted" This reverts commit 8eafc8e41e20c4e95a3a90834f06a6e9f445e2d5. * Override `test_save_load` and push model to save Maybe this will help me repro this weird bug * pass my repo_id * add endpoint * Pass a temp (write) token just for this CI * Undo last few commits, still pushing to hub for model debugging The issue seems to be with save_pretrained(), when I looked at the model saved from the CI test failure it is basically empty and has no weights. `self.save_weights(..)` seems to be failing in save_pretrained but needs more debugging * Add logging to modeling tf utils, will be reverted just for debugging * Debugging, will revert * Revert "Debugging, will revert" This reverts commit 9d0d3075fb7c82d8cde3a5c76bc8f3876c5c55d3. * Revert "Add logging to modeling tf utils, will be reverted just for debugging" This reverts commit 774b6b7b1c17b3ce5d7634ade768f2f686cee617. * Remove `test_save_load` The CI failures are gone after my latest rebase, no idea why but I was still saving the model to my hub on HF and the tf_model.h5 file now has everything. * Run make fix-copies * Run ruff format tests src utils * Debugging commit, will be reverted * Run ruff, also trigger CI run * Run ruff again * Undo debugging commit --------- Co-authored-by:
Matt <Rocketknight1@users.noreply.github.com>
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- 24 Apr, 2024 2 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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- 19 Apr, 2024 1 commit
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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:
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- 18 Apr, 2024 2 commits
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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:Joao Gante <joao@huggingface.co>
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- 17 Apr, 2024 1 commit
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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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- 15 Apr, 2024 1 commit
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amyeroberts authored
* Initial add model additions * Test * All weights loading * Can perform full forward pass * Local and remote the same * Matching local and remote * Fixup * Idefics2Model importable; fixup docstrings * Don't skip by default * Remove deprecated use_resampler arg * Remove self.config * DecoupledLinear takes config * Tidy up * Enable eager attention and tidy up * Most tests passing * Update for batch of processed images * Add image processor * Update doc pages * Update conversion script * Remove erroneous breakpoint * Remove accidendtal spelling change * Update to reflect changes on hub - make generate work * Fix up * Image processor tests * Update tests * Add a processor * Add a processor * Update convert script * Update modeling file - remove fixmes * Bug fix * Add processing test * Use processor * Fix up * Update src/transformers/models/idefics2/modeling_idefics2.py Co-authored-by:
Victor SANH <victorsanh@gmail.com> * Update src/transformers/models/idefics2/modeling_idefics2.py Co-authored-by:
Pablo Montalvo <39954772+molbap@users.noreply.github.com> Co-authored-by:
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- 11 Apr, 2024 1 commit
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Eduardo Pacheco authored
* Fixed typo when converting weigths to GroundingDINO vision backbone * Final modifications on modeling * Removed unnecessary class * Fixed convert structure * Added image processing * make fixup partially completed * Now text_backbone_config has its own class * Modified convert script * Removed unnecessary config attribute * Added new function to generate sub sentence mask * Renamed parameters with gamma in the name as it's currently not allowed * Removed tokenization and image_processing scripts since we'll map from existing models * Fixed some issues with configuration * Just some modifications on conversion script * Other modifications * Copied deformable detr * First commit * Added bert to model * Bert validated * Created Text and Fusion layers for Encoder * Adapted Encoder layer * Fixed typos * Adjusted Encoder * Converted encoder to hf * Modified Decoder Layer * Modified main decoder class * Removed copy comments * Fixed forward from GroundingDINOModel and GroundingDINODecoder * Added all necessary layers, configurations and forward logic up to GroundingDINOModel * Added all layers to convertion * Fixed outputs for GroundingDINOModel and GroundingDINOForObjectDetection * Fixed mask input to encoders and fixed nn.MultiheadAttention batch first and attn output * Fixed forward from GroundingDINOTextEnhancerLayer * Fixed output bug with GroundingDINODeformableLayer * Fixed bugs that prevent GroundingDINOForObjectDetection to run forward method * Fixed attentions to be passed correctly * Passing temperature arg when creating Sine position embedding * Removed copy comments * Added temperature argument for position embedding * Fixed typo when converting weigths to GroundingDINO vision backbone * Final modifications on modeling * Removed unnecessary class * Fixed convert structure * Added image processing * make fixup partially completed * Now text_backbone_config has its own class * Modified convert script * Removed unnecessary config attribute * Added new function to generate sub sentence mask * Renamed parameters with gamma in the name as it's currently not allowed * Removed tokenization and image_processing scripts since we'll map from existing models * Fixed some issues with configuration * Just some modifications on conversion script * Other modifications * Fix style * Improve fixup * Improve conversion script * Improve conversion script * Add GroundingDINOProcessor * More improvements * Return token type ids * something * Fix more tests * More improvements * More cleanup * More improvements * Fixed tests, improved modeling and config * More improvements and fixing tests * Improved tests and modeling * Improved tests and added image processor * Improved tests inference * More improvements * More test improvements * Fixed last test * Improved docstrings and comments * Fix style * Update src/transformers/models/grounding_dino/modeling_grounding_dino.py Co-authored-by:
Rafael Padilla <31217453+rafaelpadilla@users.noreply.github.com> * Update src/transformers/models/grounding_dino/modeling_grounding_dino.py Co-authored-by:
Sangbum Daniel Choi <34004152+SangbumChoi@users.noreply.github.com> * Update src/transformers/models/grounding_dino/modeling_grounding_dino.py Co-authored-by:
Tianqi Xu <40522713+dandansamax@users.noreply.github.com> * Update docs/source/en/model_doc/grounding-dino.md Co-authored-by:
Niels <niels.rogge1@gmail.com> Co-authored-by:
Eduardo Pacheco <eduardo.pacheco@limehome.com> Co-authored-by:
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- 10 Apr, 2024 1 commit
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Arthur authored
* Fork. * RecurrentGemma initial commit. * Updating __init__.py. * Minor modification to how we initialize the cache. Changing how the config specifies the architecture. * Reformat code to 4 spaces. Fixed a few typos. * Fixed the forward pass. Still unclear on the cache? * Fixed the RecurrentGemmaForCausalLM * Minor comment that we might not need attention_mask and output_attention arguments. * Now cache should work as well. * Adding a temporary example to check whether the model generation works. * Adding the tests and updating imports. * Adding the example file missing in the previous commit. * First working example. * Removing .gitignore and reverting parts of __init__. * Re-add .gitignore. * Addressing comments for configuration. * Move mask creation to `_prepare_inputs_for_generation`. * First try at integration tests: 1. AttributeError: 'GriffinCausalLMOutput' object has no attribute 'attentions'. 2. `cache_position` not passed * Transfoering between machines. * Running normal tests. * Minor fix. * More fixes. * Addressing more comments. * Minor fixes. * first stab at cleanup * more refactoring * fix copies and else * renaming and get init to work * fix causal mask creation * update * nit * fix a hell lot of things * updates * update conversion script * make all keys importable * nits * add auto mappings * properly convert ffw_up and down * add scaling * fix generations * for recurrent dtype * update * fix going beyong window * fixup * add missing files * current updates to remove last einops * finish modeling refactor * TADA * fix compile * fix most failing testt ? ? * update tests * refactor and update * update * nits, fixup and update tests * more fixup * nits * fix imports * test format * fixups * nits * tuple typing * fix code quality * add model card * fix doc * skip most generation tests * nits * style * doc fixes * fix pr and check_copies? * last nit * oupsy * Apply suggestions from code review Co-authored-by:
Lysandre Debut <hi@lysand.re> * update * Update src/transformers/models/recurrent_gemma/convert_recurrent_gemma_to_hf.py Co-authored-by:
Aleksandar Botev <botev@google.com> Co-authored-by:
Leonard Berrada <lberrada@users.noreply.github.com> Co-authored-by:
anushanf <anushanf@google.com> Co-authored-by:
botev <botevmg@gmail.com> Co-authored-by:
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- 27 Mar, 2024 1 commit
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Bo Zheng authored
* add support for qwen2 MoE models * update docs * add support for qwen2 MoE models * update docs * update model name & test * update readme * update class names & readme & model_doc of Qwen2MoE. * update architecture name * fix qwen2_moe tests * use Qwen2Tokenizer instead of Qwen2MoeTokenizer * update modeling_qwen2_moe.py * fix model architecture * fix qwen2_moe tests * use Qwen2Tokenizer instead of Qwen2MoeTokenizer * update modeling_qwen2_moe.py * fix model architecture * fix style * fix test when there are sparse and non sparse layers * fixup * Update README.md Co-authored-by:
bozheng-hit <dsoul0621@gmail.com> Co-authored-by:
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- 20 Mar, 2024 1 commit
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NielsRogge authored
* First draft * Fix tests, add docs * Improve docstrings * Fix test * Address comments * Address comments * Remove vocab_size attribute * Remove batch_size * Address comment * Add image processor tests * Support fx * Update docstring * Add support for 34b * Convert 34b model * Add integration tests * Update checkpoints * Convert vicuna-13b, remove doc tests * Remove script * Remove file * Address comments * Improve docstrings * Deprecate vocab_size * Remove aspect_ratio_setting * Address comments * Update READMEs * Add tips about chat templates * Fix tests * Deprecate vocab_size safely * Update tests --------- Co-authored-by:
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- 19 Mar, 2024 1 commit
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StevenBucaille authored
* Added SuperPoint docs * Added tests * Removed commented part * Commit to create and fix add_superpoint branch with a new branch * Fixed dummy_pt_objects * Committed missing files * Fixed README.md * Apply suggestions from code review Fixed small changes Co-authored-by:
steven <steven.bucaillle@gmail.com> Co-authored-by:
Steven Bucaille <steven.bucaille@buawei.com>
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- 18 Mar, 2024 1 commit
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Yoach Lacombe authored
* first modeling code * make repository * still WIP * update model * add tests * add latest change * clean docstrings and copied from * update docstrings md and readme * correct chroma function * correct copied from and remove unreleated test * add doc to toctree * correct imports * add convert script to notdoctested * Add suggestion from Sanchit Co-authored-by:
Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * correct get_uncoditional_inputs docstrings * modify README according to SANCHIT feedback * add chroma to audio utils * clean librosa and torchaudio hard dependencies * fix FE * refactor audio decoder -> audio encoder for consistency with previous musicgen * refactor conditional -> encoder * modify sampling rate logics * modify license at the beginning * refactor all_self_attns->all_attentions * remove ignore copy from causallm generate * add copied from for from_sub_models * fix make copies * add warning if audio is truncated * add copied from where relevant * remove artefact * fix convert script * fix torchaudio and FE * modify chroma method according to feedback-> better naming * refactor input_values->input_features * refactor input_values->input_features and fix import fe * add input_features to docstrigs * correct inputs_embeds logics * remove dtype conversion * refactor _prepare_conditional_hidden_states_kwargs_for_generation ->_prepare_encoder_hidden_states_kwargs_for_generation * change warning for chroma length * Update src/transformers/models/musicgen_melody/convert_musicgen_melody_transformers.py Co-authored-by:
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- 15 Mar, 2024 1 commit
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Saurabh Dash authored
* Cohere Model Release (#1) Cohere Model Release * Remove unnecessary files and code (#2) Some cleanup * Delete cohere-model directory (#3) * Make Fix (#5) * Pr fixes (#6) * fixes for pr * pr fixes for the format * pr fixes for the format * src/transformers/models/auto/tokenization_auto.py * Tokenizer test (#8) * tokenizer test * format fix * Adding Docs and other minor changes (#7) * Add modeling tests (#9) * Smol Fix (#11) * tokenization tests are fixed * format fixes * fix pr doc tests * fix pr doc tests * fix pr doc tests * fix pr style check * small changes in cohere.md * FIX: Address final comments for transformers integration (#13) * fix modeling final nits and add proper test file * for now leave empty tests * add integration test * push new test * fix modeling cohere (#14) * Update chat templates to use the new API (#15) --------- Co-authored-by:
ahmetustun <ahmetustun89@gmail.com> Co-authored-by:
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- 13 Mar, 2024 1 commit
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Nate Cibik authored
* Added pytests for pvt-v2, all passed * Added pvt_v2 to docs/source/end/model_doc * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * PvT-v2 now works in AutoModel * Reverted batch eval changes for PR * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat. Added additional type support for image size in config * Fixed config backbone compat * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Allowed for batching of eval metrics * copied models/pvt to adapt to pvt_v2 * First commit of pvt_v2 * Set key and value layers to use separate linear modules. Fixed pruning function * Set AvgPool to 7 * Fixed issue in init * PvT-v2 now works in AutoModel * Successful conversion of pretrained weights for PVT-v2 * Successful conversion of pretrained weights for PVT-v2 models * Added pytests for pvt-v2, all passed * Ran fix-copies and fixup. All checks passed * Added additional ReLU for linear attention mode * pvt_v2_b2_linear converted and working * Reverted batch eval changes for PR * Updated index.md * Expanded type support for Pvt-v2 config * Fixed config docstring. Added channels property * Fixed model names in tests * Fixed config backbone compat * Ran fix-copies * Fixed PvtV2Backbone tests * Added TFRegNet to OBJECTS_TO_IGNORE in check_docstrings.py * Fixed backbone stuff and fixed tests: all passing * Ran make fixup * Made modifications for code checks * Remove ONNX config from configuration_pvt_v2.py Co-authored-by:
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- 05 Mar, 2024 1 commit
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Arthur authored
* initial-commit * start cleaning * small nits * small nits * current updates * add kernels * small refactoring little step * add comments * styling * nit * nits * Style * Small changes * Push dummy mambda simple slow * nit * Use original names * Use original names and remove norm * Updates for inference params * Style nd updates * nits * Match logits * Add a test * Add expected generated text * nits doc, imports and styling * style * oups * dont install kernels, invite users to install the required kernels * let use use the original packages * styling * nits * fix some copieds * update doc * fix-copies * styling done * nits * fix import check * run but wrong cuda ress * mamba CUDA works :) * fix the fast path * config naming nits * conversion script is not required at this stage * finish fixing the fast path: generation make sense now! * nit * Let's start working on the CIs * style * better style * more nits * test nit * quick fix for now * nits * nit * nit * nit * nits * update test rest * fixup * update test * nit * some fixes * nits * update test values * fix styling * nit * support peft * integrations tests require torchg * also add slow markers * styling * chose forward wisely * nits * update tests * fix gradient checkpointing * fixup * nit * fix doc * check copies * fix the docstring * fix some more tests * style * fix beam search * add init schene * update * nit * fix * fixup the doc * fix the doc * fixup * tentative update but slow is no longer good * nit * should we always use float32? * nits * revert wrong changes * res in float32 * cleanup * skip fmt for now * update generation values * update test values running original model * fixup * update tests + rename inference_params to cache_params + make sure training does not use cache_params * small nits * more nits * fix final CIs * style * nit doc * I hope final doc nits * nit * 馃珷 * final touch! * fix torch import * Apply suggestions from code review Co-authored-by:
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- 04 Mar, 2024 1 commit
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NielsRogge authored
* First draft * More improvements * More improvements * More fixes * Fix copies * More improvements * More fixes * More improvements * Convert checkpoint * More improvements, set up tests * Fix more tests * Add UdopModel * More improvements * Fix equivalence test * More fixes * Redesign model * Extend conversion script * Use real inputs for conversion script * Add image processor * Improve conversion script * Add UdopTokenizer * Add fast tokenizer * Add converter * Update README's * Add processor * Add fully fledged tokenizer * Add fast tokenizer * Use processor in conversion script * Add tokenizer tests * Fix one more test * Fix more tests * Fix tokenizer tests * Enable fast tokenizer tests * Fix more tests * Fix additional_special_tokens of fast tokenizer * Fix tokenizer tests * Fix more tests * Fix equivalence test * Rename image to pixel_values * Rename seg_data to bbox * More renamings * Remove vis_special_token * More improvements * Add docs * Fix copied from * Update slow tokenizer * Update fast tokenizer design * Make text input optional * Add first draft of processor tests * Fix more processor tests * Fix decoder_start_token_id * Fix test_initialization * Add integration test * More improvements * Improve processor, add test * Add more copied from * Add more copied from * Add more copied from * Add more copied from * Remove print statement * Update README and auto mapping * Delete files * Delete another file * Remove code * Fix test * Fix docs * Remove asserts * Add doc tests * Include UDOP in exotic model tests * Add expected tesseract decodings * Add sentencepiece * Use same design as T5 * Add UdopEncoderModel * Add UdopEncoderModel to tests * More fixes * Fix fast tokenizer * Fix one more test * Remove parallelisable attribute * Fix copies * Remove legacy file * Copy from T5Tokenizer * Fix rebase * More fixes, copy from T5 * More fixes * Fix init * Use ArthurZ/udop for tests * Make all model tests pass * Remove UdopForConditionalGeneration from auto mapping * Fix more tests * fixups * more fixups * fix the tokenizers * remove un-necessary changes * nits * nits * replace truncate_sequences_boxes with truncate_sequences for fix-copies * nit current path * add a test for input ids * ids that we should get taken from c9f7a32f57440d90ff79890270d376a1cc0acb68 * nits converting * nits * apply ruff * nits * nits * style * fix slow order of addition * fix udop fast range as well * fixup * nits * Add docstrings * Fix gradient checkpointing * Update code examples * Skip tests * Update integration test * Address comment * Make fixup * Remove extra ids from tokenizer * Skip test * Apply suggestions from code review Co-authored-by:
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- 28 Feb, 2024 1 commit
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RaymondLi0 authored
* Copy model * changes * misc * fixes * add embed and residual dropout (#30) * misc * remove rms norm and gated MLP * remove copied mentions where its not a copy anymore * remove unused _shape * copied from mistral instead * fix copies * fix copies * add not doctested * fix * fix copyright * Update docs/source/en/model_doc/starcoder2.md Co-authored-by:
Joel Lamy-Poirier <joel.lamy-poirier@servicenow.com> Co-authored-by:
younesbelkada <younesbelkada@gmail.com> Co-authored-by:
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- 26 Feb, 2024 1 commit
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Eduardo Pacheco authored
* First commit * Improvements * More improvements * Converted original checkpoint to HF checkpoint * Fix style * Fixed forward * More improvements * More improvements * Update src/transformers/models/seggpt/modeling_seggpt.py Co-authored-by:
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- 21 Feb, 2024 1 commit
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Arthur authored
* inital commit * update * update conversion checkpoint * update conversion script * nits * some fixes * nits * merge * fix permute * nits * fix * nits * nits * nits * fix rope * fix both rope * nites * style * make sure flax works * fix flax init code * fix foward * nits * print flax generation out * current code * nits * SIIIIIIIIIIIIIIIIIII * update * add new tokenizer * correct fast tokenizer * fix conversion * more comments * fix modeling and conversion * nits and nits * nits testing * add some tokenization tests * add some edge cases * add slow tests and fix them * fixup * fix copies for modeling * fix copies * add 7B slow tests * fix * fix * fix tests * make tokenizer cis go green * styling * last tokenizer nits * update jax tests * fix flax for 7b * add jit testing
馃 * cleanups * isolated nit, inv_freq for rotary_emb.inv_freq * propagate to jax * Apply suggestions from code review Co-authored-by:馃殌 * update all readmes --------- Co-authored-by:sanchit-gandhi <sanchit@huggingface.co> Co-authored-by:
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- 14 Feb, 2024 1 commit
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Jonathan Tow authored
* Add `StableLM` * fix(model): re-create from `huggingface-cli add-new-model-like persimmon` * fix: re-add changes to address comments * fix(readme): add links to paper * fix(tokenization_auto): remove `GPTNeoXTokenizerFastFast` ref * fix(tests): re-add `@slow` decorator to integration tests * fix(tests): import slow... * fix(readme_hd): remove whitespace edit * fix(tokenizer): auto tokenizer tuple * skip doctests for `modeling_stablelm`
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- 08 Feb, 2024 1 commit
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Klaus Hipp authored
* Fix model documentation links in attention.md * Fix external link syntax * Fix target anchor names of section links * Fix copyright statement comments * Fix documentation headings
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- 31 Jan, 2024 1 commit
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Kian Sierra McGettigan authored
* direct copy from llama work * mistral modules forward pass working * flax mistral forward pass with sliding window * added tests * added layer collection approach * Revert "added layer collection approach" This reverts commit 0e2905bf2236ec323163fc1a9f0c016b21aa8b8f. * Revert "Revert "added layer collection approach"" This reverts commit fb17b6187ac5d16da7c461e1130514dc3d137a43. * fixed attention outputs * added mistral to init and auto * fixed import name * fixed layernorm weight dtype * freeze initialized weights * make sure conversion consideres bfloat16 * added backend * added docstrings * added cache * fixed sliding window causal mask * passes cache tests * passed all tests * applied make style * removed commented out code * applied fix-copies ignored other model changes * applied make fix-copies * removed unused functions * passed generation integration test * slow tests pass * fixed slow tests * changed default dtype from jax.numpy.float32 to float32 for docstring check * skip cache test for FlaxMistralForSequenceClassification since if pad_token_id in input_ids it doesn't score previous input_ids * updated checkpoint since from_pt not included * applied black style * removed unused args * Applied styling and fixup * changed checkpoint for doc back * fixed rf after adding it to hf hub * Add dummy ckpt * applied styling * added tokenizer to new ckpt * fixed slice format * fix init and slice * changed ref for placeholder TODO * added copies from Llama * applied styling * applied fix-copies * fixed docs * update weight dtype reconversion for sharded weights * removed Nullable input ids * Removed unnecessary output attentions in Module * added embedding weight initialziation * removed unused past_key_values * fixed deterministic * Fixed RMS Norm and added copied from * removed input_embeds * applied make style * removed nullable input ids from sequence classification model * added copied from GPTJ * added copied from Llama on FlaxMistralDecoderLayer * added copied from to FlaxMistralPreTrainedModel methods * fix test deprecation warning * freeze gpt neox random_params and fix copies * applied make style * fixed doc issue * skipped docstring test to allign # copied from * applied make style * removed FlaxMistralForSequenceClassification * removed unused padding_idx * removed more sequence classification * removed sequence classification * applied styling and consistency * added copied from in tests * removed sequence classification test logic * applied styling * applied make style * removed freeze and fixed copies * undo test change * changed repeat_kv to tile * fixed to key value groups * updated copyright year * split casual_mask * empty to rerun failed pt_flax_equivalence test FlaxWav2Vec2ModelTest * went back to 2023 for tests_pr_documentation_tests * went back to 2024 * changed tile to repeat * applied make style * empty for retry on Wav2Vec2
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- 25 Jan, 2024 1 commit
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NielsRogge authored
* First draft * More improvements * More improvements * More improvements * More improvements * Add docs * Remove file * Add copied from * Address comments * Address comments * Address comments * Fix style * Update docs * Convert all checkpoints, add integration test * Rename checkpoints * Add pretrained backbone attributes * Fix default config * Address comment * Add figure to docs * Fix bug thanks to @xenova * Update conversion script * Fix integration test
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- 18 Jan, 2024 1 commit
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Yoach Lacombe authored
* first commit * correct default value non causal * update config and modeling code * update converting checkpoint * clean modeling and fix tests * make style * add new config parameters to docstring * fix copied from statements * Apply suggestions from code review Co-authored-by:
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- 17 Jan, 2024 1 commit
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Junyang Lin authored
* add config, modeling, and tokenization * add auto and init * update readme * update readme * update team name * fixup * fixup * update config * update code style * update for fixup * update for fixup * update for fixup * update for testing * update for testing * fix bug for config and tokenization * fix bug for bos token * not doctest * debug tokenizer * not doctest * debug tokenization * debug init for tokenizer * fix style * update init * delete if in token auto * add tokenizer doc * add tokenizer in init * Update dummy_tokenizers_objects.py * update * update * debug * Update tokenization_qwen2.py * debug * Update convert_slow_tokenizer.py * add copies * add copied from and make style * update files map * update test * fix style * fix merge reading and update tests * fix tests * fix tests * fix style * debug a variable in readme * Update src/transformers/models/qwen2/configuration_qwen2.py Co-authored-by:
Ren Xuancheng <jklj077@users.noreply.github.com> Co-authored-by:
renxuancheng.rxc <renxuancheng.rxc@alibaba-inc.com> Co-authored-by:
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- 08 Jan, 2024 1 commit
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NielsRogge authored
* Add first draft * Use appropriate gelu function * More improvements * More improvements * More improvements * Convert checkpoint * More improvements * Improve docs, remove print statements * More improvements * Add link * remove unused masking function * begin tokenizer * do_lower_case * debug * set split_special_tokens=True * Remove script * Fix style * Fix rebase * Use same design as CLIP * Add fast tokenizer * Add SiglipTokenizer to init, remove extra_ids * Improve conversion script * Use smaller inputs in conversion script * Update conversion script * More improvements * Add processor to conversion script * Add tests * Remove print statements * Add tokenizer tests * Fix more tests * More improvements related to weight initialization * More improvements * Make more tests pass * More improvements * More improvements * Add copied from * Add canonicalize_text * Enable fast tokenizer tests * More improvements * Fix most slow tokenizer tests * Address comments * Fix style * Remove script * Address some comments * Add copied from to tests * Add more copied from * Add more copied from * Add more copied from * Remove is_flax_available * More updates * Address comment * Remove SiglipTokenizerFast for now * Add caching * Remove umt5 test * Add canonicalize_text inside _tokenize, thanks Arthur * Fix image processor tests * Skip tests which are not applicable * Skip test_initialization * More improvements * Compare pixel values * Fix doc tests, add integration test * Add do_normalize * Remove causal mask and leverage ignore copy * Fix attention_mask * Fix remaining tests * Fix dummies * Rename temperature and bias * Address comments * Add copied from to tokenizer tests * Add SiglipVisionModel to auto mapping * Add copied from to image processor tests * Improve doc * Remove SiglipVisionModel from index * Address comments * Improve docs * Simplify config * Add first draft * Make it like mistral * More improvements * Fix attention_mask * Fix output_attentions * Add note in docs * Convert multilingual model * Convert large checkpoint * Convert more checkpoints * Add pipeline support, correct image_mean and image_std * Use padding=max_length by default * Make processor like llava * Add code snippet * Convert more checkpoints * Set keep_punctuation_string=None as in OpenCLIP * Set normalized=False for special tokens * Fix doc test * Update integration test * Add figure * Update organization * Happy new year * Use AutoModel everywhere --------- Co-authored-by:patil-suraj <surajp815@gmail.com>
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- 03 Jan, 2024 1 commit
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Connor Henderson authored
* start - docs, SpeechT5 copy and rename * add relevant code from FastSpeech2 draft, have tests pass * make it an actual conformer, demo ex. * matching inference with original repo, includes debug code * refactor nn.Sequentials, start more desc. var names * more renaming * more renaming * vocoder scratchwork * matching vocoder outputs * hifigan vocoder conversion script * convert model script, rename some config vars * replace postnet with speecht5's implementation * passing common tests, file cleanup * expand testing, add output hidden states and attention * tokenizer + passing tokenizer tests * variety of updates and tests * g2p_en pckg setup * import structure edits * docstrings and cleanup * repo consistency * deps * small cleanup * forward signature param order * address comments except for masks and labels * address comments on attention_mask and labels * address second round of comments * remove old unneeded line * address comments part 1 * address comments pt 2 * rename auto mapping * fixes for failing tests * address comments part 3 (bart-like, train loss) * make style * pass config where possible * add forward method + tests to WithHifiGan model * make style * address arg passing and generate_speech comments * address Arthur comments * address Arthur comments pt2 * lint changes * Sanchit comment * add g2p-en to doctest deps * move up self.encoder * onnx compatible tensor method * fix is symbolic * fix paper url * move models to espnet org * make style * make fix-copies * update docstring * Arthur comments * update docstring w/ new updates * add model architecture images * header size * md wording update * make style
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- 13 Dec, 2023 1 commit
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Younes Belkada authored
* v1 * add-new-model-like * revert * fix forward and conversion script * revert * fix copies * fixup * fix * Update docs/source/en/index.md * Apply suggestions from code review * push * fix * fixes here and there * up * fixup and fix tests * Apply suggestions from code review * add docs * fixup * fixes * docstring * add docstring * fixup * docstring * fixup * nit * docs * more copies * fix copies * nit * update test
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- 11 Dec, 2023 2 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:
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NielsRogge authored
* More improvements * Improve variable names * Update READMEs, improve docs
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- 07 Dec, 2023 2 commits
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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
馃槈 * 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:shauray8 <shauray8@users.noreply.github.com> Co-authored-by:
haotian-liu <haotian-liu@users.noreply.github.com> Co-authored-by:
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Alex McKinney authored
* Copies `modeling_flax_gpt_neo.py` to start * MLP Block. WIP Attention and Block * Adds Flax implementation of `LlamaMLP` Validated with in-file test. Some slight numeric differences, but assuming it isn't an issue * Adds `FlaxLlamaRMSNorm` layer `flax.linen` includes `RMSNorm` layer but not necessarily in all versions. Hence, we add in-file. * Adds FlaxLlamaAttention Copied from GPT-J as it has efficient caching implementation as well as rotary embeddings. Notice numerically different, but not by a huge amount. Needs investigating * Adds `FlaxLlamaDecoderLayer` numerically inaccurate, debugging.. * debugging rotary mismatch gptj uses interleaved whilst llama uses contiguous i think they match now but still final result is wrong. maybe drop back to just debugging attention layer? * fixes bug with decoder layer still somewhat numerically inaccurate, but close enough for now * adds markers for what to implement next the structure here diverges a lot from the PT version. not a big fan of it, but just get something working for now * implements `FlaxLlamaBlockCollection`] tolerance must be higher than expected, kinda disconcerting * Adds `FlaxLlamaModule` equivalent PyTorch model is `LlamaModel` yay! a language model
馃 * adds `FlaxLlamaForCausalLMModule` equivalent to `LlamaForCausalLM` still missing returning dict or tuple, will add later * start porting pretrained wrappers realised it probably needs return dict as a prereq * cleanup, quality, style * readds `return_dict` and model output named tuples * (tentatively) pretrained wrappers work馃敟 * fixes numerical mismatch in `FlaxLlamaRMSNorm` seems `jax.lax.rsqrt` does not match `torch.sqrt`. manually computing `1 / jax.numpy.sqrt` results in matching values. * [WIP] debugging numerics * numerical match I think issue was accidental change of backend. forcing CPU fixes test. We expect some mismatch on GPU. * adds in model and integration tests for Flax Llama summary of failing: - mul invalid combination of dimensions - one numerical mismatch - bf16 conversion (maybe my local backend issue) - params are not FrozenDict * adds missing TYPE_CHECKING import and `make fixup` * adds back missing docstrings needs review on quality of docstrings, not sure what is required. Furthermore, need to check if `CHECKPOINT_FOR_DOC` is valid. See TODO * commenting out equivalence test as can just use common * debugging * Fixes bug where mask and pos_ids were swapped in pretrained models This results in all tests passing now馃敟 * cleanup of modeling file * cleanup of test file * Resolving simpler review comments * addresses more minor review comments * fixing introduced pytest errors from review * wip additional slow tests * wip tests need to grab a GPU machine to get real logits for comparison otherwise, slow tests should be okay * `make quality`, `make style` * adds slow integration tests - checking logits - checking hidden states - checking generation outputs * `make fix-copies` * fix mangled function following `make fix-copies` * adds missing type checking imports * fixes missing parameter checkpoint warning * more finegrained 'Copied from' tags avoids issue of overwriting `LLAMA_INPUTS_DOCSTRING` * swaps import guards ??? how did these get swapped initially? * removing `inv_freq` again as pytorch version has now removed * attempting to get CI to pass * adds doc entries for llama flax models * fixes typo in __init__.py imports * adds back special equivalence tests these come from the gpt neo flax tests. there is special behaviour for these models that needs to override the common version * overrides tests with dummy to see if CI passes need to fill in these tests later * adds my contribution to docs * `make style; make quality` * replaces random masking with fixed to work with flax version * `make quality; make style` * Update src/transformers/models/llama/modeling_flax_llama.py Co-authored-by:
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- 05 Dec, 2023 1 commit
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Arindam Jati authored
* patchtsmixer initial commit * x,y->context_values,target_values, unittest addded * cleanup code * minor * return hidden states * model tests, partial integration tests * ettm notebook temporary * minor * config mask bug fix, tests updated * final ETT notebooks * add selfattn * init * added docstrings * PatchTSMixerForPretraining -> PatchTSMixerForMaskPretraining * functionality tests added * add start and input docstrings * docstring edits * testcase edits * minor changes * docstring error fixed * ran make fixup * finalize integration tests and docs * minor * cleaned gitignore * added dataclass decorator, ran black formatter * ran ruff * formatting * add slow decorator * renamed in_Channel to input_size and default to 1 * shorten dataclass names * use smaller model for testing * moved the 3 heads to the modeling file * use scalers instead of revin * support forecast_channel_indices * fix regression scaling * undo reg. scaling * removed unneeded classes * forgot missing * add more layers * add copied positional_encoding * use patchmask from patchtst * removed dependency on layers directory * formatting * set seed * removed unused imports * fixed forward signature test * adding distributional head for PatchTSMixerForecasting * add generate to forecast * testcases for generate * add generate and distributional head for regression * raise Exception for negative values for neg binominal distribution * formatting changes * remove copied from patchtst and add TODO for test passing * make copies * doc edits * minor changes * format issues * minor changes * minor changes * format docstring * change some class names to PatchTSMixer + class name Transpose to PatchTSMixerTranspose GatedAttention to PatchTSMixerGatedAttention * change NormLayer to PatchTSMixerNormLayer * change MLP to PatchTSMixerMLP * change PatchMixer to PatchMixerBlock, FeatureMixer to FeatureMixerBlock * change ChannelFeatureMixer to ChannelFeatureMixerBlock * change PatchMasking to PatchTSMixerMasking * change Patchify to PatchTSMixerPatchify * list to `list` * fix docstrings * formatting * change bs to batch_size, edit forecast_masking * edit random_masking * change variable name and update docstring in PatchTSMixerMasking * change variable name and update docstring in InjectScalerStatistics4D * update forward call in PatchTSMixerTranspose * change variable name and update docstring in PatchTSMixerNormLayer * change variable name and update docstring in PatchTSMixerMLP * change variable name and update docstring in ChannelFeatureMixerBlock * formatting * formatting issues * docstring issue * fixed observed_mask type in docstrings * use FloatTensor type * formatting * fix rescaling issue in forecasting, fixed integration tests * add docstring from decorator * fix docstring * Update README.md Co-authored-by:
Arindam Jati <arindam.jati@ibm.com> Co-authored-by:
vijaye12 <vijaye12@in.ibm.com> Co-authored-by:
Kashif Rasul <kashif.rasul@gmail.com> Co-authored-by:
nnguyen <nnguyen@us.ibm.com> Co-authored-by:
vijaye12 <vijaykr.e@gmail.com> Co-authored-by:
Nam Nguyen <namctin@gmail.com> Co-authored-by:
Wesley Gifford <79663411+wgifford@users.noreply.github.com> Co-authored-by:
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