* Add tf code for efficientformer
* Fix return dict bug - return last hidden state after last stage
* Fix corresponding return dict bug
* Override test tol
* Change default values of training to False
* Set training to default False X3
* Rm axis from ln
* Set init in dense projection
* Rm debug stuff
* Make style; all tests pass.
* Modify year to 2023
* Fix attention biases codes
* Update the shape list logic
* Add a batch norm eps config
* Remove extract comments in test files
* Add conditional attn and hidden states return for serving output
* Change channel dim checking logic
* Add exception for withteacher model in training mode
* Revert layer count for now
* Add layer count for conditional layer naming
* Transpose for conv happens only in main layer
* Make tests smaller
* Make style
* Update doc
* Rm from_pt
* Change to actual expect image class label
* Remove stray print in tests
* Update image processor test
* Remove the old serving output logic
* Make style
* Make style
* Complete test
* First commit
* Add auto-translation with GPT-4
* make fixup
* Add a functional layernorm for TF
* Add all the auxiliary imports etc.
* Add the extra processor and tests
* rebase to main
* Add all the needed fixes to the GPT code
* make fixup
* Make convolutions channels-last so they run on CPU
* make fixup
* Fix final issues
* Fix other models affected by test change
* Clarify comment on the sparse_prompt_embeddings check
* Refactor functional_layernorm, use shape_list in place of .shape in some places
* Remove deprecated torch-alike code
* Update tests/models/sam/test_modeling_tf_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/sam/test_modeling_tf_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Refactor processor with common methods and separated private methods
* make fixup
* Quietly delete the file that didn't do anything (sorry Sylvain)
* Refactor the processor tests into one file
* make fixup
* Clean up some unnecessary indirection
* Fix TF mask postprocessing
* Add more processor equivalence tests
* Refactor generate_crop_boxes to use framework-neutral np code
* Make the serving output correctly conditional
* Fix error message line length
* Use dict keys rather than indices internally in both TF and PT SAM call/forward
* Return dicts internally in the call/forward methods
* Revert changes to common tests and just override check_pt_tf_outputs
* Revert changes to other model tests
* Clarify comments for functional layernorm
* Add missing transpose from PT code
* Removed unused copied from in PT code
* Remove overrides for tests that don't exist in TF
* Fix transpose and update tests for PT and TF to check pred_masks
* Add training flag
* Update tests to use TF checkpoints
* Update index.mdx
* Add missing cross-test decorator
* Remove optional extra asterisks
* Revert return_dict changes in PT code
* Update src/transformers/models/sam/modeling_tf_sam.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Remove None return annotations on init methods
* Update tests/models/sam/test_processor_sam.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Fix input_boxes shapes
* make fixup
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* First draft of RWKV-4
* Add support for generate
* Style post-rebase
* Properly use state
* Write doc
* Fix doc
* More math
* Add model to README, dummies and clean config
* Fix init
* multiple fixes:
- fix common tests
- fix configuraion default values
- add CI test for checking state computation
- fix some CI tests
* correct tokenizer
* some tweaks
- fix config docstring
- fix failing tests
* fix CI tests
- add output_attention / output_hidden_states
- override test_initialization
- fix failing CIs
* fix conversion script
- fix sharded case
- add new arguments
* add slow tests + more fixes on conversion script
* add another test
* final fixes
* change single name variable
* add mock attention mask for pipeline to work
* correct eos token id
* fix nits
* add checkpoints
* Apply suggestions from code review
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* add `tie_word_embeddings` in docstring
* change tensor name
* fix final nits
* Trigger CI
---------
Co-authored-by: younesbelkada <younesbelkada@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* first draft - gives index error in question_answering.py
* maturing
* no labels
* pipeline should know about QA
* fixing checks
* formatting
* fixed docstring
* initial commit
* formatting
* adding the class to many places
* towards less unhappy checks
* nearly there
* and gpt neox for qa
* use right model
* forgot this one
* base_model_prefix is "gpt_neox" for GPTNeoX* models
* unnecessary stuff
* Update src/transformers/models/gpt_neox/modeling_gpt_neox.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* format
* Update src/transformers/models/gpt_neox/modeling_gpt_neox.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* removed gpt2 stuff
---------
Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* first draft - gives index error in question_answering.py
* maturing
* no labels
* pipeline should know about QA
* fixing checks
* formatting
* fixed docstring
* initial commit
* formatting
* adding the class to many places
* towards less unhappy checks
* nearly there
* Update src/transformers/models/gpt_neo/modeling_gpt_neo.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* avoid error
* moving to device of star/end_logits
---------
Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* [doc] Try a few ≠ ways of linking to Papers, users, and org profiles
* Empty commit
* Empty commit now that the backend is fixed
---------
Co-authored-by: Lysandre <lysandre@huggingface.co>
* first draft - gives index error in question_answering.py
* maturing
* no labels
* pipeline should know about QA
* fixing checks
* formatting
* fixed docstring
* make sure legacy code executes
* comment
* like this
---------
Co-authored-by: Prof. Peter Schneider-Kamp <jps@ordbogen.com>
Adds FocalNet by Microsoft to transformers
---------
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
Co-authored-by: alaradirik <alaradirik@gmail.com>
* Add model to doc tests
* Remove generate and replace by prepare_inputs_for_generation
* More fixes
* Remove print statements
* Update integration tests
* Fix generate
* Remove model from auto mapping
* Use auto processor
* Fix integration tests
* Fix test
* Add inference code snippet
* Remove is_encoder_decoder
* Update docs
* Remove notebook link
* resolve conflicts
* rebase and make style
* test
* test
* test
* rebase and make style
* rebase and make style
* tests
* tests
* rewrite some functions
* rebase and make style
* fix load_tf_weights_in_cpmant
* reformat some unrelated files
* upgrade quality
* fix some bugs & docstring
* add models and tests
* solve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* tests
* resolve conflicts
* resolve conflicts
* fix load_tf_weights_in_cpmant
* reformat some unrelated files
* upgrade quality
* fix some bugs & docstring
* save resolution
* make style
* delete redefinition code
* reformat function
* reformat
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* tests
* resolve conflicts
* resolve conflicts
* fix load_tf_weights_in_cpmant
* reformat some unrelated files
* upgrade quality
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* resolve conflicts
* fix load_tf_weights_in_cpmant
* reformat some unrelated files
* upgrade quality
* resolve conflicts
* make style
* fix bugs and refactor
* modify docstrings and make style
* unify import format in __init__.py
* fix import-altclp bug
* fix copies to update index.md
* fix unused config parameters
* fix unused config parameters
* fix unused config parameters
* update README_ja.md
* dummy commit for unit test
* fix attention mask
* add CPMAntTokenizer&-Fast to auto-mapping
* drop redundant changes in README_ko
* fix defaults in docstring
* fix use_cache and some docstring
* add missing args in tokenizer
* modify tester inheritance
* add is_jieba_available
* fix some bugs
* make style and fix-copies
* add doctests
* skip integration tests
* add is_jieba_available
* fix bugs in common tests
* adjust docstrings and make style
* add argument docstring
* adjust code to some specifications
* make style and fix-copies
* add fast tokenization test
* dummy commit for unit test
* dummy commit for unit test
* dummy commit for unit test
* normalize some comments and names
* Bert->CPMAnt
* camel names and drop redundant codes
* make style and fix-coies
* add CpmTokenizerFast _import_structure
* drop cpmanttokenizerfast in model_doc
* fix some problems
* fix CPMAnt tokenization for common test
* make style and fixup
* fix copies and fixup
* fix bugs in tokenization test
* dummy commit for connection failure in unittest
* fix copies
* drop trailing comma
* fix decorator in tests
* dummy commit for connection failure in unittest
---------
Co-authored-by: Gong Baitao <gongbaitao11@gmail.com>
* Adding Llama FastTokenizer support.
- Requires https://github.com/huggingface/tokenizers/pull/1183 version
- Only support byte_fallback for llama, raise otherwise (safety net).
- Lots of questions are special tokens
How to test:
```python
from transformers.convert_slow_tokenizer import convert_slow_tokenizer
from transformers import AutoTokenizer
from tokenizers import Tokenizer
tokenizer = AutoTokenizer.from_pretrained("huggingface/llama-7b")
if False:
new_tokenizer = Tokenizer.from_file("tok.json")
else:
new_tokenizer = convert_slow_tokenizer(tokenizer)
new_tokenizer.save("tok.json")
strings = [
"This is a test",
"生活的真谛是",
"生活的真谛是[MASK]。",
# XXX: This one is problematic because of special tokens
# "<s> Something something",
]
for string in strings:
encoded = tokenizer(string)["input_ids"]
encoded2 = new_tokenizer.encode(string).ids
assert encoded == encoded2, f"{encoded} != {encoded2}"
decoded = tokenizer.decode(encoded)
decoded2 = new_tokenizer.decode(encoded2)
assert decoded.strip() == decoded2, f"{repr(decoded)} != {repr(decoded2)}"
```
The converter + some test script.
The test script.
Tmp save.
Adding Fast tokenizer + tests.
Adding the tokenization tests.
Correct combination.
Small fix.
Fixing tests.
Fixing with latest update.
Rebased.
fix copies + normalized added tokens + copies.
Adding doc.
TMP.
Doc + split files.
Doc.
Versions + try import.
Fix Camembert + warnings -> Error.
Fix by ArthurZucker.
Not a decorator.
* Fixing comments.
* Adding more to docstring.
* Doc rewriting.
* Initial commit
* more stash commit
* Yet another stash commit
* yet more stash commit
* Mostly working except for docs / repo consistency
* Stop importing model list from torch file
* Add TF BLIP models to docs
* Add auto classes
* Move get_text_features and get_image_features
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/blip/test_modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update tests/models/blip/test_modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update tests/models/blip/test_modeling_tf_blip_text.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
* Use channels_last convolutions in TF (better performance + compatibility)
* Remove _shape function
* Move multi-line statement to one line in PT + TF
* Specify tf.keras.layers instead of importing from it
* Remove test_gradient_checkpointing and empty test_training methods
* move some multi-line statements to one line
* Update docstring for generate
* Remove pruned heads set
* Remove self.seq_len_dim
* Fixed issues with loss computation, should resolve some tests. Also ensured that the PT version follows the config for output_attentions and output_hidden_states
* ensure original model follows config in more cases
* Skip the same cross-attention tests in the PT tests - didn't realize we did it twice!
* Add training args throughout the models and layers
* make fixup
* Fix docstring for inputs_embeds
* Add docstring for is_decoder
* Add docstrings to text models
* Remove redundant computation
* Add unpack_inputs / keras_serializable
* Add modeling_tf_blip to doctests
* Add config classes for keras serialization
* Changes to allow model porting with pt-to-tf
* Quick fix to decoder head and test tweaks
* Revert an issue with masking the embeddings outputs
* Allow missing keys in some equivalence tests (for unused layers)
* Add tf-pt equivalence tests back in
* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make fixup
* Refactor invert_attention_mask out into tf_utils
* Re-enable cross-tests on the PT side too
---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Initial commit
* update modeling code
* update doc
* add functions necessary
* fix impotrs
* revert changes
* fixup
* more styling to get going
* remove standalone encoder
* update code
* styling
* fix config and model
* update code and some refactoring
* make more tests pass
* Adding NLLB-200 - MoE - 54.5B for no language left behind
Fixes#21300
* fix mor common tests
* styke
* update testing file
* update
* update
* Router2 doc
* update check config with sparse layer
* add dummy router
* update current conversion script
* create on the fly conversion script
* Fixup
* style
* style 2
* fix empty return
* fix return
* Update default config sparse layers
* easier to create sparse layers
* update
* update conversion script
* update modeling
* add to toctree
* styling
* make ruff happy
* update docstring
* update conversion script
* update, will break tests but impelemting top2
* update
* ❗local groups are supported here
* ⚠️ Support for local groups is now removed ⚠️
This is because it has to work with model parallelism that we do not support
* finish simplificaiton
* Fix forward
* style
* fixup
* Update modelling and test, refactoring
* update tests
* remove final layer)norm as it is done in the FF
* routing works! Logits test added
* nit in test
* remove top1router
* style
* make sure sparse are tested. Had to change route_tokens a liottle bit
* add support for unslip models when converting
* fixup
* style
* update test s
* update test
* REFACTOR
* encoder outputs match!
* style
* update testing
* 🎉encoder and decoder logits match 🎉
* styleing
* update tests
* cleanup tests
* fix router test and CIs
* cleanup
* cleanup test styling
* fix tests
* Finally the generation tests match!
* cleanup
* update test
* style testing file
* remove script
* cleanup
* more cleanup
* nits
* update
* NLLB tokenizer is wrong and will be fixed soon
* use LongTensors
* update tests
* revert some small changes
* fix second expert sampling and batch prioritized routing
* update tests
* finish last tests
* make ruff happy
* update
* ruff again
* style
* Update docs/source/en/model_doc/nllb-moe.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Updates based on review
* style and fix import issue
* nit
* more nits
* cleanup
* styling
* update test_seconde_expert_policy
* fix name
* last nit on the markdown examples
---------
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add mega file structure and plain pytorch version of mega source code
* added config class with old naming conventions
* filled in mega documentation
* added config class and embeddings with optional token types
* updated notes
* starting the conversion process, deleted intermediate and added use_cache back to config
* renamed config attributes in modeling_mega.py
* checkpointing before refactoring incremental decoding functions
* removed stateful incremental key/values for EMA and self-attention
* refactored MovingAverageGatedAttention to remove stateful k/v history and use unified attention mask
* MovingAverageGatedAttention works with incremental decoding + past values, added sequence length enforcement
* more comments in MovingAverageGatedAttention + checkpointing before GatedCrossAttention
* bug fix in attention mask handling in MovingAverageGatedAttention
* removed incremental state from GatedCrossAttention and removed IncrementalState class
* finished gated cross attention and got MegaLayer working
* fixed causal masking in mega decoder
* fixed how padding and causal masks are passed through MegaLayer with and without k/v caching
* finished MegaModel; tested with encoder, decoder-only, and cross-attention type inputs; started work on downstream classes; removed mentions of position_ids
* added optional dense hidden layer for masked and causal LM classes
* docstring updates in MultiHeadEMA and GatedCrossAttention, removed unnecessary inputs in cross-attention
* removed before_attn_fn in Mega class and updated docstrings and comments up to there
* bug fix in MovingAverageGatedAttention masking
* working conversion of MLM checkpoint in scratchpad script -- perfect matches
* moved arg for hidden dense layer in LM head to config; discovered issue where from_pretrained is renaming gamma and beta parameters
* renamed gamma and beta parameters to avoid HF renaming when loading from checkpoint
* finished checkpoint conversion script
* cleanup old class in mega config script
* removed 'copied from' statements and passing integration tests
* added num_attention_heads=1 to config for integration compatibility, decoder tests working, generation tests failing
* fixed tuple output of megamodel
* all common tests passing after fixing issues in decoder, gradient retention, and initialization
* added mega-specific tests, ready for more documentation and style checks
* updated docstrings; checkpoint before style fixes
* style and quality checks, fixed initialization problem in float_tensor, ready for PR
* added mega to toctree
* removed unnecessary arg in megaconfig
* removed unused arg and fixed code samples with leftover roberta models
* Apply suggestions from code review
Applied all suggestions except the one renaming a class, as I'll need to update that througout
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixed issue where .view breaks batch dimension, conversion script fixed with absolute imports, updated readme with Mega->MEGA
* removed asserts in Mega code, renamed sequencenorm, gatedcrossattention, and NFFN, replaced get_activation_fn with ACTFN, and added sequencenorm to layer norms
* reformatted .forward() docstrings to match style and removed unused mask input in cross-attention
* removed all reset_parameters() methods and rolled into MegaPreTrainedModel._init_weights()
* renamed all single-letter variables and improved readability in tensor size comments, Mega->MEGA in 2 documentation files
* variable names in NFFN
* manual Mega->MEGA changes in docs
* Mega->MEGA in config auto
* style and quality fixes
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* renamed parameters and variables with confusing names, added copied from statements, moved fft conv to its own method, other cleanup from PR comments
* commit before dealing with merge conflicts
* made new attention activation functions available in ACT2FN and added generation test from OPT
* style and quality in activations and tests
* documentation fixes, renaming variables in dropout and rotary positions, used built-in causal masking, encoders->layers in MegaModel, moved comments into docstrings
* style and quality fixes after latest updates, before rotary position ids
* causal mask in MegaBlock docstring + added missing device passing
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update README.md
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* added Mega prefixes where missing, reverted MegaSequenceNorm to if-else, other module renaming requested in PR
* style and quality fixes + readme updates pointing to main
---------
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* add new model of MGP-STR
* fix the check failings
* remove torch and numpy from mgp_tokenization
* remove unused import from modeling_mgp_str
* add test_processing_mgp_str
* rm test_processing_mgp_str.py
* add test_processing_mgp_str
* add test_processing_mgp_str
* add test_processing_mgp_str
* rm test_processing_mgp_str and add softmax outs to model
* rm test_processing_mgp_str and add softmax outs to model
* rewrite the code of mgp-str according to PR suggestions
* rewrite the code of mgp-str according to PR suggestions
* add new model of MGP-STR
* fix the check failings
* remove torch and numpy from mgp_tokenization
* remove unused import from modeling_mgp_str
* add test_processing_mgp_str
* rm test_processing_mgp_str.py
* add test_processing_mgp_str
* add test_processing_mgp_str
* add test_processing_mgp_str
* rm test_processing_mgp_str and add softmax outs to model
* rewrite the code of mgp-str according to PR suggestions
* rewrite the code of mgp-str according to PR suggestions
* remove representation_size from MGPSTRConfig
* reformat configuration_mgp_str.py
* format test_processor_mgp_str.py
* add test for tokenizer and complete model/processer test and model file
* rm Unnecessary tupple in modeling_mgp_str
* reduce hidden_size/layers/label_size in test_model
* add integration tests and change MGPSTR to Mgpstr
* add test for logit values
* reformat test model file
---------
Co-authored-by: yue kun <yuekun.wp@alibaba-inc.com>
* added informer to gitignore
* added informer to gitignore
* WIP informer2020
* added checking that instantiate works
* added config using gluonTS by kashif
* WIP config
* adding informeConfig. need to remove FeatureEmbedder
* done InformerConfig, but need to change the names
* Done informer model init. working on enc-dec
* added things to address, after reading again enc-dec in the paper
* done modeling - checking initialization work
* added informer to gitignore
* WIP informer2020
* added checking that instantiate works
* added config using gluonTS by kashif
* WIP config
* adding informeConfig. need to remove FeatureEmbedder
* done InformerConfig, but need to change the names
* Done informer model init. working on enc-dec
* added things to address, after reading again enc-dec in the paper
* done modeling - checking initialization work
* moved enc-dec init to InformerEncoder/Decoder init
* added 'init_std' to config, now model init works!
* WIP conversion script, and added code sources
* WIP conversion script: loading original informer pth works
* WIP conversion script: change defaults in the config
* WIP conversion script: supporting Informer input embedding
* WIP conversion script: added parameters for the informer embed
* WIP conversion script: change dim_feedforward=2048
* WIP conversion script: remove unused args for loading checkpoint
* just cleaning up
* DataEmbedding removed, after thinking with Kashif
* working on forward pass
* WIP forward pass: trying to establish working batch for forward pass
* cleaning and finalizing
* adding HF names and docs
* init after cleaning works
* WIP in tests
* added docs for the informer specific args
* fix style
* undo change
* cleaning informer, now need to work only enc-dec
* initial enc-dec classes
* added encoder and decoder
* added todo
* add todos for conv_layers
* added decoder docs from vanilla
* added encoder docs from vanilla
* remove encoder decoder from the original informer
* removed AttentionLayer from the original paper
* removed TriangularCausalMask, same as decoder_attention_mask
* initial sparse attention
* use conv_layers
* fixed test_config test
* fix parenthesis when itearting zip(layers, conv_layers)
* error found in prob attention, added sizes as comments
* fix sizes
* added proposal for q_reduce indexing, and remove unused
* WIP ProbMask, and changed factor=2 for testing
* remove unused libs for this PR for creating the env
* fix checking the attn_weights.size() after bmm
* Q_reduce: changed from torch.gather to simple slicing
* WIP calculate final attn_output
* finish adding v_aggregated, attn_output ready
* changed tgt_len to u in attention_mask, need to fix the size error
* comment attention_mask for encoder, and fix if cond for v_agg
* added ProbMask support (wip), removed old original code
* finished ProbMask 😃
* Revert "remove unused libs for this PR for creating the env"
This reverts commit 11a081e09e.
* fixes
* make style
* fix initial tests
* fix more tests
* dry
* make style
* remove unused files
* style
* added integration tests
* fix num_static_real_features
* fix header
* remove unused function
* fix example
* fix docs
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/modeling_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* Update src/transformers/models/informer/configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* fixes for reviewer
* use prediction_length from model
* fix style
* fixed informer.mdx
* added to index
* updated readme
* undo
* make fix-copies
* typo
* fix copy
* added Informer to toctree
* in order
* fixed comments
* remove unneeded new lines in docs
* make static real and cat optional
* fix use of distil conv layers
* fixed integration test
* added checkpoint for convlayer
* make fix-copies
* updated from time series model
* make fix-copies
* copy decoder
* fix unit tests
* updated scaling config
* fix integration tests
* IGNORE_NON_TESTED
* IGNORE_NON_AUTO_CONFIGURED
* IGNORE_NON_AUTO_CONFIGURED
* updated check configs
* fix formatting
* undo change from time series
* prediction_length should not be None
* aliign with the blog: prettify ProbSparse and change attention_factor to sampling_factor
* make style
* make fix-copies
* niels CR: update contributed by
* niels CR: update configuration_informer.py
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* niels CR: update kashif -> huggingface
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* niels CR: `sampling_factor` only relevant when `attention_type`=prob
* make style
* fixed U_part: added multiplication by `L_Q`
* fixed bug: remove `is not None` from `if config.distil`
* fixed test: `decoder_seq_length` to `encoder_seq_length` in cross_attentions check
* fix integration tests
* updated model hub
* do not shift as in training
* undo
* fix make-copies
* make fix-copies
* added `if prediction_length is None`
* changed `ProbSparseAttention` to `InformerProbSparseAttention`
* changed `V_sum` -> `v_mean_dim_time`
* changed `ConvLayer` to `InformerConvLayer` and fixed `super()`
* TimeSeriesTansformer->Informer in decoder's Copied from
* more descriptive in ProbSparse
* make style
* fix coped from
* Revert "added `if prediction_length is None`"
This reverts commit b4cbddfa05.
* fixed indent
* use InformerSinusoidalPositionalEmbedding
* make fix-style
* fix from #21860
* fix name
* make fix-copies
* use time series utils
* fix dec num_heads
* docstring
* added time series util doc
* _import_structure
* formatting
* changes from review
* make style
* fix docs
* fix doc
* removed NegativeLogLikelihood
---------
Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
* [Whisper] Add model for audio classification
* make fix-copies
* add to docs
* add docstring
* empty returns
* add code example
* switch to fleurs
* stick everything on one line
Adds the ALIGN model to transformers. ALIGN is introduced in "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision" by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
* first draft of model summary
* restructure docs
* finish first draft
* ✨minor reviews and edits
* apply feedbacks
* save important info, create new page for attention
* add attention doc to toctree
* ✨ few more minor fixes
* config and tokenization(fast too) changed and ErnieEncoder added
* Slow Tokenization Added
* Tokenizer(slow) is now working and Fast Tokenizer removed
* Added Config code
* Added Base Model and utils
* ErnieMModel is now working
* All added except tests
* All tests passed except ErnieUIEM
* All tests passed
* all fixes done
* all fixes done
* fixed MAP
* fixed check_code_quality
* fixed Build PR Documentation issue
* Added changes(comments) and also updated to the latest upstream/main
* Added fixup
* Added # Copied comments
* Added fixup
* Added more comments and some nits
* Added fixup
* Fixed README_hd.md
* Added more fixes
* ErnieMTokenizer (being sentencepiece) protected and other docs edited
* Added code_quality fix
* Fixed for
* Added more fix
* modified AZ
* ernie-m tokenization test added!
* attention mask part fixed(with 0->self.config.pad_token_id)
* applied make fixup
* Add X-MOD to Readme
* Add documentation for X-MOD
* Implement X-MOD
* Fix formatting of X-MOD docs
* Change signature of X-MOD forward methods to use lang_ids
* Minor changes
* Rebase with main and run make fix-copies
* Make suggested changes to docstrings
* Improve code readability
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* Fix code style
* Conversion script: Remove asserts and type annotations
* Remove _TOKENIZER_FOR_DOC
* XMOD -> Xmod
* Update copyright note
* Fix doctests
* Fix docstring
* Add integration test for FillMaskPipeline
* Revert "Add integration test for FillMaskPipeline"
This reverts commit 4381eb3b1d0f5d85785f89caba83928e6efa6d1f.
* Add end-to-end integration test for mask fill
* make style
* Rebase with main and make fix-copies
---------
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
* First draft
* More improvements
* More improvements
* Improve conversion script
* Convert all weights
* Make forward pass work
* Make logits match
* More improvements
* More improvements
* More improvements
* Use get_input_embeddings
* Improve some more
* Improve model tests
* Improve model tests
* More improvements
* Fix processor
* Update files
* Update prepare_inputs_for_generation
* More improvements
* Fix copies
* More fixes
* Make fixup
* More improvements
* Add support for seq2seq language model
* More improvements
* Fix test
* More improvements
* Improve conversion script
* Remove some todo's
* Fix README's
* Improve conversion script
* Fix generation
* Fix style and remove Blip2Model
* Fix model outputs
* More improvements
* Set eos_token_id in config
* Fix quality
* Small improvements
* Add processor tests
* More improvements
* Apply suggestions
* Apply suggestions
* Add integration test
* Update image URL
* Add integration test
* Fix model_type
* Update style
* Improve docs
* Add doc tests
* Fix copies
* Remove tests which are passing
* Improve some more
* Add tests for seq2seq language models
* Minor fix
* Convert more checkpoints
* finalize CI
* Fix blip and blip2 processors
* add `accelerate` support for `blip2`
* clean up
* make style
* Update conversion script
* Update conversion script some more
* Update organization
* revert toc file
* add blip-2 to toc file
* Some more improvements
* Fix docstring
* Improve docs
---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: younesbelkada <younesbelkada@gmail.com>
* doc: introduce new section for XLM-V model
* doc: mention more details for XLM-V integration
* docs: paper abstract in italics, model identifier for base model added
* doc: mention new XLM-V support
* auto: add XLM-V mapping
* doc: run make fix-copies ;)
* make SpeechT5 model by copying Wav2Vec2
* add paper to docs
* whoops added docs in wrong file
* remove SpeechT5Tokenizer + put CTC back in the name
* remove deprecated class
* remove unused docstring
* delete SpeechT5FeatureExtractor, use Wav2Vec2FeatureExtractor instead
* remove classes we don't need right now
* initial stab at speech encoder prenet
* add more speech encoder prenet stuff
* improve SpeechEncoderPrenet
* add encoder (not finished yet)
* add relative position bias to self-attention
* add encoder CTC layers
* fix formatting
* add decoder from BART, doesn't work yet
* make it work with generate loop
* wrap the encoder into a speech encoder class
* wrap the decoder in a text decoder class
* changed my mind
* changed my mind again ;-)
* load decoder weights, make it work
* add weights for text decoder postnet
* add SpeechT5ForCTC model that uses only the encoder
* clean up EncoderLayer and DecoderLayer
* implement _init_weights in SpeechT5PreTrainedModel
* cleanup config + Encoder and Decoder
* add head + cross attention masks
* improve doc comments
* fixup
* more cleanup
* more fixup
* TextDecoderPrenet works now, thanks Kendall
* add CTC loss
* add placeholders for other pre/postnets
* add type annotation
* fix freeze_feature_encoder
* set padding tokens to 0 in decoder attention mask
* encoder attention mask downsampling
* remove features_pen calculation
* disable the padding tokens thing again
* fixup
* more fixup
* code review fixes
* rename encoder/decoder wrapper classes
* allow checkpoints to be loaded into SpeechT5Model
* put encoder into wrapper for CTC model
* clean up conversion script
* add encoder for TTS model
* add speech decoder prenet
* add speech decoder post-net
* attempt to reconstruct the generation loop
* add speech generation loop
* clean up generate_speech
* small tweaks
* fix forward pass
* enable always dropout on speech decoder prenet
* sort declaration
* rename models
* fixup
* fix copies
* more fixup
* make consistency checker happy
* add Seq2SeqSpectrogramOutput class
* doc comments
* quick note about loss and labels
* add HiFi-GAN implementation (from Speech2Speech PR)
* rename file
* add vocoder to TTS model
* improve vocoder
* working on tokenizer
* more better tokenizer
* add CTC tokenizer
* fix decode and batch_code in CTC tokenizer
* fix processor
* two processors and feature extractors
* use SpeechT5WaveformFeatureExtractor instead of Wav2Vec2
* cleanup
* more cleanup
* even more fixup
* notebooks
* fix log-mel spectrograms
* support reduction factor
* fixup
* shift spectrograms to right to create decoder inputs
* return correct labels
* add labels for stop token prediction
* fix doc comments
* fixup
* remove SpeechT5ForPreTraining
* more fixup
* update copyright headers
* add usage examples
* add SpeechT5ProcessorForCTC
* fixup
* push unofficial checkpoints to hub
* initial version of tokenizer unit tests
* add slow test
* fix failing tests
* tests for CTC tokenizer
* finish CTC tokenizer tests
* processor tests
* initial test for feature extractors
* tests for spectrogram feature extractor
* fixup
* more fixup
* add decorators
* require speech for tests
* modeling tests
* more tests for ASR model
* fix imports
* add fake tests for the other models
* fixup
* remove jupyter notebooks
* add missing SpeechT5Model tests
* add missing tests for SpeechT5ForCTC
* add missing tests for SpeechT5ForTextToSpeech
* sort tests by name
* fix Hi-Fi GAN tests
* fixup
* add speech-to-speech model
* refactor duplicate speech generation code
* add processor for SpeechToSpeech model
* add usage example
* add tests for speech-to-speech model
* fixup
* enable gradient checkpointing for SpeechT5FeatureEncoder
* code review
* push_to_hub now takes repo_id
* improve doc comments for HiFi-GAN config
* add missing test
* add integration tests
* make number of layers in speech decoder prenet configurable
* rename variable
* rename variables
* add auto classes for TTS and S2S
* REMOVE CTC!!!
* S2S processor does not support save/load_pretrained
* fixup
* these models are now in an auto mapping
* fix doc links
* rename HiFiGAN to HifiGan, remove separate config file
* REMOVE auto classes
* there can be only one
* fixup
* replace assert
* reformat
* feature extractor can process input and target at same time
* update checkpoint names
* fix commit hash
* updated resources for LayoutLM
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* fixed formatting, removed extra section
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Added resource section to GPT-J docs
* Added most of the links found
* Addressing review comments
* Fixing formatting
* Update docs/source/en/model_doc/gptj.mdx
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Fixing one of the labels
---------
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* [FT] First commit for graphormer architecture.
The model has no tokenizer, as it uses a collator and preprocessing function for its input management.
Architecture to be tested against original one.
The arch might need to be changed to fit the checkpoint, but a revert to the original arch will make the code less nice to read.
TODO: doc
* [FIX] removed test model
* [FIX] import error
* [FIX] black and flake
* [DOC] added paper refs
* [FIX] [DOC]
* [FIX] black
* [DOC] Updated READMEs
* [FIX] Order of imports + rm Tokenizer calls
* [FIX] Moved assert in class to prevent doc build failure
* [FIX] make fix-copies
* [Doc] update from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [FIX] Removed Graphormer from Sequence classification model list
* [DOC] Added HF copyright to Cython file
* [DOC] Fixed comments
* [FIX] typos in class doc + removed config classes.
Todo: update doc from paper definitions
* [FIX] Removed dependency to fairseq, and replaced all asserts with Exception management
* [FIX] Homogeneized initialization of weights to pretrained constructor
* [FIX] [CP] Updated multi_hop parameter to get same results as in original implementation
* [DOC] Relevant parameter description in the configuration file
* [DOC] Updated doc and comments in main graphormer file
* [FIX] make style and quality checks
* [DOC] Fix doc format
* [FIX] [WIP] Updated part of the tests, though still a wip
* [FIX] [WIP]
* [FIX] repo consistency
* [FIX] Changed input names for more understandability
* [FIX] [BUG] updated num_classes params for propagation in the model
* simplified collator
* [FIX] Updated tests to follow new naming pattern
* [TESTS] Updated test suite along with model
* |FIX] rm tokenizer import
* [DOC] add link to graphormerdoc
* Changed section in doc from text model to graph model
* Apply suggestions from code review
Spacing, inits
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* [DOC] Explain algos_graphormer functions
* Cython soft import protection
* Rm call to Callable in configuration graphormer
* [FIX] replaced asserts with Exceptions
* Add org to graphormer checkpoints
* Prefixed classes with Graphormer
* Management of init functions
* format
* fixes
* fix length file
* update indent
* relaunching ci
* Errors for missing cython imports
* fix style
* fix style doc
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* `blip` support for training
* remove labels creation
* remove unneeded `decoder_input_ids` creation
* final changes
- add colab link to documentation
- reduction = mean for loss
* fix nits
* update link
* clearer error message
* torch.jit._state
* Fix past CI
* Fix for perceiver
* Fix REALM
* Fix for Bloom
* Fix for SwinMode
* Fix for TrajectoryTransformerModel
* Fix for test_wav2vec2_with_lm
* make style
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
* Copy RoBERTa
* formatting
* implement RoBERTa with prelayer normalization
* update test expectations
* add documentation
* add convertion script for DinkyTrain weights
* update checkpoint repo
Unfortunately the original checkpoints assumes a hacked roberta model
* add to RoBERTa-PreLayerNorm docs to toc
* run utils/check_copies.py
* lint files
* remove unused import
* fix check_repo reporting wrongly a test is missing
* fix import error, caused by rebase
* run make fix-copies
* add RobertaPreLayerNormConfig to ROBERTA_EMBEDDING_ADJUSMENT_CONFIGS
* Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* fixup: Fix documentation <Facebook> -> Facebook
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add missing Flax header
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* expected_slice -> EXPECTED_SLICE
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* update copies after rebase
* add missing copied from statements
* make fix-copies
* make prelayernorm explicit in code
* fix checkpoint path for the original implementation
* add flax integration tests
* improve docs
* update utils/documentation_tests.txt
* lint files
* Remove Copyright notice
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* make fix-copies
* Remove EXPECTED_SLICE calculation comments
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Add templates for gpt-sw3
* Add templates for gpt-sw3
* Added sentencepiece tokenizer
* intermediate commit with many changes
* fixed conflicts
* Init commit for tokenization port
* Tokenization progress
* Remove fast tokenizer
* Clean up and rename spm.model -> spiece.model
* Remove TF -> PT conversion script template, Clean up Megatron -> PT script
* Optimize encode & decode performance
* added new attention
* added new attention
* attention for gpt-sw3 working
* attention good
* Cache is now working
* fixed attention mask so that it works with causal attention
* fixed badbmm bug for cpu and caching
* updated config with correct parameters
* Refactor and leave optimizations as separate functions to avoid breaking expected functionality
* Fix special tokens mapping for both tokenizers
* cleaning up of code and comments
* HF compatible attention outputs
* Tokenizer now passing tests, add documentation
* Update documentation
* reverted back to base implementation after checking that it is identical to pretrained model
* updated gpt-sw3 config
* updated conversion script
* aligned parameters with gpt-sw3 config
* changed default scale_attn_by_inverse_layer_idx to true
* removed flag from conversion script
* added temporary model path
* reverted back to functioning convert script
* small changes to default config
* updated tests for gpt-sw3
* make style, make quality, minor cleanup
* Change local paths to testing online repository
* Change name: GptSw3 -> GPTSw3
* Remove GPTSw3TokenizerFast references
* Use official model repository and add more model sizes
* Added reference to 6.7b model
* Add GPTSw3DoubleHeadsModel to IGNORE_NON_AUTO_CONFIGURED, like GPT2DoubleHeadsModel
* Remove pointers to non-existing TFGPTSw3
* Add GPTSw3 to docs/_toctree.yml
* Remove TF artifacts from GPTSw3 in __init__ files
* Update README:s with 'make fix-copies'
* Add 20b model to archive list
* Add documentation for GPT-Sw3
* Fix typo in documentation for GPT-Sw3
* Do 'make fix-copies' again after having updated docs
* Fix some typos in docs
* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/configuration_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update tests/models/gpt_sw3/test_tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/modeling_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Resolve comments from PR feedback
* Resolve more comments from PR feedback, also set use_cache=True in convert script
* Add '# Copied from' comments for GPTSw3 modeling
* Set 'is_parallelizable = False'
* Remove '# Copied from' where code was modified and add 'with x->y' when appropriate
* Remove parallelize in mdx
* make style, make quality
* Update GPTSw3Config default values and corresponding documentation
* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/__init__.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Clean up and protect GPTSw3Tokenizer imports with is_sentencepiece_available
* Make style, make quality
* Add dummy object for GPTSw3Tokenizer via 'make fix-copies'
* make fix-copies
* Remove GPTSw3 modeling classes
* make style, make quality
* Add GPTSw3 auto-mappings for other GPT2 heads
* Update docs/source/en/model_doc/gpt-sw3.mdx
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/convert_megatron_to_pytorch.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Update src/transformers/models/gpt_sw3/tokenization_gpt_sw3.py
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Remove old TODO-comment
* Add example usage to GPTSw3Tokenizer docstring
* make style, make quality
* Add implementation details and example usage to gpt-sw3.mdx
Co-authored-by: JoeyOhman <joeyoh@kth.se>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* read to load
* base functionality
* revert init
* fix dummy data
* moving right along
* moving right along
* finally
* cleanup
* pull out comment
* add test
* update docstring for main class
* flake comments and rewriting copies from make repo-consistency`
* remove irrelevant differences/accidental spaces
* put copies back after space removals
* mid
* final test pass
* stray comment
* update test file
* update test file
* fixup
* black
* missed
* black missed one more
* sytle
* add doc update
* fix order of output class
* comment
* Revert "comment"
This reverts commit 03f86b6948.
* remove redundant function, and redundant reshape
* move change out of common
* style
* put common spaces back
* reorder kwargs in output
* doc style
* biogpt initial commit
* updated init
* fix faster decoding with use_cache
* 1. fix input_ids and input_embeds with correct device
2. added _keys_to_ignore_on_load_missing
3. updated prepare_inputs_for_generation
* add activation_dropout and scale_embedding
* replace fsmt attention with bart attention
* added test
* run make fix-copies
* doc init and fix build
* updated README with proper information
* 1. added tips to docs
2. updated BioGptTokenizer func
* 1. added tokenizer test
2. refactor tokenizer
* make fixup
* add biogpt fairseq to hf converter
* updated layer names more
similar to original checkpoints
* config update doc string and set defaults
* added "#copied" from bart model and
updated doc strings
* enable model_input_names in tokenizer
* 1. positionalembedding depending on attention_mask
2. added attention mask to prepare for generation
* added test to verify past and generation
* BioGptLMHeadModel -> BioGptForCausalLM
* fix typo
* tokenization and test
Copyright and updated assertion
* updated Copyright and
one func at time in line
* Copyright updates and
minor doc fix
* replace assertion with ValueError
* rm extra space
* added code syntax
* revert cmnt position change
* add tokenizer to auto
* updated doc string
* tokenizer doc string update
* biogpt hub model update to microsoft/biogpt
* make fixup
* rm cmnt to fix flake8 5.0.4 vs 6 error
* add minimal working gpt2 tokenizer
* graph mode and output equivalence tests working
* not today tensorflow. serialization test passing!
* fix style, documentation, docstrings and all that jazz
* passing consistency checks
* move keras nlp to tf dependencies
* fix tf modeling utils and gpt2 attention to enable compiling
* fix (I hope) keras nlp dependencies
* rever changes on generation
* remove debug prints
* remove redundant tf dummy objects
* add from config, get config and max length settings to address review
* let flake ignore the error on distillation you are welcome
* test from config
* add padding test
* address sgugger review
* Add Donut image processor
* Update src/transformers/image_transforms.py
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* Fix docstrings
* Full var names in docstring
Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com>
* First draft
* Make conversion script work
* Add id2label mapping, run code quality
* Fix copies
* Add first draft of feature extractor
* Update conversion script to use feature extractor
* Make more tests pass
* Add docs
* update input_features to input_values + pad by default to max length
* Fix doc tests
* Add feature extractor tests
* Add proper padding/truncation to feature extractor
* Add support for conversion of all audioset checkpoints
* Improve docs and extend conversion script
* Fix README
* Rename spectogram to spectrogram
* Fix copies
* Add integration test
* Remove dummy conv
* Update to ast
* Update organization
* Fix init
* Rename model to AST
* Add require_torchaudio annotator
* Move import of ASTFeatureExtractor under a is_speech_available
* Fix rebase
* Add pipeline config
* Update name of classifier head
* Rename time_dimension and frequency_dimension for clarity
* Remove print statement
* Fix pipeline test
* Fix pipeline test
* Fix index table
* Fix init
* Fix conversion script
* Rename to ForAudioClassification
* Fix index table
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* add model files etc for MobileNetV2
rename files for MobileNetV1
initial implementation of MobileNetV1
fix conversion script
cleanup
write docs
tweaks
fix conversion script
extract hidden states
fix test cases
make fixup
fixup it all
remove main from doc link
fixes
fix tests
fix up
use google org
fix weird assert
* fixup
* use google organization for checkpoints
* Add DiNAT
* Adds DiNAT + tests
* Minor fixes
* Added HF model
* Add natten to dependencies.
* Cleanup
* Minor fixup
* Reformat
* Optional NATTEN import.
* Reformat & add doc to _toctree
* Reformat (finally)
* Dummy objects for DiNAT
* Add NAT + minor changes
Adds NAT as its own independent model + docs, tests
Adds NATTEN to ext deps to ensure ci picks it up.
* Remove natten from `all` and `dev-torch` deps, add manual pip install to ci tests
* Minor fixes.
* Fix READMEs.
* Requested changes to docs + minor fixes.
* Requested changes.
* Add NAT/DiNAT tests to layoutlm_job
* Correction to Dinat doc.
* Requested changes.
* Add resources of OpenAI GPT
* Delete Deploy section and add .
* Add scripts
* Update docs/source/en/model_doc/openai-gpt.mdx
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Delete causal-language-modeling section
* Add TFOpenAIGPTLMHeadModel
* Add resources from community
* Delete a link
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Adds image-guided object detection method to OwlViTForObjectDetection class as described in the original paper. One-shot/ image-guided object detection enables users to use a query image to search for similar objects in the input image.
Co-Authored-By: Dhruv Karan k4r4n.dhruv@gmail.com
* WIP: Added CLIP resources from HuggingFace blog
* ADD: Notebooks documentation to clip
* Add link straight to notebook
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* Change notebook links to colab
Co-authored-by: Ambuj Pawar <your_email@abc.example>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* allow loading projection in text and vision model
* begin tests
* finish test for CLIPTextModelTest
* style
* add slow tests
* add new classes for projection heads
* remove with_projection
* add in init
* add in doc
* fix tests
* fix some more tests
* fix copies
* fix docs
* remove leftover from fix-copies
* add the head models in IGNORE_NON_AUTO_CONFIGURED
* fix docstr
* fix tests
* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add docstr for models
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
* add model files etc for MobileNetV2
* rename files for MobileNetV1
* initial implementation of MobileNetV1
* fix conversion script
* cleanup
* write docs
* tweaks
* fix conversion script
* extract hidden states
* fix test cases
* make fixup
* fixup it all
* rename V1 to V2
* fix checkpoints
* fixup
* implement first block + weight conversion
* add remaining layers
* add output stride and dilation
* fixup
* add tests
* add deeplabv3+ head
* a bit of fixup
* finish deeplab conversion
* add link to doc
* fix issue with JIT trace
in_height and in_width would be Tensor objects during JIT trace, which caused Core ML conversion to fail on the remainder op. By making them ints, the result of the padding calculation becomes a constant value.
* cleanup
* fix order of models
* fix rebase error
* remove main from doc link
* add image processor
* remove old feature extractor
* fix converter + other issues
* fixup
* fix unit test
* add to onnx tests (but these appear broken now)
* add post_process_semantic_segmentation
* use google org
* remove unused imports
* move args
* replace weird assert
* move generation_*.py src files into generation/*.py
* populate generation.__init__ with lazy loading
* move imports and references from generation.xxx.object to generation.object
* Add first draft
* Update conversion script
* Improve conversion script
* Improve conversion script some more
* Add conditional embeddings
* Add initial decoder
* Fix activation function of decoder
* Make decoder outputs match original implementation
* Make decoder outputs match original implementation
* Add more copied from statements
* Improve model outputs
* Fix auto tokenizer file
* Fix more tests
* Add test
* Improve README and docs, improve conditional embeddings
* Fix more tests
* Remove print statements
* Remove initial embeddings
* Improve conversion script
* Add interpolation of position embeddings
* Finish addition of interpolation of position embeddings
* Add support for refined checkpoint
* Fix refined checkpoint
* Remove unused parameter
* Improve conversion script
* Add support for training
* Fix conversion script
* Add CLIPSegFeatureExtractor
* Fix processor
* Fix CLIPSegProcessor
* Fix conversion script
* Fix most tests
* Fix equivalence test
* Fix README
* Add model to doc tests
* Use better variable name
* Convert other checkpoint as well
* Update config, add link to paper
* Add docs
* Update organization
* Replace base_model_prefix with clip
* Fix base_model_prefix
* Fix checkpoint of config
* Fix config checkpoint
* Remove file
* Use logits for output
* Fix tests
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>
* docs: Fix typo in ONNX parser help: 'tolerence' => 'tolerance'
* docs: Resolve many typos in the English docs
Typos found via 'codespell ./docs/source/en'
* initial commit
* First draft that gets outputs without crashing!
* Add all the ported openfold dependencies
* testing
* Restructure config files for ESMFold
* Debugging to find output discrepancies
* Mainly style
* Make model runnable without extra deps
* Remove utils and merge them to the modeling file
* Use correct gelu and remove some debug prints
* More cleanup
* Update esm docs
* Update conversion script to support ESMFold properly
* Port some top-level changes from ESMFold repo
* Expand EsmFold docstrings
* Make attention_mask optional (default to all 1s)
* Add inference test for ESMFold
* Use config and not n kwargs
* Add modeling output class
* Remove einops
* Remove chunking in ESM FFN
* Update tests for ESMFold
* Quality
* REpo consistency
* Remove tree dependency from ESMFold
* make fixup
* Add an error in case my structure map function breaks later
* Remove needless code
* Stop auto-casting the LM to float16 so CPU tests pass
* Stop auto-casting the LM to float16 so CPU tests pass
* Final test updates
* Split test file
* Copyright and quality
* Unpin PyTorch to see built doc
* Fix config file to_dict() method
* Add some docstrings to the output
* Skip TF checkpoint tests for ESM until we reupload those
* make fixup
* More docstrings
* Unpin to get even with main
* Flag example to write
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>
* Partial TF port for ESM model
* Add ESM-TF tests
* Add the various imports for TF-ESM
* TF weight conversion almost ready
* Stop ignoring the decoder weights in PT
* Add tests and lots of fixes
* fix-copies
* Fix imports, add model docs
* Add get_vocab() to tokenizer
* Fix vocab links for pretrained files
* Allow multiple inputs with a sep
* Use EOS as SEP token because ESM vocab lacks SEP
* Correctly return special tokens mask from ESM tokenizer
* make fixup
* Stop testing unsupported embedding resizing
* Handle TF bias correctly
* Skip all models with slow tokenizers in the token classification test
* Fixing the batch/unbatcher of pipelines to accomodate the `None` being
passed around.
* Fixing pipeline bug caused by slow tokenizer being different.
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update src/transformers/models/esm/modeling_tf_esm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update set_input_embeddings and the copyright notices
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Add initial files for depth estimation pipelines
* Add test file for depth estimation pipeline
* Update model mapping names
* Add updates for depth estimation output
* Add generic test
* Hopefully fixing the tests.
* Check if test passes
* Add make fixup and make fix-copies changes after rebase with main
* Rebase with main
* Fixing up depth pipeline.
* This is not used anymore.
* Fixing the test. `Image` is a module `Image.Image` is the type.
* Update docs/source/en/main_classes/pipelines.mdx
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* First draft
* Fix more things
* Improve more things
* Remove some head models
* Fix more things
* Add missing layers
* Remove tokenizer
* Fix more things
* Fix copied from statements
* Make all tests pass
* Remove print statements
* Remove files
* Fix README and docs
* Add integration test and fix organization
* Add tips
* Apply suggestions from code review
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* Make tests faster, improve docs
* Fix doc tests
* Add model to toctree
* Add docs
* Add note about creating new checkpoint
* Remove is_decoder
* Make tests smaller, add docs
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
* implemented TFCvtModel and TFCvtForImageClassification and modified relevant files, added an exception in convert_tf_weight_name_to_pt_weight_name, added quick testing file to compare with pytorch model
* added docstring + testing file in transformers testing suite
* added test in testing file, modified docs to pass repo-consistency, passed formatting test
* refactoring + passing all test
* small refacto, removing unwanted comments
* improved testing config
* corrected import error
* modified acces to pretrained model archive list, to pass tf_test
* corrected import structure in init files
* modified testing for keras_fit with cpu
* correcting PR issues + Refactoring
* Refactoring : improving readability and reducing the number of permutations
* corrected momentum value + cls_token initialization
* removed from_pt as weights were added to the hub
* Update tests/models/cvt/test_modeling_tf_cvt.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Add `OPTForQuestionAnswering`
- added `OPTForQuestionAnswering` class based on `BloomForQuestionAnswering`
- added `OPTForQuestionAnswering` in common tests
- all common tests pass
- make fixup done
* added docstrings for OPTForQuestionAnswering
* Fix docstrings for OPTForQuestionAnswering
* Add ZeroShotObjectDetectionPipeline (#18445)
* Add AutoModelForZeroShotObjectDetection task
This commit also adds the following
- Add explicit _processor method for ZeroShotObjectDetectionPipeline.
This is necessary as pipelines don't auto infer processors yet and
`OwlVitProcessor` wraps tokenizer and feature_extractor together, to
process multiple images at once
- Add auto tests and other tests for ZeroShotObjectDetectionPipeline
* Add AutoModelForZeroShotObjectDetection task
This commit also adds the following
- Add explicit _processor method for ZeroShotObjectDetectionPipeline.
This is necessary as pipelines don't auto infer processors yet and
`OwlVitProcessor` wraps tokenizer and feature_extractor together, to
process multiple images at once
- Add auto tests and other tests for ZeroShotObjectDetectionPipeline
* Add batching for ZeroShotObjectDetectionPipeline
* Fix doc-string ZeroShotObjectDetectionPipeline
* Fix output format: ZeroShotObjectDetectionPipeline
