* fix XPU UT error case brough by RNG difference btw XPU and CUDA
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* enable tests/models/llama/test_modeling_llama.py::LlamaIntegrationTest::test_model_7b_logits and tests/models/llama/test_modeling_llama.py::LlamaIntegrationTest::test_model_7b_logits_bf16 on xpu
Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* Revert "enable tests/models/llama/test_modeling_llama.py::LlamaIntegrationTest::test_model_7b_logits and tests/models/llama/test_modeling_llama.py::LlamaIntegrationTest::test_model_7b_logits_bf16 on xpu"
This reverts commit 3ef83a4f02.
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Signed-off-by: YAO Matrix <matrix.yao@intel.com>
* use torch.testing.assertclose instead to get more details about error in cis
* fix
* style
* test_all
* revert for I bert
* fixes and updates
* more image processing fixes
* more image processors
* fix mamba and co
* style
* less strick
* ok I won't be strict
* skip and be done
* up
* Add SynthIDTextWatermarkLogitsProcessor
* esolving comments.
* Resolving comments.
* esolving commits,
* Improving SynthIDWatermark tests.
* switch to PT version
* detector as pretrained model + style
* update training + style
* rebase
* Update logits_process.py
* Improving SynthIDWatermark tests.
* Shift detector training to wikitext negatives and stabilize with lower learning rate.
* Clean up.
* in for 7B
* cleanup
* upport python 3.8.
* README and final cleanup.
* HF Hub upload and initiaze.
* Update requirements for synthid_text.
* Adding SynthIDTextWatermarkDetector.
* Detector testing.
* Documentation changes.
* Copyrights fix.
* Fix detector api.
* ironing out errors
* ironing out errors
* training checks
* make fixup and make fix-copies
* docstrings and add to docs
* copyright
* BC
* test docstrings
* move import
* protect type hints
* top level imports
* watermarking example
* direct imports
* tpr fpr meaning
* process_kwargs
* SynthIDTextWatermarkingConfig docstring
* assert -> exception
* example updates
* no immutable dict (cant be serialized)
* pack fn
* einsum equivalent
* import order
* fix test on gpu
* add detector example
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Co-authored-by: Sumedh Ghaisas <sumedhg@google.com>
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: sumedhghaisas2 <138781311+sumedhghaisas2@users.noreply.github.com>
Co-authored-by: raushan <raushan@huggingface.co>
* add early stopping logits processor
* black formmated
* indent
* follow method signature
* actual logic
* check for None
* address comments on docstrings and method signature
* add unit test under `LogitsProcessorTest` wip
* unit test passing
* black formatted
* condition per sample
* add to BarkModelIntegrationTests
* wip BarkSemanticModelTest
* rename and add to kwargs handling
* not add to BarkSemanticModelTest
* correct logic and assert last outputs tokens different in test
* doc-builder style
* read from kwargs as well
* assert len of with less than that of without
* ruff
* add back seed and test case
* add original impl default suggestion
* doc-builder
* rename and use softmax
* switch back to LogitsProcessor and update docs wording
* camelCase and spelling and saving compute
* assert strictly less than
* assert less than
* expand test_generate_semantic_early_stop instead
* Fix issues in test_exponential_decay_length_penalty
Fix tests which were broken and add validation of negative scores.
Current test didn't take into account that ExponentialDecayLengthPenalty updates the score inplace, resulting in updates to base tested Tensor.
In addition, the gt assert had empty Tensors due to indexing along the batch dimension.
Test is currently expected to fail to show ExponentialDecayLengthPenalty issues with negative scores
* Fix ExponentialDecayLengthPenalty negative logits issue
In cases where the scores are negative, ExponentialDecayLengthPenalty decreases the score of eos_token_id instead of increasing it.
To fix this issue we compute the penalty of the absolute value and add it to the original score.
* Add examples for ExponentialDecayLengthPenalty
* Fix styling issue in ExponentialDecayLengthPenalty doc
* Apply suggestions from code review
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Style and quality fix
* Fix example outputs
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Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
* Add epsilon- and eta-sampling.
Add epsilon- and eta-sampling, following the official code from https://github.com/john-hewitt/truncation-sampling and adapting to be more configurable, as required by Huggingface transformers.
* Add unit tests for epsilon- and eta-sampling.
* Black: fix code formatting.
* Fix docstring spacing.
* Clean up newlines.
* Fix implementation bugs and their associated tests.
* Remove epsilon- and eta-sampling parameters from PretrainedConfig.
* Clarify and clean up the documentation.
* Remove parameters for PretrainedConfig test.
* 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
