tests: fix pytorch tensor placement errors #33485
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This ci failure does not seem to be relevant, I did not touch this part of the code: |
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Hi @dvrogozh, thanks for opening a PR! Could you provide some more context around this issue, specifically linking to a related github issue or providing a minimal reproducible code snippet for the error? |
@amyeroberts : see: |
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For the failing examples tests - I believe this has been fixed upstream. Could you rebase on main -- this should make everything green and ready to merge :) |
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Rebased. It seems there are still some issues running tests from main branch.
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Considering that we did not see these tests on my prev. merge base, that's regression. Indeed: First bad commit 78b2929, after merge of: Filed: to track this. @avishaiElmakies : fyi. |
Hm. I reproduced this issue on main without my PR. But I could not find a revision in history which works. I still see same error. I wonder, was one of the packages we depend upon updated behind the scenes so this test got affected? Filed #33650 to track. |
Ok, waiting once this will get resolved on infrastructure side... |
This commit fixes the following errors: * Fix "expected all tensors to be on the same device" error * Fix "can't convert device type tensor to numpy" According to pytorch documentation torch.Tensor.numpy(force=False) performs conversion only if tensor is on CPU (plus few other restrictions) which is not the case. For our case we need force=True since we just need a data and don't care about tensors coherency. Fixes: huggingface#33517 See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
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@amyeroberts : ci passed now. No changes in PR, just rebasing to get fixes from main branch. |
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@dvrogozh Great, thanks for updating! |
avishaiElmakies
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Sep 25, 2024
This commit fixes the following errors: * Fix "expected all tensors to be on the same device" error * Fix "can't convert device type tensor to numpy" According to pytorch documentation torch.Tensor.numpy(force=False) performs conversion only if tensor is on CPU (plus few other restrictions) which is not the case. For our case we need force=True since we just need a data and don't care about tensors coherency. Fixes: huggingface#33517 See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
amyeroberts
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Oct 2, 2024
This commit fixes the following errors: * Fix "expected all tensors to be on the same device" error * Fix "can't convert device type tensor to numpy" According to pytorch documentation torch.Tensor.numpy(force=False) performs conversion only if tensor is on CPU (plus few other restrictions) which is not the case. For our case we need force=True since we just need a data and don't care about tensors coherency. Fixes: huggingface#33517 See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
ArthurZucker
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Oct 10, 2024
* add sdpa to OPT * chore: remove redundant whitespace in OPTDecoder class * fixup * bug fix * add sdpa and attention generate test * fixup * Refactor OPTAttention forward method for improved readability and maintainability * undo refactor for _shape and key,val states * add OPT to doc, fixup didn't find it for some reason * change order * change default attn_implemntation in testing to eager * [run-slow] opt * change test_eager_matches_sdpa_generate to the one llama * Update default attention implementation in testing common * [run-slow] opt * remove uneeded print * [run-slow] opt * refactor model testers to have attn_implementation="eager" * [run-slow] opt * convert test_eager_matches_sdpa_generate to opt-350M * bug fix when creating mask for opt * [run-slow] opt * if layer head mask default to eager * if head mask is not none fall to eager * [run-slow] opt * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Clean up Unpack imports (#33631) clean up Unpack imports * Fix DPT /Dinov2 sdpa regression on main (#33660) * fallback to eager if output attentions. * fix copies * handle dependency errors in check_imports (#33622) * handle dependency errors in check_imports * change log level to warning * add back self.max_position_embeddings = config.max_position_embeddings (#33550) * add back self.max_position_embeddings = config.max_position_embeddings * fix-copies * Fix Llava conversion for LlavaQwen2ForCausalLM with Clip vision tower (#33613) fix llavaqwen2 model conversion * Uniformize kwargs for Udop processor and update docs (#33628) * Add optional kwargs and uniformize udop * cleanup Unpack * nit Udop * Generation: deprecate `PreTrainedModel` inheriting from `GenerationMixin` (#33203) * Enable BNB multi-backend support (#31098) * enable cpu bnb path * fix style * fix code style * fix 4 bit path * Update src/transformers/utils/import_utils.py Co-authored-by: Aarni Koskela <akx@iki.fi> * add multi backend refactor tests * fix style * tweak 4bit quantizer + fix corresponding tests * tweak 8bit quantizer + *try* fixing corresponding tests * fix dequant bnb 8bit * account for Intel CPU in variability of expected outputs * enable cpu and xpu device map * further tweaks to account for Intel CPU * fix autocast to work with both cpu + cuda * fix comments * fix comments * switch to testing_utils.torch_device * allow for xpu in multi-gpu tests * fix tests 4bit for CPU NF4 * fix bug with is_torch_xpu_available needing to be called as func * avoid issue where test reports attr err due to other failure * fix formatting * fix typo from resolving of merge conflict * polish based on last PR review Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * fix CI * Update src/transformers/integrations/integration_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/integrations/integration_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix error log * fix error msg * add \n in error log * make quality * rm bnb cuda restriction in doc * cpu model don't need dispatch * fix doc * fix style * check cuda avaliable in testing * fix tests * Update docs/source/en/model_doc/chameleon.md Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * Update docs/source/en/model_doc/llava_next.md Co-authored-by: Aarni Koskela <akx@iki.fi> * Update tests/quantization/bnb/test_4bit.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update tests/quantization/bnb/test_4bit.py Co-authored-by: Aarni Koskela <akx@iki.fi> * fix doc * fix check multibackends * fix import sort * remove check torch in bnb * docs: update bitsandbytes references with multi-backend info * docs: fix small mistakes in bnb paragraph * run formatting * reveret bnb check * move bnb multi-backend check to import_utils * Update src/transformers/utils/import_utils.py Co-authored-by: Aarni Koskela <akx@iki.fi> * fix bnb check * minor fix for bnb * check lib first * fix code style * Revert "run formatting" This reverts commit ac108c6. * fix format * give warning when bnb version is low and no cuda found] * fix device assignment check to be multi-device capable * address akx feedback on get_avlbl_dev fn * revert partially, as we don't want the function that public, as docs would be too much (enforced) --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com> Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Fix error string after refactoring into get_chat_template (#33652) * Fix error string after refactoring into get_chat_template * Take suggestion from CR Co-authored-by: Matt <Rocketknight1@users.noreply.github.com> --------- Co-authored-by: Matt <Rocketknight1@users.noreply.github.com> * uniformize git processor (#33668) * uniformize git processor * update doctring * Modular `transformers`: modularity and inheritance for new model additions (#33248) * update exampel * update * push the converted diff files for testing and ci * correct one example * fix class attributes and docstring * nits * oups * fixed config! * update * nitd * class attributes are not matched against the other, this is missing * fixed overwriting self.xxx now onto the attributes I think * partial fix, now order with docstring * fix docstring order? * more fixes * update * fix missing docstrings! * examples don't all work yet * fixup * nit * updated * hick * update * delete * update * update * update * fix * all default * no local import * fix more diff * some fix related to "safe imports" * push fixed * add helper! * style * add a check * all by default * add the * update * FINALLY! * nit * fix config dependencies * man that is it * fix fix * update diffs * fix the last issue * re-default to all * alll the fixes * nice * fix properties vs setter * fixup * updates * update dependencies * make sure to install what needs to be installed * fixup * quick fix for now * fix! * fixup * update * update * updates * whitespaces * nit * fix * simplify everything, and make it file agnostic (should work for image processors) * style * finish fixing all import issues * fixup * empty modeling should not be written! * Add logic to find who depends on what * update * cleanup * update * update gemma to support positions * some small nits * this is the correct docstring for gemma2 * fix merging of docstrings * update * fixup * update * take doc into account * styling * update * fix hidden activation * more fixes * final fixes! * fixup * fixup instruct blip video * update * fix bugs * align gemma2 with the rest as well * updats * revert * update * more reversiom * grind * more * arf * update * order will matter * finish del stuff * update * rename to modular * fixup * nits * update makefile * fixup * update order of the checks! * fix * fix docstring that has a call inside * fiix conversion check * style * add some initial documentation * update * update doc * some fixup * updates * yups * Mostly todo gimme a minut * update * fixup * revert some stuff * Review docs for the modular transformers (#33472) Docs * good update * fixup * mmm current updates lead to this code * okay, this fixes it * cool * fixes * update * nit * updates * nits * fix doc * update * revert bad changes * update * updates * proper update * update * update? * up * update * cool * nits * nits * bon bon * fix * ? * minimise changes * update * update * update * updates? * fixed gemma2 * kind of a hack * nits * update * remove `diffs` in favor of `modular` * fix make fix copies --------- Co-authored-by: Lysandre Debut <hi@lysand.re> * Fix CIs post merging modular transformers (#33681) update * Fixed docstring for cohere model regarding unavailability of prune_he… (#33253) * Fixed docstring for cohere model regarding unavailability of prune_head() methods The docstring mentions that cohere model supports prune_heads() methods. I have fixed the docstring by explicitly mentioning that it doesn't support that functionality. * Update src/transformers/models/cohere/modeling_cohere.py --------- Co-authored-by: Lysandre Debut <hi@lysand.re> * Generation tests: update imagegpt input name, remove unused functions (#33663) * Improve Error Messaging for Flash Attention 2 on CPU (#33655) Update flash-attn error message on CPU Rebased to latest branch * Gemma2: fix config initialization (`cache_implementation`) (#33684) * Fix ByteLevel alphabet missing when Sequence pretokenizer is used (#33556) * Fix ByteLevel alphabet missing when Sequence pretokenizer is used * Fixed formatting with `ruff`. * Uniformize kwargs for image-text-to-text processors (#32544) * uniformize FUYU processor kwargs * Uniformize instructblip processor kwargs * Fix processor kwargs and tests Fuyu, InstructBlip, Kosmos2 * Uniformize llava_next processor * Fix save_load test for processor with chat_template only as extra init args * Fix import Unpack * Fix Fuyu Processor import * Fix FuyuProcessor import * Fix FuyuProcessor * Add defaults for specific kwargs kosmos2 * Fix Udop to return BatchFeature instead of BatchEncoding and uniformize kwargs * Add tests processor Udop * remove Copied from in processing Udop as change of input orders caused by BatchEncoding -> BatchFeature * Fix overwrite tests kwargs processors * Add warnings and BC for changes in processor inputs order, change docs, add BC for text_pair as arg for Udop * Fix processing test fuyu * remove unnecessary pad_token check in instructblip ProcessorTest * Fix BC tests and cleanup * FIx imports fuyu * Uniformize Pix2Struct * Fix wrong name for FuyuProcessorKwargs * Fix slow tests reversed inputs align fuyu llava-next, change udop warning * Fix wrong logging import udop * Add check images text input order * Fix copies * change text pair handling when positional arg * rebase on main, fix imports in test_processing_common * remove optional args and udop uniformization from this PR * fix failing tests * remove unnecessary test, fix processing utils and test processing common * cleanup Unpack * cleanup * fix conflict grounding dino * 🚨🚨 Setting default behavior of assisted decoding (#33657) * tests: fix pytorch tensor placement errors (#33485) This commit fixes the following errors: * Fix "expected all tensors to be on the same device" error * Fix "can't convert device type tensor to numpy" According to pytorch documentation torch.Tensor.numpy(force=False) performs conversion only if tensor is on CPU (plus few other restrictions) which is not the case. For our case we need force=True since we just need a data and don't care about tensors coherency. Fixes: #33517 See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com> * bump tokenizers, fix added tokens fast (#32535) * update based on tokenizers release * update * nits * update * revert re addition * don't break that yet * fmt * revert unwanted * update tokenizers version * update dep table * update * update in conversion script as well * some fix * revert * fully revert * fix training * remove set trace * fixup * update * update * [Pixtral] Improve docs, rename model (#33491) * Improve docs, rename model * Fix style * Update repo id * fix code quality after merge * HFQuantizer implementation for compressed-tensors library (#31704) * Add compressed-tensors HFQuantizer implementation * flag serializable as False * run * revive lines deleted by ruff * fixes to load+save from sparseml, edit config to quantization_config, and load back * address satrat comment * compressed_tensors to compressed-tensors and revert back is_serializable * rename quant_method from sparseml to compressed-tensors * tests * edit tests * clean up tests * make style * cleanup * cleanup * add test skip for when compressed tensors is not installed * remove pydantic import + style * delay torch import in test * initial docs * update main init for compressed tensors config * make fix-copies * docstring * remove fill_docstring * Apply suggestions from code review Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * review comments * review comments * comments - suppress warnings on state dict load, tests, fixes * bug-fix - remove unnecessary call to apply quant lifecycle * run_compressed compatability * revert changes not needed for compression * no longer need unexpected keys fn * unexpected keys not needed either * Apply suggestions from code review Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * add to_diff_dict * update docs and expand testing * Update _toctree.yml with compressed-tensors * Update src/transformers/utils/quantization_config.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * update doc * add note about saving a loaded model --------- Co-authored-by: George Ohashi <george@neuralmagic.com> Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: Sara Adkins <sara@neuralmagic.com> Co-authored-by: Sara Adkins <sara.adkins65@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Dipika Sikka <ds3822@columbia.edu> Co-authored-by: Dipika <dipikasikka1@gmail.com> * update model card for opt * add batch size to inference table * [slow-run] opt * [run-slow] opt --------- Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com> Co-authored-by: Avishai Elmakies <avishai.elma@cs.huji.ac.il> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com> Co-authored-by: chengchengpei <5881383+chengchengpei@users.noreply.github.com> Co-authored-by: Isotr0py <2037008807@qq.com> Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> Co-authored-by: jiqing-feng <jiqing.feng@intel.com> Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com> Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Tibor Reiss <75096465+tibor-reiss@users.noreply.github.com> Co-authored-by: Matt <Rocketknight1@users.noreply.github.com> Co-authored-by: Lysandre Debut <hi@lysand.re> Co-authored-by: Muhammad Naufil <m.naufil1@gmail.com> Co-authored-by: sizhky <yyeshr@gmail.com> Co-authored-by: Umar Butler <umar@umar.au> Co-authored-by: Jonathan Mamou <jonathan.mamou@intel.com> Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: Benjamin Fineran <bfineran@users.noreply.github.com> Co-authored-by: George Ohashi <george@neuralmagic.com> Co-authored-by: Sara Adkins <sara@neuralmagic.com> Co-authored-by: Sara Adkins <sara.adkins65@gmail.com> Co-authored-by: Dipika Sikka <ds3822@columbia.edu> Co-authored-by: Dipika <dipikasikka1@gmail.com>
NielsRogge
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Oct 21, 2024
* add sdpa to OPT * chore: remove redundant whitespace in OPTDecoder class * fixup * bug fix * add sdpa and attention generate test * fixup * Refactor OPTAttention forward method for improved readability and maintainability * undo refactor for _shape and key,val states * add OPT to doc, fixup didn't find it for some reason * change order * change default attn_implemntation in testing to eager * [run-slow] opt * change test_eager_matches_sdpa_generate to the one llama * Update default attention implementation in testing common * [run-slow] opt * remove uneeded print * [run-slow] opt * refactor model testers to have attn_implementation="eager" * [run-slow] opt * convert test_eager_matches_sdpa_generate to opt-350M * bug fix when creating mask for opt * [run-slow] opt * if layer head mask default to eager * if head mask is not none fall to eager * [run-slow] opt * Update src/transformers/models/opt/modeling_opt.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Clean up Unpack imports (huggingface#33631) clean up Unpack imports * Fix DPT /Dinov2 sdpa regression on main (huggingface#33660) * fallback to eager if output attentions. * fix copies * handle dependency errors in check_imports (huggingface#33622) * handle dependency errors in check_imports * change log level to warning * add back self.max_position_embeddings = config.max_position_embeddings (huggingface#33550) * add back self.max_position_embeddings = config.max_position_embeddings * fix-copies * Fix Llava conversion for LlavaQwen2ForCausalLM with Clip vision tower (huggingface#33613) fix llavaqwen2 model conversion * Uniformize kwargs for Udop processor and update docs (huggingface#33628) * Add optional kwargs and uniformize udop * cleanup Unpack * nit Udop * Generation: deprecate `PreTrainedModel` inheriting from `GenerationMixin` (huggingface#33203) * Enable BNB multi-backend support (huggingface#31098) * enable cpu bnb path * fix style * fix code style * fix 4 bit path * Update src/transformers/utils/import_utils.py Co-authored-by: Aarni Koskela <akx@iki.fi> * add multi backend refactor tests * fix style * tweak 4bit quantizer + fix corresponding tests * tweak 8bit quantizer + *try* fixing corresponding tests * fix dequant bnb 8bit * account for Intel CPU in variability of expected outputs * enable cpu and xpu device map * further tweaks to account for Intel CPU * fix autocast to work with both cpu + cuda * fix comments * fix comments * switch to testing_utils.torch_device * allow for xpu in multi-gpu tests * fix tests 4bit for CPU NF4 * fix bug with is_torch_xpu_available needing to be called as func * avoid issue where test reports attr err due to other failure * fix formatting * fix typo from resolving of merge conflict * polish based on last PR review Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * fix CI * Update src/transformers/integrations/integration_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/integrations/integration_utils.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix error log * fix error msg * add \n in error log * make quality * rm bnb cuda restriction in doc * cpu model don't need dispatch * fix doc * fix style * check cuda avaliable in testing * fix tests * Update docs/source/en/model_doc/chameleon.md Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * Update docs/source/en/model_doc/llava_next.md Co-authored-by: Aarni Koskela <akx@iki.fi> * Update tests/quantization/bnb/test_4bit.py Co-authored-by: Aarni Koskela <akx@iki.fi> * Update tests/quantization/bnb/test_4bit.py Co-authored-by: Aarni Koskela <akx@iki.fi> * fix doc * fix check multibackends * fix import sort * remove check torch in bnb * docs: update bitsandbytes references with multi-backend info * docs: fix small mistakes in bnb paragraph * run formatting * reveret bnb check * move bnb multi-backend check to import_utils * Update src/transformers/utils/import_utils.py Co-authored-by: Aarni Koskela <akx@iki.fi> * fix bnb check * minor fix for bnb * check lib first * fix code style * Revert "run formatting" This reverts commit ac108c6. * fix format * give warning when bnb version is low and no cuda found] * fix device assignment check to be multi-device capable * address akx feedback on get_avlbl_dev fn * revert partially, as we don't want the function that public, as docs would be too much (enforced) --------- Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com> Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Fix error string after refactoring into get_chat_template (huggingface#33652) * Fix error string after refactoring into get_chat_template * Take suggestion from CR Co-authored-by: Matt <Rocketknight1@users.noreply.github.com> --------- Co-authored-by: Matt <Rocketknight1@users.noreply.github.com> * uniformize git processor (huggingface#33668) * uniformize git processor * update doctring * Modular `transformers`: modularity and inheritance for new model additions (huggingface#33248) * update exampel * update * push the converted diff files for testing and ci * correct one example * fix class attributes and docstring * nits * oups * fixed config! * update * nitd * class attributes are not matched against the other, this is missing * fixed overwriting self.xxx now onto the attributes I think * partial fix, now order with docstring * fix docstring order? * more fixes * update * fix missing docstrings! * examples don't all work yet * fixup * nit * updated * hick * update * delete * update * update * update * fix * all default * no local import * fix more diff * some fix related to "safe imports" * push fixed * add helper! * style * add a check * all by default * add the * update * FINALLY! * nit * fix config dependencies * man that is it * fix fix * update diffs * fix the last issue * re-default to all * alll the fixes * nice * fix properties vs setter * fixup * updates * update dependencies * make sure to install what needs to be installed * fixup * quick fix for now * fix! * fixup * update * update * updates * whitespaces * nit * fix * simplify everything, and make it file agnostic (should work for image processors) * style * finish fixing all import issues * fixup * empty modeling should not be written! * Add logic to find who depends on what * update * cleanup * update * update gemma to support positions * some small nits * this is the correct docstring for gemma2 * fix merging of docstrings * update * fixup * update * take doc into account * styling * update * fix hidden activation * more fixes * final fixes! * fixup * fixup instruct blip video * update * fix bugs * align gemma2 with the rest as well * updats * revert * update * more reversiom * grind * more * arf * update * order will matter * finish del stuff * update * rename to modular * fixup * nits * update makefile * fixup * update order of the checks! * fix * fix docstring that has a call inside * fiix conversion check * style * add some initial documentation * update * update doc * some fixup * updates * yups * Mostly todo gimme a minut * update * fixup * revert some stuff * Review docs for the modular transformers (huggingface#33472) Docs * good update * fixup * mmm current updates lead to this code * okay, this fixes it * cool * fixes * update * nit * updates * nits * fix doc * update * revert bad changes * update * updates * proper update * update * update? * up * update * cool * nits * nits * bon bon * fix * ? * minimise changes * update * update * update * updates? * fixed gemma2 * kind of a hack * nits * update * remove `diffs` in favor of `modular` * fix make fix copies --------- Co-authored-by: Lysandre Debut <hi@lysand.re> * Fix CIs post merging modular transformers (huggingface#33681) update * Fixed docstring for cohere model regarding unavailability of prune_he… (huggingface#33253) * Fixed docstring for cohere model regarding unavailability of prune_head() methods The docstring mentions that cohere model supports prune_heads() methods. I have fixed the docstring by explicitly mentioning that it doesn't support that functionality. * Update src/transformers/models/cohere/modeling_cohere.py --------- Co-authored-by: Lysandre Debut <hi@lysand.re> * Generation tests: update imagegpt input name, remove unused functions (huggingface#33663) * Improve Error Messaging for Flash Attention 2 on CPU (huggingface#33655) Update flash-attn error message on CPU Rebased to latest branch * Gemma2: fix config initialization (`cache_implementation`) (huggingface#33684) * Fix ByteLevel alphabet missing when Sequence pretokenizer is used (huggingface#33556) * Fix ByteLevel alphabet missing when Sequence pretokenizer is used * Fixed formatting with `ruff`. * Uniformize kwargs for image-text-to-text processors (huggingface#32544) * uniformize FUYU processor kwargs * Uniformize instructblip processor kwargs * Fix processor kwargs and tests Fuyu, InstructBlip, Kosmos2 * Uniformize llava_next processor * Fix save_load test for processor with chat_template only as extra init args * Fix import Unpack * Fix Fuyu Processor import * Fix FuyuProcessor import * Fix FuyuProcessor * Add defaults for specific kwargs kosmos2 * Fix Udop to return BatchFeature instead of BatchEncoding and uniformize kwargs * Add tests processor Udop * remove Copied from in processing Udop as change of input orders caused by BatchEncoding -> BatchFeature * Fix overwrite tests kwargs processors * Add warnings and BC for changes in processor inputs order, change docs, add BC for text_pair as arg for Udop * Fix processing test fuyu * remove unnecessary pad_token check in instructblip ProcessorTest * Fix BC tests and cleanup * FIx imports fuyu * Uniformize Pix2Struct * Fix wrong name for FuyuProcessorKwargs * Fix slow tests reversed inputs align fuyu llava-next, change udop warning * Fix wrong logging import udop * Add check images text input order * Fix copies * change text pair handling when positional arg * rebase on main, fix imports in test_processing_common * remove optional args and udop uniformization from this PR * fix failing tests * remove unnecessary test, fix processing utils and test processing common * cleanup Unpack * cleanup * fix conflict grounding dino * 🚨🚨 Setting default behavior of assisted decoding (huggingface#33657) * tests: fix pytorch tensor placement errors (huggingface#33485) This commit fixes the following errors: * Fix "expected all tensors to be on the same device" error * Fix "can't convert device type tensor to numpy" According to pytorch documentation torch.Tensor.numpy(force=False) performs conversion only if tensor is on CPU (plus few other restrictions) which is not the case. For our case we need force=True since we just need a data and don't care about tensors coherency. Fixes: huggingface#33517 See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com> * bump tokenizers, fix added tokens fast (huggingface#32535) * update based on tokenizers release * update * nits * update * revert re addition * don't break that yet * fmt * revert unwanted * update tokenizers version * update dep table * update * update in conversion script as well * some fix * revert * fully revert * fix training * remove set trace * fixup * update * update * [Pixtral] Improve docs, rename model (huggingface#33491) * Improve docs, rename model * Fix style * Update repo id * fix code quality after merge * HFQuantizer implementation for compressed-tensors library (huggingface#31704) * Add compressed-tensors HFQuantizer implementation * flag serializable as False * run * revive lines deleted by ruff * fixes to load+save from sparseml, edit config to quantization_config, and load back * address satrat comment * compressed_tensors to compressed-tensors and revert back is_serializable * rename quant_method from sparseml to compressed-tensors * tests * edit tests * clean up tests * make style * cleanup * cleanup * add test skip for when compressed tensors is not installed * remove pydantic import + style * delay torch import in test * initial docs * update main init for compressed tensors config * make fix-copies * docstring * remove fill_docstring * Apply suggestions from code review Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * review comments * review comments * comments - suppress warnings on state dict load, tests, fixes * bug-fix - remove unnecessary call to apply quant lifecycle * run_compressed compatability * revert changes not needed for compression * no longer need unexpected keys fn * unexpected keys not needed either * Apply suggestions from code review Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * add to_diff_dict * update docs and expand testing * Update _toctree.yml with compressed-tensors * Update src/transformers/utils/quantization_config.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * update doc * add note about saving a loaded model --------- Co-authored-by: George Ohashi <george@neuralmagic.com> Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: Sara Adkins <sara@neuralmagic.com> Co-authored-by: Sara Adkins <sara.adkins65@gmail.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Dipika Sikka <ds3822@columbia.edu> Co-authored-by: Dipika <dipikasikka1@gmail.com> * update model card for opt * add batch size to inference table * [slow-run] opt * [run-slow] opt --------- Signed-off-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com> Co-authored-by: Avishai Elmakies <avishai.elma@cs.huji.ac.il> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com> Co-authored-by: chengchengpei <5881383+chengchengpei@users.noreply.github.com> Co-authored-by: Isotr0py <2037008807@qq.com> Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> Co-authored-by: jiqing-feng <jiqing.feng@intel.com> Co-authored-by: Aarni Koskela <akx@iki.fi> Co-authored-by: Titus von Koeller <9048635+Titus-von-Koeller@users.noreply.github.com> Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Tibor Reiss <75096465+tibor-reiss@users.noreply.github.com> Co-authored-by: Matt <Rocketknight1@users.noreply.github.com> Co-authored-by: Lysandre Debut <hi@lysand.re> Co-authored-by: Muhammad Naufil <m.naufil1@gmail.com> Co-authored-by: sizhky <yyeshr@gmail.com> Co-authored-by: Umar Butler <umar@umar.au> Co-authored-by: Jonathan Mamou <jonathan.mamou@intel.com> Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: Benjamin Fineran <bfineran@users.noreply.github.com> Co-authored-by: George Ohashi <george@neuralmagic.com> Co-authored-by: Sara Adkins <sara@neuralmagic.com> Co-authored-by: Sara Adkins <sara.adkins65@gmail.com> Co-authored-by: Dipika Sikka <ds3822@columbia.edu> Co-authored-by: Dipika <dipikasikka1@gmail.com>
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This commit fixes the following errors:
And few other variants of above where model or inputs are not on the right device.
According to pytorch documentation torch.Tensor.numpy(force=False) performs conversion only if tensor is on CPU (plus few other restrictions) which is not the case. For our case we need force=True since we just need a data and don't care about tensors coherency.
Fixes: #33517
See: https://pytorch.org/docs/2.4/generated/torch.Tensor.numpy.html
CC: @sanchit-gandhi, @amyeroberts