add sdpa to OPT (#33298)
* 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 ac108c6d6b34f45a5745a736ba57282405cfaa61. * 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>
This commit is contained in:
Avishai Elmakies
@@ -110,6 +110,73 @@ Below is an expected speedup diagram that compares pure inference time between t
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</div>
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### Using Scaled Dot Product Attention (SDPA)
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PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
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encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
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[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
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or the [GPU Inference](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention)
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page for more information.
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SDPA is used by default for `torch>=2.1.1` when an implementation is available, but you may also set
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`attn_implementation="sdpa"` in `from_pretrained()` to explicitly request SDPA to be used.
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```python
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from transformers import OPTForCausalLM
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model = OPTForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16, attn_implementation="sdpa")
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...
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```
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For the best speedups, we recommend loading the model in half-precision (e.g. `torch.float16` or `torch.bfloat16`).
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On a local benchmark (L40S-45GB, PyTorch 2.4.0, OS Debian GNU/Linux 11) using `float16` with
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[facebook/opt-350m](https://huggingface.co/facebook/opt-350m), we saw the
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following speedups during training and inference.
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### Training
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| batch_size | seq_len | Time per batch (eager - s) | Time per batch (sdpa - s) | Speedup (%) | Eager peak mem (MB) | sdpa peak mem (MB) | Mem saving (%) |
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|--------------:|-----------:|:------------------------------|-----------------------------:|:---------------|:-----------------------|----------------------:|:------------------|
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| 1 | 128 | 0.047 | 0.037 | 26.360 | 1474.611 | 1474.32 | 0.019 |
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| 1 | 256 | 0.046 | 0.037 | 24.335 | 1498.541 | 1499.49 | -0.063 |
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| 1 | 512 | 0.046 | 0.037 | 24.959 | 1973.544 | 1551.35 | 27.215 |
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| 1 | 1024 | 0.062 | 0.038 | 65.135 | 4867.113 | 1698.35 | 186.578 |
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| 1 | 2048 | 0.230 | 0.039 | 483.933 | 15662.224 | 2715.75 | 476.718 |
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| 2 | 128 | 0.045 | 0.037 | 20.455 | 1498.164 | 1499.49 | -0.089 |
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| 2 | 256 | 0.046 | 0.037 | 24.027 | 1569.367 | 1551.35 | 1.161 |
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| 2 | 512 | 0.045 | 0.037 | 20.965 | 3257.074 | 1698.35 | 91.778 |
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| 2 | 1024 | 0.122 | 0.038 | 225.958 | 9054.405 | 2715.75 | 233.403 |
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| 2 | 2048 | 0.464 | 0.067 | 593.646 | 30572.058 | 4750.55 | 543.548 |
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| 4 | 128 | 0.045 | 0.037 | 21.918 | 1549.448 | 1551.35 | -0.123 |
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| 4 | 256 | 0.044 | 0.038 | 18.084 | 2451.768 | 1698.35 | 44.361 |
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| 4 | 512 | 0.069 | 0.037 | 84.421 | 5833.180 | 2715.75 | 114.791 |
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| 4 | 1024 | 0.262 | 0.062 | 319.475 | 17427.842 | 4750.55 | 266.860 |
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| 4 | 2048 | OOM | 0.062 | Eager OOM | OOM | 4750.55 | Eager OOM |
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| 8 | 128 | 0.044 | 0.037 | 18.436 | 2049.115 | 1697.78 | 20.694 |
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| 8 | 256 | 0.048 | 0.036 | 32.887 | 4222.567 | 2715.75 | 55.484 |
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| 8 | 512 | 0.153 | 0.06 | 154.862 | 10985.391 | 4750.55 | 131.245 |
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| 8 | 1024 | 0.526 | 0.122 | 330.697 | 34175.763 | 8821.18 | 287.428 |
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| 8 | 2048 | OOM | 0.122 | Eager OOM | OOM | 8821.18 | Eager OOM |
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### Inference
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| batch_size | seq_len | Per token latency eager (ms) | Per token latency SDPA (ms) | Speedup (%) | Mem eager (MB) | Mem BT (MB) | Mem saved (%) |
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|--------------:|-----------:|--------------------------------:|-------------------------------:|---------------:|------------------:|---------------:|-----------------:|
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| 1 | 128 | 11.634 | 8.647 | 34.546 | 717.676 | 717.674 | 0 |
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| 1 | 256 | 11.593 | 8.86 | 30.851 | 742.852 | 742.845 | 0.001 |
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| 1 | 512 | 11.515 | 8.816 | 30.614 | 798.232 | 799.593 | -0.17 |
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| 1 | 1024 | 11.556 | 8.915 | 29.628 | 917.265 | 895.538 | 2.426 |
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| 2 | 128 | 12.724 | 11.002 | 15.659 | 762.434 | 762.431 | 0 |
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| 2 | 256 | 12.704 | 11.063 | 14.83 | 816.809 | 816.733 | 0.009 |
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| 2 | 512 | 12.757 | 10.947 | 16.535 | 917.383 | 918.339 | -0.104 |
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| 2 | 1024 | 13.018 | 11.018 | 18.147 | 1162.65 | 1114.81 | 4.291 |
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| 4 | 128 | 12.739 | 10.959 | 16.243 | 856.335 | 856.483 | -0.017 |
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| 4 | 256 | 12.718 | 10.837 | 17.355 | 957.298 | 957.674 | -0.039 |
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| 4 | 512 | 12.813 | 10.822 | 18.393 | 1158.44 | 1158.45 | -0.001 |
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| 4 | 1024 | 13.416 | 11.06 | 21.301 | 1653.42 | 1557.19 | 6.18 |
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| 8 | 128 | 12.763 | 10.891 | 17.193 | 1036.13 | 1036.51 | -0.036 |
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| 8 | 256 | 12.89 | 11.104 | 16.085 | 1236.98 | 1236.87 | 0.01 |
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| 8 | 512 | 13.327 | 10.939 | 21.836 | 1642.29 | 1641.78 | 0.031 |
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| 8 | 1024 | 15.181 | 11.175 | 35.848 | 2634.98 | 2443.35 | 7.843 |
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## OPTConfig
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@@ -246,6 +246,7 @@ For now, Transformers supports SDPA inference and training for the following arc
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* [NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)
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* [OLMo](https://huggingface.co/docs/transformers/model_doc/olmo#transformers.OlmoModel)
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* [OLMoE](https://huggingface.co/docs/transformers/model_doc/olmoe#transformers.OlmoeModel)
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* [OPT](https://huggingface.co/docs/transformers/en/model_doc/opt)
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* [PaliGemma](https://huggingface.co/docs/transformers/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration)
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* [Phi](https://huggingface.co/docs/transformers/model_doc/phi#transformers.PhiModel)
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* [Phi3](https://huggingface.co/docs/transformers/model_doc/phi3#transformers.Phi3Model)
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