From fe358f4849c005e57328d57a998ab9411e6bbe90 Mon Sep 17 00:00:00 2001 From: yaoyu-33 <54727607+yaoyu-33@users.noreply.github.com> Date: Tue, 16 Jan 2024 14:49:55 -0800 Subject: [PATCH] NeMo Multimodal Docs and Tests Initial PR (#8028) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * [TTS] Fix FastPitch data prep tutorial (#7524) Signed-off-by: Ryan * add italian tokenization (#7486) * add italian tokenization Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add more ipa lexicon it Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix error deletion Signed-off-by: GiacomoLeoneMaria * add test Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: GiacomoLeoneMaria Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Replace None strategy with auto in tutorial notebooks (#7521) (#7527) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * unpin setuptools (#7534) (#7535) Signed-off-by: fayejf <36722593+fayejf@users.noreply.github.com> Co-authored-by: fayejf <36722593+fayejf@users.noreply.github.com> * remove auto generated examples (#7510) * explicitly remove autogenerated examples for data parallel evaluation Signed-off-by: arendu * mark autogenrated and remove it for test Signed-off-by: arendu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: arendu Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Add the `strategy` argument to `MegatronGPTModel.generate()` (#7264) It is passed as an explicit argument rather than through `**strategy_args` so as to ensure someone cannot accidentally pass other arguments that would end up being ignored. It is a keyword-only argument to ensure that if in the future we want to update the signature to `**strategy_args`, we can do it without breaking code. Signed-off-by: Olivier Delalleau <507137+odelalleau@users.noreply.github.com> * Fix PTL2.0 related ASR bugs in r1.21.0: Val metrics logging, None dataloader issue (#7531) (#7533) * fix none dataloader issue ptl2 * ptl2.0 logging fixes for rnnt_models --------- Signed-off-by: KunalDhawan Co-authored-by: Kunal Dhawan Co-authored-by: Nithin Rao * gpus -> devices (#7542) (#7545) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao * Update FFMPEG version to fix issue with torchaudio (#7551) (#7553) Signed-off-by: smajumdar Co-authored-by: Somshubra Majumdar * PEFT GPT & T5 Refactor (#7308) * initial implementation of add_adapters API * correct type hint * Add config in add_adapters for save and load (@author bobchen) * Remove AdapterConfig to avoid import error * Add AdaterConfig back and move adaptermixin to sft model * Add NLPSaveRestoreConnector as default in NLPModel.restore_from * Add restore_from_nemo_with_adapter and test script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * rename t5 file and classes to be consistent with GPT * add t5 sft dataset * add support for single-file format with T5SFTDataset * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Various small changes to make T5 SFT work like GPT SFT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add adapter evaluation test script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add MultiAdaterConfig for ia3 and fix builder issue * Make ptuning for T5SFTModel work using mixin * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add IA3_Adapter for AdapterName * Add adapter name for ptuning and attention adapter * Make test script GPT/T5 agnostic * Add layer selection feature * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Integrate adapter name and config * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update gpt peft tuning script to new API * add t5 peft tuning script with new API * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix IA3 layer selection issue * Override state_dict on SFT model instead of mixin * Add load adapter by adapter config * move peft config map away from example script * auto get config from nemo adapter * Move PEFTConfig to new file * fix ckpt save/load for t5 * name change: add_adapters -> add_adapter * variable name change * update t5 script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix t5 issues * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add weight tying * update gpt tuning script * PEFT-API proposal * Fix according to comments * update tuning scripts * move merge_cfg_with to mixin class since it applies to both gpt and t5 and requires the model class for restore * Add mcore_gpt support for NLPAdapterMixin * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix typo * variable name change to distinguish "peft" and "adapter" * override `load_adapters` to support `add_adapter` name change * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update tuning and eval script for adapter save/load * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add Ptuning on first stage only * add lora tutorial for review * Fix layer selection for mcore * add landing page * fix resume training Signed-off-by: jasonwan * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add mcore condition in sharded_state_dict to make sft work * Update lora_tutorial.md First edit of this file for PEFT documentation for NeMO Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> * rename Adapter to AttentionAdapter to avoid confusion in doc * Change load_adapters to load .nemo * add quick start guide * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add load_adapters with .ckpt * Remove setup_complete changes in load_adapters * update landing page * remove typo * Updated quick_start.md per Chen Cui Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> * Add inference config merger and tutorial * Add doc string for NLPAdapterModelMixin and deprecated warning on MegatronGPTPEFTModel * add supported_methods.md and update other documentations * Update supported_methods.md minor updates. Signed-off-by: Adi Renduchintala * Update landing_page.md minor update. Signed-off-by: Adi Renduchintala * Modify doc string for NLPAdapterModelMixin * Add doc string add_adapters in NLPAdapterModelMixin * rename canonical adapters * remove mcore hard dependency * [PATCH] move microbatch calculator to nemo from apex * remove apex dependency in gpt and t5 sft models * remove apex dependency in gpt model * render doc strings * fix * Add missing virtual_tokens on ptuning * fix docstrings * update gpt-style model coverage in docs * update docstring * Remove pdb * add lightning_fabric to make docstring rendering work * Add Ptuning missing key * try docstring rendering * Fix ptuning issue * update gpt t5 peft tuning and eval scripts * typos * update eval config * fix bug relating to apex dependency removal * typo * make predict step behave the same as test step * make lora tutorial work in notebook * cosmetics * update yaml scripts * mcore_gpt attribute optional * typo * update eval scripts and fix T5 eval bugs * add NLPDDPStrategyNotebook and trainer builder logic to use it * update lora notebook to use new trainer builder * fix microbatch calculator bug for inference after training * Convert markdown files to RST and incorporate with doc * typo * revise language * remove extra cell * remove unnecessary inheritance * remove old tests * move layer selection default so logging messages make sense * remove `save_adapters` as adapter weights are saved automatically during training * initialize weights from a checkpoint instead of randomly * multiple fields can form a context (#7147) * list of context fields and flexible prompt template Signed-off-by: arendu * list of fields for context Signed-off-by: arendu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * Fix bug Signed-off-by: Cheng-Ping Hsieh * Add multiple truncation fields and middle truncation Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Compatible to old ckpt Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix tokenize detokenize issue Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove detokenization, add truncation augmentation Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Resolve comments Signed-off-by: Cheng-Ping Hsieh * Remove unused import Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert eos Signed-off-by: Cheng-Ping Hsieh * Add tokenizer space_sensitive attribute Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix error Signed-off-by: Cheng-Ping Hsieh * Fix erorr and use re Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * Change assert logic Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Follow adi suggestion Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove merge function Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add example and comment Signed-off-by: Cheng-Ping Hsieh * Remove context_key and add comment Signed-off-by: Cheng-Ping Hsieh * Remove random truncation Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix template none Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh --------- Signed-off-by: arendu Signed-off-by: Cheng-Ping Hsieh Signed-off-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> * revert config changes * remove accidental breakpoint * support TP>1 loading * infer adapter type from checkpoint in during eval * breakup add adapter * enable interpolation of train_ds and validation_ds * update metric calc script to conform to single-file eval format * remove extraneous print * update lora notebook for updated merge_inference_cfg * Update nlp_adapter_mixins.py variable name change Signed-off-by: Chen Cui * turn off grad scaler for PP to match old scripts * remove PEFTSaveRestoreConnector since functionality all covered by the new mixin class * remove resume_from_checkpoint check since covered in #7335 * revert changes made in eval config interpolation * more interpolation * typo * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove dup line Signed-off-by: Chen Cui * code style warnings Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix config mistake Signed-off-by: Chen Cui * add copyright header Signed-off-by: Chen Cui * fix code check warnings Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert changes to remove apex dependency (mixed apex+nemo microbatch calculator broke some CI tests) Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add more deprecation notices Signed-off-by: Chen Cui * update deprecation notices Signed-off-by: Chen Cui * update deprecation notices Signed-off-by: Chen Cui * consolidate peft and sft scripts Signed-off-by: Chen Cui * update CI tests Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * notebook branch points to main to prepare for merge Signed-off-by: Chen Cui * fix gpt and t5 validation with any metric other than loss Signed-off-by: Chen Cui * support pre-extracted checkpoints Signed-off-by: Chen Cui --------- Signed-off-by: jasonwan Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> Signed-off-by: Adi Renduchintala Signed-off-by: arendu Signed-off-by: Cheng-Ping Hsieh Signed-off-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Chen Cui Co-authored-by: Chen Cui Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Marc Romeyn Co-authored-by: jasonwan Co-authored-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> Co-authored-by: Adi Renduchintala Co-authored-by: Yuanzhe Dong Co-authored-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> * fix a typo (#7496) Signed-off-by: BestJuly * [TTS] remove curly braces from ${BRANCH} in jupyer notebook cell. (#7554) (#7560) * remove curly braces. * remove installation of pynini. --------- Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> * add youtube embed url (#7570) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> * Remap speakers to continuous range of speaker_id for dataset AISHELL3 (#7536) * Remap speakers to continuous range of speaker_id for dataset AISHELL3 * Add new key/value pair to record raw speaker for AISHELL3 dataset Signed-off-by: Robin Dong --------- Signed-off-by: Robin Dong Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * fix validation_step_outputs initialization for multi-dataloader (#7546) (#7572) * added correct validation_step_outputs initialization for mutli-dataloader * changed kernel for display * Update logic for validation and test step outputs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert multidataloader changes in multilang ASR notebook --------- Signed-off-by: KunalDhawan Signed-off-by: smajumdar Co-authored-by: Kunal Dhawan Co-authored-by: Somshubra Majumdar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Append output of val step to self.validation_step_outputs (#7530) (#7532) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * [TTS] fixed trainer's accelerator and strategy. (#7569) (#7574) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> * Append val/test output to instance variable in EncDecSpeakerLabelModel (#7562) (#7573) * Append val/test output to the instance variable in EncDecSpeakerLabelModel * Handle test case in evaluation_step * Replace type with isinstance --------- Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * Fix CustomProgressBar for resume (#7427) (#7522) * Fix CustomProgress Bar for resume and multiple epochs * Edit num_training_batches * Use max_steps as total for progress bar for resume * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * fix typos in nfa and speech enhancement tutorials (#7580) (#7583) Signed-off-by: Elena Rastorgueva Co-authored-by: Elena Rastorgueva <80532067+erastorgueva-nv@users.noreply.github.com> * Add strategy as ddp_find_unused_parameters_true for glue_benchmark.py (#7454) (#7461) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * update strategy (#7577) (#7578) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao * Fix typos (#7581) * Change hifigan finetune strategy to ddp_find_unused_parameters_true (#7579) (#7584) * Change strategy to auto --------- Signed-off-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> * [BugFix] Add missing quotes for auto strategy in tutorial notebooks (#7541) (#7548) * Add missing quotes for auto strategy * Revert trainer.gpus to trainer.devices in Self_Supervised_Pre_Training.ipynb --------- Signed-off-by: Abhishree Signed-off-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * add build os key (#7596) (#7599) * add build os key * add tools * update to stable version --------- Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao * StarCoder SFT test + bump PyT NGC image to 23.09 (#7540) * Add SFT StarCoder test Signed-off-by: Jan Lasek * Remove _modify_config call as it is covered in load_from_nemo just below Signed-off-by: Jan Lasek * Test with pyt:23.09 container Signed-off-by: Jan Lasek --------- Signed-off-by: Jan Lasek * defaults changed (#7600) * defaults changed Signed-off-by: arendu * typo Signed-off-by: arendu * update Signed-off-by: arendu --------- Signed-off-by: arendu * add ItalianPhonemesTokenizer (#7587) * add ItalianPhonemesTokenizer Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix Italian phonemes Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add test Signed-off-by: GiacomoLeoneMaria --------- Signed-off-by: GiacomoLeoneMaria Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * best ckpt fix (#7564) (#7588) Signed-off-by: dimapihtar Co-authored-by: Dmytro Pykhtar <37850217+dimapihtar@users.noreply.github.com> * Add files via upload (#7598) specifies the branch Signed-off-by: George <37293288+Jorjeous@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * Fix validation in G2PModel and ThutmoseTaggerModel (#7597) (#7606) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain (#7576) (#7586) * Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Sangkug Lym Co-authored-by: Sangkug Lym Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Safeguard nemo_text_processing installation on ARM (#7485) * safeguard nemo_text_processing installing Signed-off-by: Jason * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update check Signed-off-by: Jason --------- Signed-off-by: Jason Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Bound transformers version in requirements (#7620) Signed-off-by: Abhishree * fix llama2 70b lora tuning bug (#7622) * fix llama2 70b lora tuning bug Signed-off-by: Chen Cui * Update peft_config.py brackets Signed-off-by: Adi Renduchintala --------- Signed-off-by: Chen Cui Signed-off-by: Adi Renduchintala Co-authored-by: Adi Renduchintala * Fix import error no module name model_utils (#7629) Signed-off-by: Mehadi Hasan Menon * add fc large ls models (#7641) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Koluguri * bugfix: trainer.gpus, trainer.strategy, trainer.accelerator (#7621) (#7642) * [TTS] bugfix for Tacotron2 tutorial due to PTL 2.0 * trainer.gpus -> trainer.devices * fixed related tutorial bugs --------- Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * fix ssl models ptl monitor val through logging (#7608) (#7614) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Co-authored-by: Eric Harper Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * Fix metrics for SE tutorial (#7604) (#7612) Signed-off-by: Ante Jukić Co-authored-by: anteju <108555623+anteju@users.noreply.github.com> * Add ddp_find_unused_parameters=True and change accelerator to auto (#7623) (#7644) * Add ddp_find_unused_parameters=True and change acclerator to auto * Add ddp_find_unused_parameters True for normalization_as_tagging_train.py --------- Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * Fix py3.11 dataclasses issue (#7616) * Fix py3.11 dataclasses issue (#7582) * Update ASR configs to support Python 3.11 Signed-off-by: smajumdar * Update TTS configs to support Python 3.11 Signed-off-by: smajumdar * Guard MeCab and Ipadic Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix remaining ASR dataclasses Signed-off-by: smajumdar * Fix remaining ASR dataclasses Signed-off-by: smajumdar * Fix scripts Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: smajumdar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Update name to ConfidenceMethodConfig Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain (#7576) (#7586) * Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Sangkug Lym Co-authored-by: Sangkug Lym Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Safeguard nemo_text_processing installation on ARM (#7485) * safeguard nemo_text_processing installing Signed-off-by: Jason * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update check Signed-off-by: Jason --------- Signed-off-by: Jason Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix changes to confidence measure Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: smajumdar Signed-off-by: Sangkug Lym Signed-off-by: Jason Co-authored-by: Somshubra Majumdar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Sangkug Lym Co-authored-by: Jason * [Stable Diffusion/ControlNet] Enable O2 training for SD and Fix ControlNet CI failure * Mingyuanm/dreambooth fix * Fix NeMo CI Infer Issue * DreamFusion * Move neva export changes * Add Imagen Synthetic Dataloader * Add VITWrapper and export stuff to wrapper * Update neva with megatron-core support * Fix issues with Dockerfile (#7650) (#7652) Signed-off-by: smajumdar Co-authored-by: Somshubra Majumdar * [ASR] RNN-T greedy decoding max_frames fix for alignment and confidence (#7635) * decoding and test fix Signed-off-by: Aleksandr Laptev * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Aleksandr Laptev Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * [ASR] Fix type error in jasper (#7636) (#7653) Signed-off-by: Ryan Co-authored-by: Ryan Langman * [TTS] Add STFT and SI-SDR loss to audio codec recipe (#7468) * [TTS] Add STFT and SI-SDR loss to audio codec recipe Signed-off-by: Ryan * [TTS] Fix STFT resolution Signed-off-by: Ryan * [TTS] Fix training metric logging Signed-off-by: Ryan * [TTS] Add docstring to mel and stft losses Signed-off-by: Ryan * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Ryan Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Create per.py (#7538) * Move model precision copy (#7336) * move cfg precision set to megatron base model Signed-off-by: Maanu Grover * remove copy from other models Signed-off-by: Maanu Grover * modify attribute not arg Signed-off-by: Maanu Grover * fix gpt model test for ptl 2.0 Signed-off-by: Maanu Grover * rename function and add docstring Signed-off-by: Maanu Grover * replace precision to dtype conditionals with func call Signed-off-by: Maanu Grover * unnecessary function and cfg reset Signed-off-by: Maanu Grover * set default value Signed-off-by: Maanu Grover * fix precision lookup in a few more places Signed-off-by: Maanu Grover * rename mapping function Signed-off-by: Maanu Grover * ununsed import Signed-off-by: Maanu Grover * save torch datatype to model Signed-off-by: Maanu Grover * set weights precision wrt amp o2 Signed-off-by: Maanu Grover * Revert "set weights precision wrt amp o2" This reverts commit 313a4bfe5eb69d771a6d2433898c0685836aef5c. Signed-off-by: Maanu Grover * revert half precision at inference attempt Signed-off-by: Maanu Grover * move autocast dtype to base model Signed-off-by: Maanu Grover * move params dtype to base model, enable fp16 O2 inf Signed-off-by: Maanu Grover * unused imports Signed-off-by: Maanu Grover --------- Signed-off-by: Maanu Grover Signed-off-by: Sasha Meister * Fix PEFT checkpoint loading (#7388) * Fix PEFT checkpoint loading Signed-off-by: Jason Wang * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Jason Wang Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Use distributed optimizer support for multiple dtypes (#7359) * Update distopt wrapper with multiple dtype support Remove manual handling of separate FP32 optimizer. Signed-off-by: Tim Moon * Use distopt support for contiguous buffers with multiple dtypes Signed-off-by: Tim Moon * Fix typo Signed-off-by: Tim Moon * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Separate distopt buckets for first GPT layer and non-overlapped params Signed-off-by: Tim Moon * Add distopt logic for int dtypes Signed-off-by: Tim Moon * Update Apex commit Signed-off-by: Tim Moon * Remove unused variables Signed-off-by: Tim Moon * Update Apex commit in README and Jenkensfile Signed-off-by: Tim Moon * Debug Dockerfile and Jenkinsfile Signed-off-by: Tim Moon --------- Signed-off-by: Tim Moon Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Eric Harper Signed-off-by: Sasha Meister * minor fix for llama ckpt conversion script (#7387) * minor fix for llama ckpt conversion script Signed-off-by: Jason Wang * Update Jenkinsfile Signed-off-by: Jason Wang * remove fast_swiglu configuration Signed-off-by: Jason Wang --------- Signed-off-by: Jason Wang Co-authored-by: Eric Harper Signed-off-by: Sasha Meister * Fix wrong calling of librosa.get_duration() in notebook (#7376) Signed-off-by: Robin Dong Co-authored-by: Somshubra Majumdar Signed-off-by: Sasha Meister * [PATCH] PEFT import mcore (#7393) * [PATCH] PEFT import mcore Signed-off-by: Jason Wang * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Jason Wang Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Create per.py Script for calculation Punctuation Error Rate and related rates (correct rate, deletions rate, etc.) Signed-off-by: Sasha Meister <117230141+ssh-meister@users.noreply.github.com> Signed-off-by: Sasha Meister * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Sasha Meister * [TTS] Added a callback for logging initial data (#7384) Signed-off-by: Ante Jukić Signed-off-by: Sasha Meister * Update Core Commit (#7402) * Update Core Commit Signed-off-by: Abhinav Khattar * update commit Signed-off-by: Abhinav Khattar --------- Signed-off-by: Abhinav Khattar Signed-off-by: Sasha Meister * Use cfg attribute in bert (#7394) * use cfg attribute instead of arg Signed-off-by: Maanu Grover * use torch_dtype in place of cfg.precision Signed-off-by: Maanu Grover * move precision copy before super constructor Signed-off-by: Maanu Grover * use trainer arg Signed-off-by: Maanu Grover --------- Signed-off-by: Maanu Grover Signed-off-by: Sasha Meister * Add support for bias conversion in Swiglu models (#7386) * Add support for bias conversion in Swiglu models Signed-off-by: smajumdar * Add support for auto extracting tokenizer model Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add support for auto extracting tokenizer model Signed-off-by: smajumdar * Fix issue with missing tokenizer Signed-off-by: smajumdar * Refactor Signed-off-by: smajumdar * Refactor Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: smajumdar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Update save_to and restore_from for dist checkpointing (#7343) * add dist ckpt to save to, in progress Signed-off-by: eharper * move dist ckpt Signed-off-by: eharper * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * clean up Signed-off-by: eharper * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update restore from, need to figure out how to initialize distributed Signed-off-by: eharper * [pre-commit.ci] auto fixes from 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<66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: jasonwan Signed-off-by: Sasha Meister * fix forward for with mcore=false (#7403) Signed-off-by: Jimmy Zhang Co-authored-by: Jimmy Zhang Signed-off-by: Sasha Meister * Fix logging to remove 's/it' from progress bar in Megatron models and add train_step_timing (#7374) * Add CustomProgressBar class to exp_manager and trainer callbacks Signed-off-by: Abhishree * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix the progress bar to reflect total microbatch cnt Signed-off-by: Abhishree * Modify CustomProgressBar class 1) Modify CustomProgressBar class to update progress bar per global_step instead of per microbatch 2) Add the callback to other megatron training/finetuning files that are not using MegatronTrainerBuilder Signed-off-by: Abhishree * Add CustomProgressBar callback to tuning files Signed-off-by: Abhishree * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Abhishree Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Set Activation Checkpointing Defaults (#7404) * Set Activation Checkpointing Defaults Signed-off-by: Abhinav Khattar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * check for None Signed-off-by: Abhinav Khattar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Abhinav Khattar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * make loss mask default to false (#7407) Signed-off-by: eharper Signed-off-by: Sasha Meister * Add dummy userbuffer config files (#7408) Signed-off-by: Sangkug Lym Signed-off-by: Sasha Meister * add missing ubconf files (#7412) Signed-off-by: Abhinav Khattar Signed-off-by: Sasha Meister * New tutorial on Speech Data Explorer (#7405) * Added Google Colab based tutorial on Speech Data Explorer Signed-off-by: George Zelenfroynd Signed-off-by: Sasha Meister * Update ptl training ckpt conversion script to work with dist ckpt (#7416) * update ptl convert script Signed-off-by: eharper * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * don't break legacy Signed-off-by: eharper * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: eharper Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Allow disabling sanity checking when num_sanity_val_steps=0 (#7413) * Allow disabling sanity checking when num_sanity_val_steps=0 Signed-off-by: Abhishree * Update num_sanity_val_steps to be a multiple of num_microbatches Signed-off-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Abhishree Signed-off-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Add comprehensive error messages (#7261) Signed-off-by: Anton Peganov Signed-off-by: Sasha Meister * check NEMO_PATH (#7418) Signed-off-by: Nikolay Karpov Signed-off-by: Sasha Meister * layer selection for ia3 (#7417) * layer selection for ia3 Signed-off-by: arendu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: arendu Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Fix missing pip package 'einops' (#7397) Signed-off-by: Robin Dong Signed-off-by: Sasha Meister * Fix failure of pyaudio in Google Colab (#7396) Signed-off-by: Robin Dong Signed-off-by: Sasha Meister * Update README.md: output_path --> output_manifest_filepath (#7442) Signed-off-by: Samuele Cornell Signed-off-by: Sasha Meister * Add rope dynamic linear scaling (#7437) * Add dynamic linear scaling Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix Signed-off-by: Cheng-Ping Hsieh --------- Signed-off-by: Cheng-Ping Hsieh Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yang Zhang Signed-off-by: Sasha Meister * Fix None dataloader issue in PTL2.0 (#7455) * Fix None dataloader issue in PTL2.0 Signed-off-by: KunalDhawan * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * updating values of self._validation_dl and self._test_dl as well Signed-off-by: KunalDhawan * updating values of self._validation_dl and self._test_dl as well Signed-off-by: KunalDhawan * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: KunalDhawan Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * [ASR] Confidence measure -> method renames (#7434) * measure -> method Signed-off-by: Aleksandr Laptev * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Aleksandr Laptev Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Add steps for document of getting dataset 'SF Bilingual Speech' (#7378) * Add steps for document of getting dataset 'SF Bilingual Speech' Signed-off-by: Robin Dong * Update datasets.rst added a link from a tutorial demonstrating detailed data prep steps. Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> --------- Signed-off-by: Robin Dong Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Sasha Meister * RNN-T confidence and alignment bugfix (#7381) * new frame_confidence and alignments lists are now always created after the while loop Signed-off-by: Aleksandr Laptev * tests added Signed-off-by: Aleksandr Laptev --------- Signed-off-by: Aleksandr Laptev Signed-off-by: Sasha Meister * Fix resume from checkpoint in exp_manager (#7424) (#7426) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: Eric Harper Signed-off-by: Sasha Meister * Fix checking of cuda/cpu device for inputs of Decoder (#7444) * Fix checking of cuda/cpu device for inputs of Decoder Signed-off-by: Robin Dong * Update tacotron2.py Signed-off-by: Jason --------- Signed-off-by: Robin Dong Signed-off-by: Jason Co-authored-by: Jason Signed-off-by: Sasha Meister * Fix failure of ljspeech's get_data.py (#7430) * Fix failure of ljspeech's get_data.py Signed-off-by: Robin Dong * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Robin Dong Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * [TTS] Fix audio codec type checks (#7373) * [TTS] Fix audio codec type checks Signed-off-by: Ryan * [TTS] Fix audio codec tests Signed-off-by: Ryan --------- Signed-off-by: Ryan Signed-off-by: Sasha Meister * [TTS] Add dataset to path of logged artifacts (#7462) * [TTS] Add dataset to path of logged artifacts Signed-off-by: Ryan * [TTS] Revert axis name back to Audio Frames Signed-off-by: Ryan --------- Signed-off-by: Ryan Signed-off-by: Sasha Meister * Fix sft dataset truncation (#7464) * Add fix Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix Signed-off-by: Cheng-Ping Hsieh --------- Signed-off-by: Cheng-Ping Hsieh Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Automatic Lip Reading Recognition (ALR) - ASR/CV (Visual ASR) (#7330) * striding_conv1d_k5 and dw_striding_conv1d_k5 subsampling Signed-off-by: mburchi * transpose conv1d inputs Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: mburchi * Update subsampling.py change striding_conv1d_k5 to striding_conv1d Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> * cv branch Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * video manifest Signed-off-by: mburchi * add collection classes Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add test_step_outputs Signed-off-by: mburchi * correct manifest bug when having only audio or only videos Signed-off-by: mburchi * correct manifest bug when having only audio or only videos Signed-off-by: mburchi * clean references Signed-off-by: mburchi * freeze unfreeze transcribe cv models Signed-off-by: mburchi * correct manifest get_full_path bug Signed-off-by: mburchi * update for PR Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * guard torchvision Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update nemo/collections/cv/data/video_to_text_dataset.py Co-authored-by: Igor Gitman Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> * _video_speech_collate_fn in cv/data/video_to_text.py Signed-off-by: mburchi * add self.out = None to asr subsampling Signed-off-by: mburchi * Update nemo/collections/cv/data/video_to_text_dataset.py Co-authored-by: Igor Gitman Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> * cv -> multimodal/speech_cv branch Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: mburchi Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Igor Gitman Signed-off-by: Sasha Meister * HF StarCoder to NeMo conversion script (#7421) * Script to convert HF StarCoder checkpoint to NeMo Signed-off-by: Jan Lasek * StarCoder conversion test Signed-off-by: Jan Lasek * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Jan Lasek * Fix test Signed-off-by: Jan Lasek * Catch up with save_to changes Signed-off-by: Jan Lasek * Don't abbreviate args for clarity Signed-off-by: Jan Lasek * Configurable precision: BF16 vs FP32 Signed-off-by: Jan Lasek * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Jan Lasek Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * fix bug when loading dist ckpt in peft (#7452) Signed-off-by: Hongbin Liu Co-authored-by: Hongbin Liu Signed-off-by: Sasha Meister * Fix adding positional embeddings in-place in transformer module (#7440) Signed-off-by: Tamerlan Tabolov Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Sasha Meister * Fix (#7478) Signed-off-by: Cheng-Ping Hsieh Signed-off-by: Sasha Meister * add sleep (#7498) (#7499) * add sleep * add sleep onto config instead * add comment --------- Signed-off-by: Gerald Shen Co-authored-by: Gerald Shen <119401249+gshennvm@users.noreply.github.com> Signed-off-by: Sasha Meister * Fix exp manager check for sleep (#7503) (#7504) Signed-off-by: smajumdar Co-authored-by: Somshubra Majumdar Signed-off-by: Sasha Meister * bugfix: trainer.accelerator=auto from None. (#7492) (#7493) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Sasha Meister * [doc] fix broken link (#7481) Signed-off-by: Stas Bekman Signed-off-by: Sasha Meister * [TTS] Read audio as int32 to avoid flac read errors (#7477) * [TTS] Read audio as int32 to avoid flac read errors Signed-off-by: Ryan * [TTS] Add comment about read failures Signed-off-by: Ryan --------- Signed-off-by: Ryan Signed-off-by: Sasha Meister * Add dataset 'AISHELL-3' from OpenSLR for training mandarin TTS (#7409) * Add dataset 'AISHELL-3' from OpenSLR for training mandarin TTS * Train 'AISHELL-3' dataset with multi-speakers Signed-off-by: Robin Dong * Update get_data.py update copyright header Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * Update get_data.py added a disclaimer Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add new configuration file for AISHELL3 with multispeaker of fastpitch Signed-off-by: Robin Dong --------- Signed-off-by: Robin Dong Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Sasha Meister * dllogger - log on rank 0 only (#7513) Signed-off-by: Stas Bekman Signed-off-by: Sasha Meister * Fix TTS FastPitch tutorial (#7494) (#7516) * Fix --------- Signed-off-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Sasha Meister * Fix get_dist() tensor dimension (#7506) (#7515) Signed-off-by: Jocelyn Huang Co-authored-by: Jocelyn Signed-off-by: Sasha Meister * bugfix: specify trainer.strategy=auto when devices=1 (#7509) (#7512) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Sasha Meister * fix (#7511) Signed-off-by: Abhinav Khattar Signed-off-by: Sasha Meister * [TTS] Fix FastPitch data prep tutorial (#7524) Signed-off-by: Ryan Signed-off-by: Sasha Meister * add italian tokenization (#7486) * add italian tokenization Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add more ipa lexicon it Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix error deletion Signed-off-by: GiacomoLeoneMaria * add test Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: GiacomoLeoneMaria Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Replace None strategy with auto in tutorial notebooks (#7521) (#7527) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Sasha Meister * unpin setuptools (#7534) (#7535) Signed-off-by: fayejf <36722593+fayejf@users.noreply.github.com> Co-authored-by: fayejf <36722593+fayejf@users.noreply.github.com> Signed-off-by: Sasha Meister * Update per.py - if __name__ == "__main__" removed (now metric can be imported); - removed excessive classes (like "Sample" and "Statistics"); - transition from pandas df to dict of dicts; - removed unnecessary "return"; - notation fixing; - reduced calculation time Signed-off-by: Sasha Meister <117230141+ssh-meister@users.noreply.github.com> Signed-off-by: Sasha Meister * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Sasha Meister * Create punctuation_rates.py Signed-off-by: Sasha Meister <117230141+ssh-meister@users.noreply.github.com> Signed-off-by: Sasha Meister * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Sasha Meister * Format fixing Signed-off-by: Sasha Meister <117230141+ssh-meister@users.noreply.github.com> Signed-off-by: Sasha Meister * added nemo.logging, header, docstrings, how to use Signed-off-by: Sasha Meister * Added asserions to rate_punctuation.py Signed-off-by: Sasha Meister * fix typo Signed-off-by: Sasha Meister * added function for import and call, docstrings Signed-off-by: Sasha Meister * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Sasha Meister * remove auto generated examples (#7510) * explicitly remove autogenerated examples for data parallel evaluation Signed-off-by: arendu * mark autogenrated and remove it for test Signed-off-by: arendu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: arendu Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Add the `strategy` argument to `MegatronGPTModel.generate()` (#7264) It is passed as an explicit argument rather than through `**strategy_args` so as to ensure someone cannot accidentally pass other arguments that would end up being ignored. It is a keyword-only argument to ensure that if in the future we want to update the signature to `**strategy_args`, we can do it without breaking code. Signed-off-by: Olivier Delalleau <507137+odelalleau@users.noreply.github.com> Signed-off-by: Sasha Meister * Fix PTL2.0 related ASR bugs in r1.21.0: Val metrics logging, None dataloader issue (#7531) (#7533) * fix none dataloader issue ptl2 * ptl2.0 logging fixes for rnnt_models --------- Signed-off-by: KunalDhawan Co-authored-by: Kunal Dhawan Co-authored-by: Nithin Rao Signed-off-by: Sasha Meister * gpus -> devices (#7542) (#7545) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Signed-off-by: Sasha Meister * Update FFMPEG version to fix issue with torchaudio (#7551) (#7553) Signed-off-by: smajumdar Co-authored-by: Somshubra Majumdar Signed-off-by: Sasha Meister * PEFT GPT & T5 Refactor (#7308) * initial implementation of add_adapters API * correct type hint * Add config in add_adapters for save and load (@author bobchen) * Remove AdapterConfig to avoid import error * Add AdaterConfig back and move adaptermixin to sft model * Add NLPSaveRestoreConnector as default in NLPModel.restore_from * Add restore_from_nemo_with_adapter and test script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * rename t5 file and classes to be consistent with GPT * add t5 sft dataset * add support for single-file format with T5SFTDataset * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Various small changes to make T5 SFT work like GPT SFT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add adapter evaluation test script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add MultiAdaterConfig for ia3 and fix builder issue * Make ptuning for T5SFTModel work using mixin * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add IA3_Adapter for AdapterName * Add adapter name for ptuning and attention adapter * Make test script GPT/T5 agnostic * Add layer selection feature * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Integrate adapter name and config * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update gpt peft tuning script to new API * add t5 peft tuning script with new API * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix IA3 layer selection issue * Override state_dict on SFT model instead of mixin * Add load adapter by adapter config * move peft config map away from example script * auto get config from nemo adapter * Move PEFTConfig to new file * fix ckpt save/load for t5 * name change: add_adapters -> add_adapter * variable name change * update t5 script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix t5 issues * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add weight tying * update gpt tuning script * PEFT-API proposal * Fix according to comments * update tuning scripts * move merge_cfg_with to mixin class since it applies to both gpt and t5 and requires the model class for restore * Add mcore_gpt support for NLPAdapterMixin * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix typo * variable name change to distinguish "peft" and "adapter" * override `load_adapters` to support `add_adapter` name change * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update tuning and eval script for adapter save/load * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add Ptuning on first stage only * add lora tutorial for review * Fix layer selection for mcore * add landing page * fix resume training Signed-off-by: jasonwan * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add mcore condition in sharded_state_dict to make sft work * Update lora_tutorial.md First edit of this file for PEFT documentation for NeMO Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> * rename Adapter to AttentionAdapter to avoid confusion in doc * Change load_adapters to load .nemo * add quick start guide * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add load_adapters with .ckpt * Remove setup_complete changes in load_adapters * update landing page * remove typo * Updated quick_start.md per Chen Cui Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> * Add inference config merger and tutorial * Add doc string for NLPAdapterModelMixin and deprecated warning on MegatronGPTPEFTModel * add supported_methods.md and update other documentations * Update supported_methods.md minor updates. Signed-off-by: Adi Renduchintala * Update landing_page.md minor update. Signed-off-by: Adi Renduchintala * Modify doc string for NLPAdapterModelMixin * Add doc string add_adapters in NLPAdapterModelMixin * rename canonical adapters * remove mcore hard dependency * [PATCH] move microbatch calculator to nemo from apex * remove apex dependency in gpt and t5 sft models * remove apex dependency in gpt model * render doc strings * fix * Add missing virtual_tokens on ptuning * fix docstrings * update gpt-style model coverage in docs * update docstring * Remove pdb * add lightning_fabric to make docstring rendering work * Add Ptuning missing key * try docstring rendering * Fix ptuning issue * update gpt t5 peft tuning and eval scripts * typos * update eval config * fix bug relating to apex dependency removal * typo * make predict step behave the same as test step * make lora tutorial work in notebook * cosmetics * update yaml scripts * mcore_gpt attribute optional * typo * update eval scripts and fix T5 eval bugs * add NLPDDPStrategyNotebook and trainer builder logic to use it * update lora notebook to use new trainer builder * fix microbatch calculator bug for inference after training * Convert markdown files to RST and incorporate with doc * typo * revise language * remove extra cell * remove unnecessary inheritance * remove old tests * move layer selection default so logging messages make sense * remove `save_adapters` as adapter weights are saved automatically during training * initialize weights from a checkpoint instead of randomly * multiple fields can form a context (#7147) * list of context fields and flexible prompt template Signed-off-by: arendu * list of fields for context Signed-off-by: arendu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * Fix bug Signed-off-by: Cheng-Ping Hsieh * Add multiple truncation fields and middle truncation Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Compatible to old ckpt Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix tokenize detokenize issue Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove detokenization, add truncation augmentation Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Resolve comments Signed-off-by: Cheng-Ping Hsieh * Remove unused import Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert eos Signed-off-by: Cheng-Ping Hsieh * Add tokenizer space_sensitive attribute Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix error Signed-off-by: Cheng-Ping Hsieh * Fix erorr and use re Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * Change assert logic Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Follow adi suggestion Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove merge function Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add example and comment Signed-off-by: Cheng-Ping Hsieh * Remove context_key and add comment Signed-off-by: Cheng-Ping Hsieh * Remove random truncation Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix template none Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh --------- Signed-off-by: arendu Signed-off-by: Cheng-Ping Hsieh Signed-off-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> * revert config changes * remove accidental breakpoint * support TP>1 loading * infer adapter type from checkpoint in during eval * breakup add adapter * enable interpolation of train_ds and validation_ds * update metric calc script to conform to single-file eval format * remove extraneous print * update lora notebook for updated merge_inference_cfg * Update nlp_adapter_mixins.py variable name change Signed-off-by: Chen Cui * turn off grad scaler for PP to match old scripts * remove PEFTSaveRestoreConnector since functionality all covered by the new mixin class * remove resume_from_checkpoint check since covered in #7335 * revert changes made in eval config interpolation * more interpolation * typo * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove dup line Signed-off-by: Chen Cui * code style warnings Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix config mistake Signed-off-by: Chen Cui * add copyright header Signed-off-by: Chen Cui * fix code check warnings Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert changes to remove apex dependency (mixed apex+nemo microbatch calculator broke some CI tests) Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add more deprecation notices Signed-off-by: Chen Cui * update deprecation notices Signed-off-by: Chen Cui * update deprecation notices Signed-off-by: Chen Cui * consolidate peft and sft scripts Signed-off-by: Chen Cui * update CI tests Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * notebook branch points to main to prepare for merge Signed-off-by: Chen Cui * fix gpt and t5 validation with any metric other than loss Signed-off-by: Chen Cui * support pre-extracted checkpoints Signed-off-by: Chen Cui --------- Signed-off-by: jasonwan Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> Signed-off-by: Adi Renduchintala Signed-off-by: arendu Signed-off-by: Cheng-Ping Hsieh Signed-off-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Chen Cui Co-authored-by: Chen Cui Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Marc Romeyn Co-authored-by: jasonwan Co-authored-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> Co-authored-by: Adi Renduchintala Co-authored-by: Yuanzhe Dong Co-authored-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Sasha Meister * fix a typo (#7496) Signed-off-by: BestJuly Signed-off-by: Sasha Meister * [TTS] remove curly braces from ${BRANCH} in jupyer notebook cell. (#7554) (#7560) * remove curly braces. * remove installation of pynini. --------- Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Sasha Meister * add youtube embed url (#7570) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Sasha Meister * Remap speakers to continuous range of speaker_id for dataset AISHELL3 (#7536) * Remap speakers to continuous range of speaker_id for dataset AISHELL3 * Add new key/value pair to record raw speaker for AISHELL3 dataset Signed-off-by: Robin Dong --------- Signed-off-by: Robin Dong Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * fix validation_step_outputs initialization for multi-dataloader (#7546) (#7572) * added correct validation_step_outputs initialization for mutli-dataloader * changed kernel for display * Update logic for validation and test step outputs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert multidataloader changes in multilang ASR notebook --------- Signed-off-by: KunalDhawan Signed-off-by: smajumdar Co-authored-by: Kunal Dhawan Co-authored-by: Somshubra Majumdar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Append output of val step to self.validation_step_outputs (#7530) (#7532) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Sasha Meister * [TTS] fixed trainer's accelerator and strategy. (#7569) (#7574) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Sasha Meister * Append val/test output to instance variable in EncDecSpeakerLabelModel (#7562) (#7573) * Append val/test output to the instance variable in EncDecSpeakerLabelModel * Handle test case in evaluation_step * Replace type with isinstance --------- Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Sasha Meister * Fix CustomProgressBar for resume (#7427) (#7522) * Fix CustomProgress Bar for resume and multiple epochs * Edit num_training_batches * Use max_steps as total for progress bar for resume * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * fix typos in nfa and speech enhancement tutorials (#7580) (#7583) Signed-off-by: Elena Rastorgueva Co-authored-by: Elena Rastorgueva <80532067+erastorgueva-nv@users.noreply.github.com> Signed-off-by: Sasha Meister * Add strategy as ddp_find_unused_parameters_true for glue_benchmark.py (#7454) (#7461) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Sasha Meister * update strategy (#7577) (#7578) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Signed-off-by: Sasha Meister * Fix typos (#7581) Signed-off-by: Sasha Meister * Change hifigan finetune strategy to ddp_find_unused_parameters_true (#7579) (#7584) * Change strategy to auto --------- Signed-off-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping 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<66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Safeguard nemo_text_processing installation on ARM (#7485) * safeguard nemo_text_processing installing Signed-off-by: Jason * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update check Signed-off-by: Jason --------- Signed-off-by: Jason Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Function name fixing Signed-off-by: Sasha Meister <117230141+ssh-meister@users.noreply.github.com> Signed-off-by: Sasha Meister * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Sasha Meister * Moving PER to speech_to_text_eval.py Added: - "use_per": PER metric computing; - "scores_per_sample": metrics computation sample by sample for wer/cer/punctuation rates; - "output_with_scores_filename": saving manifest with metrics 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Meister * add fc large ls models (#7641) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Koluguri Signed-off-by: Sasha Meister * bugfix: trainer.gpus, trainer.strategy, trainer.accelerator (#7621) (#7642) * [TTS] bugfix for Tacotron2 tutorial due to PTL 2.0 * trainer.gpus -> trainer.devices * fixed related tutorial bugs --------- Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Sasha Meister * fix ssl models ptl monitor val through logging (#7608) (#7614) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Co-authored-by: Eric Harper Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Sasha Meister * Fix metrics for SE tutorial (#7604) (#7612) Signed-off-by: Ante Jukić Co-authored-by: anteju <108555623+anteju@users.noreply.github.com> Signed-off-by: Sasha Meister * Add ddp_find_unused_parameters=True and 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<37850217+dimapihtar@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * generalized chat sft prompt (#7655) * fix dataset issues Signed-off-by: Yi Dong * working version Signed-off-by: Yi Dong * all passed Signed-off-by: Yi Dong * refactor tests Signed-off-by: Yi Dong * all pass Signed-off-by: Yi Dong * working version Signed-off-by: Yi Dong * use end name signal for labels Signed-off-by: Yi Dong * all fixed Signed-off-by: Yi Dong * update doc Signed-off-by: Yi Dong * style fix Signed-off-by: Yi Dong * remove unused imports Signed-off-by: Yi Dong * make sure nccl not timing out Signed-off-by: Yi Dong * style fix Signed-off-by: Yi Dong * generate example template Signed-off-by: Yi Dong * generic end of name token Signed-off-by: Yi Dong * style fix Signed-off-by: Yi Dong * add the chat prompt format into the config Signed-off-by: Yi Dong * make sure sft working Signed-off-by: Yi Dong * address reviewer comment 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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * propagate mp config (#7637) (#7639) Signed-off-by: eharper Co-authored-by: Eric Harper * Add find_unused_parameters_true for text_classiftn and punctuation_capitalization (#7649) (#7657) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * Hotfix (#7501) (#7568) Signed-off-by: Jan Baczek Co-authored-by: jbaczek <45043825+jbaczek@users.noreply.github.com> * Avoid duplicated checkpoint save (#7555) (#7566) Signed-off-by: Mikołaj Błaż Co-authored-by: mikolajblaz * Cache FP8 weight and transpose only at the first micro-batch in each validation and test routine (#7470) (#7483) * Cache weight and transpose only in the first batch in all training, val, and test runs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Sangkug Lym Co-authored-by: Sangkug Lym 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mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update nemo/collections/cv/data/video_to_text_dataset.py Co-authored-by: Igor Gitman Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> * _video_speech_collate_fn in cv/data/video_to_text.py Signed-off-by: mburchi * add self.out = None to asr subsampling Signed-off-by: mburchi * Update nemo/collections/cv/data/video_to_text_dataset.py Co-authored-by: Igor Gitman Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> * cv -> multimodal/speech_cv branch Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: mburchi Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Igor Gitman Signed-off-by: Elena Rastorgueva * HF StarCoder to NeMo conversion script (#7421) * Script to convert HF StarCoder checkpoint to NeMo Signed-off-by: Jan Lasek * StarCoder conversion test Signed-off-by: Jan Lasek * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Jan Lasek * Fix test Signed-off-by: Jan Lasek * Catch up with save_to changes Signed-off-by: Jan Lasek * Don't abbreviate args for clarity Signed-off-by: Jan Lasek * Configurable precision: BF16 vs FP32 Signed-off-by: Jan Lasek * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Jan Lasek Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * fix bug when loading dist ckpt in peft (#7452) Signed-off-by: Hongbin Liu Co-authored-by: Hongbin Liu Signed-off-by: Elena Rastorgueva * Fix adding positional embeddings in-place in transformer module (#7440) Signed-off-by: Tamerlan Tabolov Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Fix (#7478) Signed-off-by: Cheng-Ping Hsieh Signed-off-by: Elena Rastorgueva * add sleep (#7498) (#7499) * add sleep * add sleep onto config instead * add comment --------- Signed-off-by: Gerald Shen Co-authored-by: Gerald Shen <119401249+gshennvm@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Fix exp manager check for sleep (#7503) (#7504) Signed-off-by: smajumdar Co-authored-by: Somshubra Majumdar Signed-off-by: Elena Rastorgueva * bugfix: trainer.accelerator=auto from None. (#7492) (#7493) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Elena Rastorgueva * [doc] fix broken link (#7481) Signed-off-by: Stas Bekman Signed-off-by: Elena Rastorgueva * [TTS] Read audio as int32 to avoid flac read errors (#7477) * [TTS] Read audio as int32 to avoid flac read errors Signed-off-by: Ryan * [TTS] Add comment about read failures Signed-off-by: Ryan --------- Signed-off-by: Ryan Signed-off-by: Elena Rastorgueva * Add dataset 'AISHELL-3' from OpenSLR for training mandarin TTS (#7409) * Add dataset 'AISHELL-3' from OpenSLR for training mandarin TTS * Train 'AISHELL-3' dataset with multi-speakers Signed-off-by: Robin Dong * Update get_data.py update copyright header Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * Update get_data.py added a disclaimer Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add new configuration file for AISHELL3 with multispeaker of fastpitch Signed-off-by: Robin Dong --------- Signed-off-by: Robin Dong Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * dllogger - log on rank 0 only (#7513) Signed-off-by: Stas Bekman Signed-off-by: Elena Rastorgueva * Fix TTS FastPitch tutorial (#7494) (#7516) * Fix --------- Signed-off-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Fix get_dist() tensor dimension (#7506) (#7515) Signed-off-by: Jocelyn Huang Co-authored-by: Jocelyn Signed-off-by: Elena Rastorgueva * bugfix: specify trainer.strategy=auto when devices=1 (#7509) (#7512) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Elena Rastorgueva * fix (#7511) Signed-off-by: Abhinav Khattar Signed-off-by: Elena Rastorgueva * [TTS] Fix FastPitch data prep tutorial (#7524) Signed-off-by: Ryan Signed-off-by: Elena Rastorgueva * add italian tokenization (#7486) * add italian tokenization Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add more ipa lexicon it Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix error deletion Signed-off-by: GiacomoLeoneMaria * add test Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: GiacomoLeoneMaria Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Replace None strategy with auto in tutorial notebooks (#7521) (#7527) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * unpin setuptools (#7534) (#7535) Signed-off-by: fayejf <36722593+fayejf@users.noreply.github.com> Co-authored-by: fayejf <36722593+fayejf@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * remove auto generated examples (#7510) * explicitly remove autogenerated examples for data parallel evaluation Signed-off-by: arendu * mark autogenrated and remove it for test Signed-off-by: arendu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: arendu Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Add the `strategy` argument to `MegatronGPTModel.generate()` (#7264) It is passed as an explicit argument rather than through `**strategy_args` so as to ensure someone cannot accidentally pass other arguments that would end up being ignored. It is a keyword-only argument to ensure that if in the future we want to update the signature to `**strategy_args`, we can do it without breaking code. Signed-off-by: Olivier Delalleau <507137+odelalleau@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Fix PTL2.0 related ASR bugs in r1.21.0: Val metrics logging, None dataloader issue (#7531) (#7533) * fix none dataloader issue ptl2 * ptl2.0 logging fixes for rnnt_models --------- Signed-off-by: KunalDhawan Co-authored-by: Kunal Dhawan Co-authored-by: Nithin Rao Signed-off-by: Elena Rastorgueva * gpus -> devices (#7542) (#7545) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Signed-off-by: Elena Rastorgueva * Update FFMPEG version to fix issue with torchaudio (#7551) (#7553) Signed-off-by: smajumdar Co-authored-by: Somshubra Majumdar Signed-off-by: Elena Rastorgueva * PEFT GPT & T5 Refactor (#7308) * initial implementation of add_adapters API * correct type hint * Add config in add_adapters for save and load (@author bobchen) * Remove AdapterConfig to avoid import error * Add AdaterConfig back and move adaptermixin to sft model * Add NLPSaveRestoreConnector as default in NLPModel.restore_from * Add restore_from_nemo_with_adapter and test script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * rename t5 file and classes to be consistent with GPT * add t5 sft dataset * add support for single-file format with T5SFTDataset * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Various small changes to make T5 SFT work like GPT SFT * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add adapter evaluation test script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add MultiAdaterConfig for ia3 and fix builder issue * Make ptuning for T5SFTModel work using mixin * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add IA3_Adapter for AdapterName * Add adapter name for ptuning and attention adapter * Make test script GPT/T5 agnostic * Add layer selection feature * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Integrate adapter name and config * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update gpt peft tuning script to new API * add t5 peft tuning script with new API * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix IA3 layer selection issue * Override state_dict on SFT model instead of mixin * Add load adapter by adapter config * move peft config map away from example script * auto get config from nemo adapter * Move PEFTConfig to new file * fix ckpt save/load for t5 * name change: add_adapters -> add_adapter * variable name change * update t5 script * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix t5 issues * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add weight tying * update gpt tuning script * PEFT-API proposal * Fix according to comments * update tuning scripts * move merge_cfg_with to mixin class since it applies to both gpt and t5 and requires the model class for restore * Add mcore_gpt support for NLPAdapterMixin * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix typo * variable name change to distinguish "peft" and "adapter" * override `load_adapters` to support `add_adapter` name change * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update tuning and eval script for adapter save/load * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add Ptuning on first stage only * add lora tutorial for review * Fix layer selection for mcore * add landing page * fix resume training Signed-off-by: jasonwan * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add mcore condition in sharded_state_dict to make sft work * Update lora_tutorial.md First edit of this file for PEFT documentation for NeMO Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> * rename Adapter to AttentionAdapter to avoid confusion in doc * Change load_adapters to load .nemo * add quick start guide * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add load_adapters with .ckpt * Remove setup_complete changes in load_adapters * update landing page * remove typo * Updated quick_start.md per Chen Cui Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> * Add inference config merger and tutorial * Add doc string for NLPAdapterModelMixin and deprecated warning on MegatronGPTPEFTModel * add supported_methods.md and update other documentations * Update supported_methods.md minor updates. Signed-off-by: Adi Renduchintala * Update landing_page.md minor update. Signed-off-by: Adi Renduchintala * Modify doc string for NLPAdapterModelMixin * Add doc string add_adapters in NLPAdapterModelMixin * rename canonical adapters * remove mcore hard dependency * [PATCH] move microbatch calculator to nemo from apex * remove apex dependency in gpt and t5 sft models * remove apex dependency in gpt model * render doc strings * fix * Add missing virtual_tokens on ptuning * fix docstrings * update gpt-style model coverage in docs * update docstring * Remove pdb * add lightning_fabric to make docstring rendering work * Add Ptuning missing key * try docstring rendering * Fix ptuning issue * update gpt t5 peft tuning and eval scripts * typos * update eval config * fix bug relating to apex dependency removal * typo * make predict step behave the same as test step * make lora tutorial work in notebook * cosmetics * update yaml scripts * mcore_gpt attribute optional * typo * update eval scripts and fix T5 eval bugs * add NLPDDPStrategyNotebook and trainer builder logic to use it * update lora notebook to use new trainer builder * fix microbatch calculator bug for inference after training * Convert markdown files to RST and incorporate with doc * typo * revise language * remove extra cell * remove unnecessary inheritance * remove old tests * move layer selection default so logging messages make sense * remove `save_adapters` as adapter weights are saved automatically during training * initialize weights from a checkpoint instead of randomly * multiple fields can form a context (#7147) * list of context fields and flexible prompt template Signed-off-by: arendu * list of fields for context Signed-off-by: arendu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * Fix bug Signed-off-by: Cheng-Ping Hsieh * Add multiple truncation fields and middle truncation Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Compatible to old ckpt Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix tokenize detokenize issue Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove detokenization, add truncation augmentation Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Resolve comments Signed-off-by: Cheng-Ping Hsieh * Remove unused import Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert eos Signed-off-by: Cheng-Ping Hsieh * Add tokenizer space_sensitive attribute Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix error Signed-off-by: Cheng-Ping Hsieh * Fix erorr and use re Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * Change assert logic Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Follow adi suggestion Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove merge function Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add example and comment Signed-off-by: Cheng-Ping Hsieh * Remove context_key and add comment Signed-off-by: Cheng-Ping Hsieh * Remove random truncation Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix template none Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix bug Signed-off-by: Cheng-Ping Hsieh --------- Signed-off-by: arendu Signed-off-by: Cheng-Ping Hsieh Signed-off-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> * revert config changes * remove accidental breakpoint * support TP>1 loading * infer adapter type from checkpoint in during eval * breakup add adapter * enable interpolation of train_ds and validation_ds * update metric calc script to conform to single-file eval format * remove extraneous print * update lora notebook for updated merge_inference_cfg * Update nlp_adapter_mixins.py variable name change Signed-off-by: Chen Cui * turn off grad scaler for PP to match old scripts * remove PEFTSaveRestoreConnector since functionality all covered by the new mixin class * remove resume_from_checkpoint check since covered in #7335 * revert changes made in eval config interpolation * more interpolation * typo * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove dup line Signed-off-by: Chen Cui * code style warnings Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix config mistake Signed-off-by: Chen Cui * add copyright header Signed-off-by: Chen Cui * fix code check warnings Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert changes to remove apex dependency (mixed apex+nemo microbatch calculator broke some CI tests) Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add more deprecation notices Signed-off-by: Chen Cui * update deprecation notices Signed-off-by: Chen Cui * update deprecation notices Signed-off-by: Chen Cui * consolidate peft and sft scripts Signed-off-by: Chen Cui * update CI tests Signed-off-by: Chen Cui * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * notebook branch points to main to prepare for merge Signed-off-by: Chen Cui * fix gpt and t5 validation with any metric other than loss Signed-off-by: Chen Cui * support pre-extracted checkpoints Signed-off-by: Chen Cui --------- Signed-off-by: jasonwan Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> Signed-off-by: Adi Renduchintala Signed-off-by: arendu Signed-off-by: Cheng-Ping Hsieh Signed-off-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Chen Cui Co-authored-by: Chen Cui Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Marc Romeyn Co-authored-by: jasonwan Co-authored-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> Co-authored-by: Adi Renduchintala Co-authored-by: Yuanzhe Dong Co-authored-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * fix a typo (#7496) Signed-off-by: BestJuly Signed-off-by: Elena Rastorgueva * [TTS] remove curly braces from ${BRANCH} in jupyer notebook cell. (#7554) (#7560) * remove curly braces. * remove installation of pynini. --------- Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Elena Rastorgueva * add youtube embed url (#7570) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Elena Rastorgueva * Remap speakers to continuous range of speaker_id for dataset AISHELL3 (#7536) * Remap speakers to continuous range of speaker_id for dataset AISHELL3 * Add new key/value pair to record raw speaker for AISHELL3 dataset Signed-off-by: Robin Dong --------- Signed-off-by: Robin Dong Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * fix validation_step_outputs initialization for multi-dataloader (#7546) (#7572) * added correct validation_step_outputs initialization for mutli-dataloader * changed kernel for display * Update logic for validation and test step outputs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert multidataloader changes in multilang ASR notebook --------- Signed-off-by: KunalDhawan Signed-off-by: smajumdar Co-authored-by: Kunal Dhawan Co-authored-by: Somshubra Majumdar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Append output of val step to self.validation_step_outputs (#7530) (#7532) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * [TTS] fixed trainer's accelerator and strategy. (#7569) (#7574) Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Elena Rastorgueva * Append val/test output to instance variable in EncDecSpeakerLabelModel (#7562) (#7573) * Append val/test output to the instance variable in EncDecSpeakerLabelModel * Handle test case in evaluation_step * Replace type with isinstance --------- Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Fix CustomProgressBar for resume (#7427) (#7522) * Fix CustomProgress Bar for resume and multiple epochs * Edit num_training_batches * Use max_steps as total for progress bar for resume * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * fix typos in nfa and speech enhancement tutorials (#7580) (#7583) Signed-off-by: Elena Rastorgueva Co-authored-by: Elena Rastorgueva <80532067+erastorgueva-nv@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Add strategy as ddp_find_unused_parameters_true for glue_benchmark.py (#7454) (#7461) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * update strategy (#7577) (#7578) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Signed-off-by: Elena Rastorgueva * Fix typos (#7581) Signed-off-by: Elena Rastorgueva * Change hifigan finetune strategy to ddp_find_unused_parameters_true (#7579) (#7584) * Change strategy to auto --------- Signed-off-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * [BugFix] Add missing quotes for auto strategy in tutorial notebooks (#7541) (#7548) * Add missing quotes for auto strategy * Revert trainer.gpus to trainer.devices in Self_Supervised_Pre_Training.ipynb --------- Signed-off-by: Abhishree Signed-off-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * add build os key (#7596) (#7599) * add build os key * add tools * update to stable version --------- Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Signed-off-by: Elena Rastorgueva * StarCoder SFT test + bump PyT NGC image to 23.09 (#7540) * Add SFT StarCoder test Signed-off-by: Jan Lasek * Remove _modify_config call as it is covered in load_from_nemo just below Signed-off-by: Jan Lasek * Test with pyt:23.09 container Signed-off-by: Jan Lasek --------- Signed-off-by: Jan Lasek Signed-off-by: Elena Rastorgueva * defaults changed (#7600) * defaults changed Signed-off-by: arendu * typo Signed-off-by: arendu * update Signed-off-by: arendu --------- Signed-off-by: arendu Signed-off-by: Elena Rastorgueva * add ItalianPhonemesTokenizer (#7587) * add ItalianPhonemesTokenizer Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix Italian phonemes Signed-off-by: GiacomoLeoneMaria * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add test Signed-off-by: GiacomoLeoneMaria --------- Signed-off-by: GiacomoLeoneMaria Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * best ckpt fix (#7564) (#7588) Signed-off-by: dimapihtar Co-authored-by: Dmytro Pykhtar <37850217+dimapihtar@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Add files via upload (#7598) specifies the branch Signed-off-by: George <37293288+Jorjeous@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Fix validation in G2PModel and ThutmoseTaggerModel (#7597) (#7606) Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain (#7576) (#7586) * Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Sangkug Lym Co-authored-by: Sangkug Lym Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Safeguard nemo_text_processing installation on ARM (#7485) * safeguard nemo_text_processing installing Signed-off-by: Jason * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update check Signed-off-by: Jason --------- Signed-off-by: Jason Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Bound transformers version in requirements (#7620) Signed-off-by: Abhishree Signed-off-by: Elena Rastorgueva * fix llama2 70b lora tuning bug (#7622) * fix llama2 70b lora tuning bug Signed-off-by: Chen Cui * Update peft_config.py brackets Signed-off-by: Adi Renduchintala --------- Signed-off-by: Chen Cui Signed-off-by: Adi Renduchintala Co-authored-by: Adi Renduchintala Signed-off-by: Elena Rastorgueva * Fix import error no module name model_utils (#7629) Signed-off-by: Mehadi Hasan Menon Signed-off-by: Elena Rastorgueva * add fc large ls models (#7641) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Koluguri Signed-off-by: Elena Rastorgueva * bugfix: trainer.gpus, trainer.strategy, trainer.accelerator (#7621) (#7642) * [TTS] bugfix for Tacotron2 tutorial due to PTL 2.0 * trainer.gpus -> trainer.devices * fixed related tutorial bugs --------- Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * fix ssl models ptl monitor val through logging (#7608) (#7614) Signed-off-by: Nithin Rao Koluguri Co-authored-by: Nithin Rao Co-authored-by: Eric Harper Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Fix metrics for SE tutorial (#7604) (#7612) Signed-off-by: Ante Jukić Co-authored-by: anteju <108555623+anteju@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Add ddp_find_unused_parameters=True and change accelerator to auto (#7623) (#7644) * Add ddp_find_unused_parameters=True and change acclerator to auto * Add ddp_find_unused_parameters True for normalization_as_tagging_train.py --------- Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * Fix py3.11 dataclasses issue (#7616) * Fix py3.11 dataclasses issue (#7582) * Update ASR configs to support Python 3.11 Signed-off-by: smajumdar * Update TTS configs to support Python 3.11 Signed-off-by: smajumdar * Guard MeCab and Ipadic Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix remaining ASR dataclasses Signed-off-by: smajumdar * Fix remaining ASR dataclasses Signed-off-by: smajumdar * Fix scripts Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: smajumdar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Update name to ConfidenceMethodConfig Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain (#7576) (#7586) * Broadcast loss only when using pipeline parallelism and within the pipeline parallel domain * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Sangkug Lym Co-authored-by: Sangkug Lym Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Safeguard nemo_text_processing installation on ARM (#7485) * safeguard nemo_text_processing installing Signed-off-by: Jason * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update check Signed-off-by: Jason --------- Signed-off-by: Jason Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Fix changes to confidence measure Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: smajumdar Signed-off-by: Sangkug Lym Signed-off-by: Jason Co-authored-by: Somshubra Majumdar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Sangkug Lym Co-authored-by: Jason Signed-off-by: Elena Rastorgueva * Fix issues with Dockerfile (#7650) (#7652) Signed-off-by: smajumdar Co-authored-by: Somshubra Majumdar Signed-off-by: Elena Rastorgueva * [ASR] RNN-T greedy decoding max_frames fix for alignment and confidence (#7635) * decoding and test fix Signed-off-by: Aleksandr Laptev * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Aleksandr Laptev Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * [ASR] Fix type error in jasper (#7636) (#7653) Signed-off-by: Ryan Co-authored-by: Ryan Langman Signed-off-by: Elena Rastorgueva * [TTS] Add STFT and SI-SDR loss to audio codec recipe (#7468) * [TTS] Add STFT and SI-SDR loss to audio codec recipe Signed-off-by: Ryan * [TTS] Fix STFT resolution Signed-off-by: Ryan * [TTS] Fix training metric logging Signed-off-by: Ryan * [TTS] Add docstring to mel and stft losses Signed-off-by: Ryan * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Ryan Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Elena Rastorgueva * add outline of asr quickstart info to asr/intro.rst Signed-off-by: Elena Rastorgueva * add CLI, LM and real-time transcription sections Signed-off-by: Elena Rastorgueva * Create per.py (#7538) * Move model precision copy (#7336) * move cfg precision set to megatron base model Signed-off-by: Maanu Grover * remove copy from other models Signed-off-by: Maanu Grover * modify attribute not arg Signed-off-by: Maanu Grover * fix gpt model test for ptl 2.0 Signed-off-by: Maanu Grover * rename function and add docstring Signed-off-by: Maanu Grover * replace precision to dtype conditionals with func call Signed-off-by: Maanu Grover * unnecessary function and cfg reset Signed-off-by: Maanu Grover * set default value Signed-off-by: Maanu Grover * fix precision lookup in a few more places Signed-off-by: Maanu Grover * rename mapping function Signed-off-by: Maanu Grover * ununsed import Signed-off-by: Maanu Grover * save torch datatype to model Signed-off-by: Maanu Grover * set weights precision wrt amp o2 Signed-off-by: Maanu Grover * Revert "set weights precision wrt amp o2" This reverts commit 313a4bfe5eb69d771a6d2433898c0685836aef5c. Signed-off-by: Maanu Grover * revert half precision at inference attempt Signed-off-by: Maanu Grover * move autocast dtype to base model Signed-off-by: Maanu Grover * move params dtype to base model, enable fp16 O2 inf Signed-off-by: Maanu Grover * unused imports Signed-off-by: Maanu Grover --------- Signed-off-by: Maanu Grover Signed-off-by: Sasha Meister * Fix PEFT checkpoint loading (#7388) * Fix PEFT checkpoint loading Signed-off-by: Jason Wang * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Jason Wang Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Use distributed optimizer support for multiple dtypes (#7359) * Update distopt wrapper with multiple dtype support Remove manual handling of separate FP32 optimizer. Signed-off-by: Tim Moon * Use distopt support for contiguous buffers with multiple dtypes Signed-off-by: Tim Moon * Fix typo Signed-off-by: Tim Moon * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Separate distopt buckets for first GPT layer and non-overlapped params Signed-off-by: Tim Moon * Add distopt logic for int dtypes Signed-off-by: Tim Moon * Update Apex commit Signed-off-by: Tim Moon * Remove unused variables Signed-off-by: Tim Moon * Update Apex commit in README and Jenkensfile Signed-off-by: Tim Moon * Debug Dockerfile and Jenkinsfile Signed-off-by: Tim Moon --------- Signed-off-by: Tim Moon Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Eric Harper Signed-off-by: Sasha Meister * minor fix for llama ckpt conversion script (#7387) * minor fix for llama ckpt conversion script Signed-off-by: Jason Wang * Update Jenkinsfile Signed-off-by: Jason Wang * remove fast_swiglu configuration Signed-off-by: Jason Wang --------- Signed-off-by: Jason Wang Co-authored-by: Eric Harper Signed-off-by: Sasha Meister * Fix wrong calling of librosa.get_duration() in notebook (#7376) Signed-off-by: Robin Dong Co-authored-by: Somshubra Majumdar Signed-off-by: Sasha Meister * [PATCH] PEFT import mcore (#7393) * [PATCH] PEFT import mcore Signed-off-by: Jason Wang * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Jason Wang Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Create per.py Script for calculation Punctuation Error Rate and related rates (correct rate, deletions rate, etc.) Signed-off-by: Sasha Meister <117230141+ssh-meister@users.noreply.github.com> Signed-off-by: Sasha Meister * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Sasha Meister * [TTS] Added a callback for logging initial data (#7384) Signed-off-by: Ante Jukić Signed-off-by: Sasha Meister * Update Core Commit (#7402) * Update Core Commit Signed-off-by: Abhinav Khattar * update commit Signed-off-by: Abhinav Khattar --------- Signed-off-by: Abhinav Khattar Signed-off-by: Sasha Meister * Use cfg attribute in bert (#7394) * use cfg attribute instead of arg Signed-off-by: Maanu Grover * use torch_dtype in place of cfg.precision Signed-off-by: Maanu Grover * move precision copy before super constructor Signed-off-by: Maanu Grover * use trainer arg Signed-off-by: Maanu Grover --------- Signed-off-by: Maanu Grover Signed-off-by: Sasha Meister * Add support for bias conversion in Swiglu models (#7386) * Add support for bias conversion in Swiglu models Signed-off-by: smajumdar * Add support for auto extracting tokenizer model Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add support for auto extracting tokenizer model Signed-off-by: smajumdar * Fix issue with missing tokenizer Signed-off-by: smajumdar * Refactor Signed-off-by: smajumdar * Refactor Signed-off-by: smajumdar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: smajumdar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Update save_to and restore_from for dist checkpointing (#7343) * add dist ckpt to save to, in progress Signed-off-by: eharper * move dist ckpt Signed-off-by: eharper * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * clean up Signed-off-by: eharper * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update restore from, need to figure out how to initialize distributed Signed-off-by: eharper * [pre-commit.ci] auto fixes from 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<66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: jasonwan Signed-off-by: Sasha Meister * fix forward for with mcore=false (#7403) Signed-off-by: Jimmy Zhang Co-authored-by: Jimmy Zhang Signed-off-by: Sasha Meister * Fix logging to remove 's/it' from progress bar in Megatron models and add train_step_timing (#7374) * Add CustomProgressBar class to exp_manager and trainer callbacks Signed-off-by: Abhishree * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix the progress bar to reflect total microbatch cnt Signed-off-by: Abhishree * Modify CustomProgressBar class 1) Modify CustomProgressBar class to update progress bar per global_step instead of per microbatch 2) Add the callback to other megatron training/finetuning files that are not using MegatronTrainerBuilder Signed-off-by: Abhishree * Add CustomProgressBar callback to tuning files Signed-off-by: Abhishree * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Abhishree Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Set Activation Checkpointing Defaults (#7404) * Set Activation Checkpointing Defaults Signed-off-by: Abhinav Khattar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * check for None Signed-off-by: Abhinav Khattar * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Abhinav Khattar Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * make loss mask default to false (#7407) Signed-off-by: eharper Signed-off-by: Sasha Meister * Add dummy userbuffer config files (#7408) Signed-off-by: Sangkug Lym Signed-off-by: Sasha Meister * add missing ubconf files (#7412) Signed-off-by: Abhinav Khattar Signed-off-by: Sasha Meister * New tutorial on Speech Data Explorer (#7405) * Added Google Colab based tutorial on Speech Data Explorer Signed-off-by: George Zelenfroynd Signed-off-by: Sasha Meister * Update ptl training ckpt conversion script to work with dist ckpt (#7416) * update ptl convert script Signed-off-by: eharper * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * don't break legacy Signed-off-by: eharper * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: eharper Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Allow disabling sanity checking when num_sanity_val_steps=0 (#7413) * Allow disabling sanity checking when num_sanity_val_steps=0 Signed-off-by: Abhishree * Update num_sanity_val_steps to be a multiple of num_microbatches Signed-off-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Abhishree Signed-off-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Add comprehensive error messages (#7261) Signed-off-by: Anton Peganov Signed-off-by: Sasha Meister * check NEMO_PATH (#7418) Signed-off-by: Nikolay Karpov Signed-off-by: Sasha Meister * layer selection for ia3 (#7417) * layer selection for ia3 Signed-off-by: arendu * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: arendu Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Fix missing pip package 'einops' (#7397) Signed-off-by: Robin Dong Signed-off-by: Sasha 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<66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yang Zhang Signed-off-by: Sasha Meister * Fix None dataloader issue in PTL2.0 (#7455) * Fix None dataloader issue in PTL2.0 Signed-off-by: KunalDhawan * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * updating values of self._validation_dl and self._test_dl as well Signed-off-by: KunalDhawan * updating values of self._validation_dl and self._test_dl as well Signed-off-by: KunalDhawan * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: KunalDhawan Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * [ASR] Confidence measure -> method renames (#7434) * measure -> method Signed-off-by: Aleksandr Laptev * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Aleksandr Laptev Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Add steps for document of getting dataset 'SF Bilingual Speech' (#7378) * Add steps for document of getting dataset 'SF Bilingual Speech' Signed-off-by: Robin Dong * Update datasets.rst added a link from a tutorial demonstrating detailed data prep steps. 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Signed-off-by: Robin Dong Signed-off-by: Jason Co-authored-by: Jason Signed-off-by: Sasha Meister * Fix failure of ljspeech's get_data.py (#7430) * Fix failure of ljspeech's get_data.py Signed-off-by: Robin Dong * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Robin Dong Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * [TTS] Fix audio codec type checks (#7373) * [TTS] Fix audio codec type checks Signed-off-by: Ryan * [TTS] Fix audio codec tests Signed-off-by: Ryan --------- Signed-off-by: Ryan Signed-off-by: Sasha Meister * [TTS] Add dataset to path of logged artifacts (#7462) * [TTS] Add dataset to path of logged artifacts Signed-off-by: Ryan * [TTS] Revert axis name back to Audio Frames Signed-off-by: Ryan --------- Signed-off-by: Ryan Signed-off-by: Sasha Meister * Fix sft dataset truncation (#7464) * Add fix Signed-off-by: Cheng-Ping Hsieh * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix Signed-off-by: Cheng-Ping Hsieh --------- Signed-off-by: Cheng-Ping Hsieh Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Signed-off-by: Sasha Meister * Automatic Lip Reading Recognition (ALR) - ASR/CV (Visual ASR) (#7330) * striding_conv1d_k5 and dw_striding_conv1d_k5 subsampling Signed-off-by: mburchi * transpose conv1d inputs Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: mburchi * Update subsampling.py change striding_conv1d_k5 to striding_conv1d Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> * cv branch Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * video manifest Signed-off-by: mburchi * add collection classes Signed-off-by: mburchi * [pre-commit.ci] auto fixes from pre-… * fix onnx (#7703) (#7704) Signed-off-by: fayejf Co-authored-by: fayejf <36722593+fayejf@users.noreply.github.com> * move core install to /workspace (#7706) Signed-off-by: Abhinav Khattar * Fix typo in audio codec config, encoder target (#7697) Signed-off-by: Ante Jukić * Replace strategy='dp'/None with 'auto' (#7681) (#7696) * Add strategy=auto for None and dp * Change strategy from None to auto --------- Signed-off-by: Abhishree Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> * [ASR] Multichannel mask estimator with flex number of channels (#7317) * Adding a mask estimator which can process an arbitrary number of channels Signed-off-by: Ante Jukić * Bypass failing tests + mark as pleasefixme Signed-off-by: Ante Jukić --------- Signed-off-by: Ante Jukić * fix ptl_bugs in slu_models.py (#7689) (#7712) * fix ptl_bugs in slu_models.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more 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information, see https://pre-commit.ci * Fix Eric's comments * Revert "Hide multimodal code changes" This reverts commit d6900f9bc1922d086e2e388dcec6e3bd2b0f59dc. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix configs * Fix neva model * Fix neva casting * Fix neva LoRA non MCore version * Fix neva LoRA MCore * [SD] group norm fixes * Fix neva cfg merge * remove groupnorm dependency * Fix copyright headers * LLaVA 1_5 and LORA update * Fix logs * Fix neva mcore infernece * Fix ema * Fix ema * Address Somshubra comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix NeVA * Remove llama tricks since we are padding the embedding weights directly now * Update Dockerfile and mm requirements * Multimodal unit and jenkins tests * Add Multimodal Docs * update default conv_template * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see 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bug fix Signed-off-by: yaoyu-33 * code scan clean * Fix Jenkinsfile * Fix copyright * Update requirements * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove git versioning in requirements_test.txt * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update requirements * Fix imports * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix imports * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix imports * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add a guard to warn users always to use spartial transformer * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix logging * Fix jenkins * Hide multimodal unit test * Remove flash attn requirement * Hide vision test * Fix requirements for multimodal * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix requirements for multimodal * Fix readme * Mv Multimodal jenkins earlier * Fix controlnet * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove some requirements * Don't use flash attn in jenkins * Turn off flash-attn * Hide MM jenkins tests * replaced pymeshlab library with trimesh * dropped pymeshlab from multimodal requirements file * Update imageio requirements --------- Signed-off-by: Ryan Signed-off-by: GiacomoLeoneMaria Signed-off-by: Abhishree Signed-off-by: fayejf <36722593+fayejf@users.noreply.github.com> Signed-off-by: arendu Signed-off-by: Olivier Delalleau <507137+odelalleau@users.noreply.github.com> Signed-off-by: KunalDhawan Signed-off-by: Nithin Rao Koluguri Signed-off-by: smajumdar Signed-off-by: jasonwan Signed-off-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> Signed-off-by: Adi Renduchintala Signed-off-by: arendu Signed-off-by: Cheng-Ping Hsieh Signed-off-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Signed-off-by: Chen Cui Signed-off-by: BestJuly Signed-off-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Signed-off-by: Robin Dong Signed-off-by: Elena Rastorgueva Signed-off-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Signed-off-by: Jan Lasek Signed-off-by: dimapihtar Signed-off-by: George <37293288+Jorjeous@users.noreply.github.com> Signed-off-by: Sangkug Lym Signed-off-by: Jason Signed-off-by: Mehadi Hasan Menon Signed-off-by: Ante Jukić Signed-off-by: Aleksandr Laptev Signed-off-by: Maanu Grover Signed-off-by: Sasha Meister Signed-off-by: Jason Wang Signed-off-by: Tim Moon Signed-off-by: Sasha Meister <117230141+ssh-meister@users.noreply.github.com> Signed-off-by: Abhinav Khattar Signed-off-by: eharper Signed-off-by: Jimmy Zhang Signed-off-by: George Zelenfroynd Signed-off-by: Anton Peganov Signed-off-by: Nikolay Karpov Signed-off-by: Samuele Cornell Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: mburchi Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> Signed-off-by: Hongbin Liu Signed-off-by: Tamerlan Tabolov Signed-off-by: Gerald Shen Signed-off-by: Stas Bekman Signed-off-by: Jocelyn Huang Signed-off-by: Yi Dong Signed-off-by: fayejf Signed-off-by: Igor Gitman Signed-off-by: Jan Baczek Signed-off-by: Mikołaj Błaż Signed-off-by: Elena Rastorgueva <80532067+erastorgueva-nv@users.noreply.github.com> Signed-off-by: Seonghun Noh Signed-off-by: Seonghun Signed-off-by: Eric Harper Signed-off-by: yaoyu-33 <54727607+yaoyu-33@users.noreply.github.com> Signed-off-by: yaoyu-33 Co-authored-by: Ryan Langman Co-authored-by: Giacomo Leone Maria Cavallini <72698188+GiacomoLeoneMaria@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: fayejf <36722593+fayejf@users.noreply.github.com> Co-authored-by: Adi Renduchintala Co-authored-by: Olivier Delalleau <507137+odelalleau@users.noreply.github.com> Co-authored-by: Kunal Dhawan Co-authored-by: Nithin Rao Co-authored-by: Somshubra Majumdar Co-authored-by: meatybobby Co-authored-by: Chen Cui Co-authored-by: Marc Romeyn Co-authored-by: jasonwan Co-authored-by: hkelly33 <58792115+hkelly33@users.noreply.github.com> Co-authored-by: Yuanzhe Dong Co-authored-by: Cheng-Ping Hsieh Co-authored-by: Cheng-Ping Hsieh <37269846+hsiehjackson@users.noreply.github.com> Co-authored-by: Li Tao Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Xuesong Yang <16880-xueyang@users.noreply.gitlab-master.nvidia.com> Co-authored-by: Robin Dong Co-authored-by: Elena Rastorgueva <80532067+erastorgueva-nv@users.noreply.github.com> Co-authored-by: Igor Gitman Co-authored-by: Jan Lasek Co-authored-by: Dmytro Pykhtar <37850217+dimapihtar@users.noreply.github.com> Co-authored-by: George <37293288+Jorjeous@users.noreply.github.com> Co-authored-by: Sangkug Lym Co-authored-by: Jason Co-authored-by: Mehadi Hasan Menon Co-authored-by: Eric Harper Co-authored-by: anteju <108555623+anteju@users.noreply.github.com> Co-authored-by: Mingyuan Ma Co-authored-by: Ao Tang Co-authored-by: Yu Yao Co-authored-by: Ahmad Kiswani Co-authored-by: Bobby Chen Co-authored-by: Aleksandr Laptev Co-authored-by: Sasha Meister <117230141+ssh-meister@users.noreply.github.com> Co-authored-by: Maanu Grover <109391026+maanug-nv@users.noreply.github.com> Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com> Co-authored-by: Abhinav Khattar Co-authored-by: JimmyZhang12 <67203904+JimmyZhang12@users.noreply.github.com> Co-authored-by: Jimmy Zhang Co-authored-by: PeganovAnton Co-authored-by: Nikolay Karpov Co-authored-by: Samuele Cornell Co-authored-by: Yang Zhang Co-authored-by: Maxime Burchi <60737204+burchim@users.noreply.github.com> Co-authored-by: Igor Gitman Co-authored-by: Kelvin Liu Co-authored-by: Hongbin Liu Co-authored-by: Tamerlan Tabolov Co-authored-by: Gerald Shen <119401249+gshennvm@users.noreply.github.com> Co-authored-by: Stas Bekman Co-authored-by: Jocelyn Co-authored-by: Evelina <10428420+ekmb@users.noreply.github.com> Co-authored-by: Yi Dong <43824965+yidong72@users.noreply.github.com> Co-authored-by: jbaczek <45043825+jbaczek@users.noreply.github.com> Co-authored-by: mikolajblaz Co-authored-by: Seonghun Noh Co-authored-by: He Huang (Steve) <105218074+stevehuang52@users.noreply.github.com> Co-authored-by: Szymon Mikler --- Jenkinsfile | 273 ++++++++++++++- docs/source/conf.py | 5 + docs/source/index.rst | 19 +- docs/source/multimodal/api.rst | 81 +++++ docs/source/multimodal/mllm/checkpoint.rst | 114 +++++++ docs/source/multimodal/mllm/configs.rst | 143 ++++++++ docs/source/multimodal/mllm/datasets.rst | 99 ++++++ .../multimodal/mllm/images/llava_arch.jpg | Bin 0 -> 117431 bytes docs/source/multimodal/mllm/intro.rst | 111 +++++++ docs/source/multimodal/mllm/neva.rst | 160 +++++++++ docs/source/multimodal/mm_all.bib | 206 ++++++++++++ docs/source/multimodal/nerf/configs.rst | 142 ++++++++ docs/source/multimodal/nerf/datasets.rst | 81 +++++ docs/source/multimodal/nerf/dreamfusion.rst | 310 ++++++++++++++++++ .../images/dreamfusion_model_overview.png | Bin 0 -> 1653618 bytes docs/source/multimodal/nerf/intro.rst | 55 ++++ .../source/multimodal/text2img/checkpoint.rst | 80 +++++ docs/source/multimodal/text2img/configs.rst | 166 ++++++++++ .../source/multimodal/text2img/controlnet.rst | 106 ++++++ docs/source/multimodal/text2img/datasets.rst | 40 +++ .../source/multimodal/text2img/dreambooth.rst | 132 ++++++++ docs/source/multimodal/text2img/imagen.rst | 287 ++++++++++++++++ .../text2img/images/controlnet-structure.png | Bin 0 -> 59531 bytes .../text2img/images/imagen_arch.png | Bin 0 -> 492447 bytes docs/source/multimodal/text2img/insp2p.rst | 84 +++++ docs/source/multimodal/text2img/intro.rst | 99 ++++++ docs/source/multimodal/text2img/sd.rst | 170 ++++++++++ docs/source/multimodal/vlm/checkpoint.rst | 92 ++++++ docs/source/multimodal/vlm/clip.rst | 156 +++++++++ docs/source/multimodal/vlm/configs.rst | 160 +++++++++ docs/source/multimodal/vlm/datasets.rst | 35 ++ .../multimodal/vlm/images/clip_arch.png | Bin 0 -> 252444 bytes docs/source/multimodal/vlm/intro.rst | 82 +++++ docs/source/vision/checkpoint.rst | 77 +++++ docs/source/vision/configs.rst | 134 ++++++++ docs/source/vision/datasets.rst | 45 +++ docs/source/vision/images/vit_arch.png | Bin 0 -> 114139 bytes docs/source/vision/intro.rst | 45 +++ docs/source/vision/vision_all.bib | 17 + docs/source/vision/vit.rst | 136 ++++++++ examples/multimodal/convert_ckpt_to_nemo.py | 3 +- .../controlnet/conf/controlnet_v1-5.yaml | 6 +- .../controlnet/controlnet_train.py | 2 +- .../dreambooth/conf/dreambooth.yaml | 6 +- .../text_to_image/dreambooth/dreambooth.py | 2 +- .../text_to_image/imagen/imagen_training.py | 2 +- .../stable_diffusion/conf/sd_train.yaml | 9 +- nemo/README.md | 1 + .../models/multimodal_llm/neva/neva_model.py | 1 - .../stable_diffusion/ldm/autoencoder.py | 6 +- .../modules/nerf/geometry/nerf_base.py | 39 +-- .../diffusionmodules/openaimodel.py | 18 + .../parts/stable_diffusion/lr_scheduler.py | 112 +++++++ .../parts/stable_diffusion/utils.py | 14 +- requirements/requirements_multimodal.txt | 16 + setup.py | 4 + 56 files changed, 4130 insertions(+), 53 deletions(-) create mode 100644 docs/source/multimodal/api.rst create mode 100644 docs/source/multimodal/mllm/checkpoint.rst create mode 100644 docs/source/multimodal/mllm/configs.rst create mode 100644 docs/source/multimodal/mllm/datasets.rst create mode 100644 docs/source/multimodal/mllm/images/llava_arch.jpg create mode 100644 docs/source/multimodal/mllm/intro.rst create mode 100644 docs/source/multimodal/mllm/neva.rst create mode 100644 docs/source/multimodal/mm_all.bib create mode 100644 docs/source/multimodal/nerf/configs.rst create mode 100644 docs/source/multimodal/nerf/datasets.rst create mode 100644 docs/source/multimodal/nerf/dreamfusion.rst create mode 100644 docs/source/multimodal/nerf/images/dreamfusion_model_overview.png create mode 100644 docs/source/multimodal/nerf/intro.rst create mode 100644 docs/source/multimodal/text2img/checkpoint.rst create mode 100644 docs/source/multimodal/text2img/configs.rst create mode 100644 docs/source/multimodal/text2img/controlnet.rst create mode 100644 docs/source/multimodal/text2img/datasets.rst create mode 100644 docs/source/multimodal/text2img/dreambooth.rst create mode 100644 docs/source/multimodal/text2img/imagen.rst create mode 100644 docs/source/multimodal/text2img/images/controlnet-structure.png create mode 100644 docs/source/multimodal/text2img/images/imagen_arch.png create mode 100644 docs/source/multimodal/text2img/insp2p.rst create mode 100644 docs/source/multimodal/text2img/intro.rst create mode 100644 docs/source/multimodal/text2img/sd.rst create mode 100644 docs/source/multimodal/vlm/checkpoint.rst create mode 100644 docs/source/multimodal/vlm/clip.rst create mode 100644 docs/source/multimodal/vlm/configs.rst create mode 100644 docs/source/multimodal/vlm/datasets.rst create mode 100644 docs/source/multimodal/vlm/images/clip_arch.png create mode 100644 docs/source/multimodal/vlm/intro.rst create mode 100644 docs/source/vision/checkpoint.rst create mode 100644 docs/source/vision/configs.rst create mode 100644 docs/source/vision/datasets.rst create mode 100644 docs/source/vision/images/vit_arch.png create mode 100644 docs/source/vision/intro.rst create mode 100644 docs/source/vision/vision_all.bib create mode 100644 docs/source/vision/vit.rst create mode 100644 nemo/collections/multimodal/parts/stable_diffusion/lr_scheduler.py create mode 100644 requirements/requirements_multimodal.txt diff --git a/Jenkinsfile b/Jenkinsfile index 8b88ba9fb1ac..d1c0c103509d 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -126,6 +126,256 @@ pipeline { sh 'CUDA_VISIBLE_DEVICES="" NEMO_NUMBA_MINVER=0.53 pytest -m "not pleasefixme" --cpu --with_downloads --relax_numba_compat' } } +// +// stage('L2: Multimodal Imagen Train') { +// when { +// anyOf { +// branch 'main' +// changeRequest target: 'main' +// } +// } +// failFast true +// steps { +// sh "rm -rf /home/TestData/multimodal/imagen_train" +// sh "pip install webdataset==0.2.48" +// sh "python examples/multimodal/text_to_image/imagen/imagen_training.py \ +// trainer.precision=16 \ +// trainer.num_nodes=1 \ +// trainer.devices=1 \ +// ++exp_manager.max_time_per_run=00:00:03:00 \ +// trainer.max_steps=20 \ +// model.micro_batch_size=1 \ +// model.global_batch_size=1 \ +// model.data.synthetic_data=True \ +// exp_manager.exp_dir=/home/TestData/multimodal/imagen_train \ +// model.inductor=False \ +// model.unet.flash_attention=False \ +// " +// sh "pip install 'webdataset>=0.1.48,<=0.1.62'" +// sh "rm -rf /home/TestData/multimodal/imagen_train" +// } +// } +// +// stage('L2: Multimodal Stable Diffusion Train') { +// when { +// anyOf { +// branch 'main' +// changeRequest target: 'main' +// } +// } +// failFast true +// steps { +// sh "rm -rf /home/TestData/multimodal/stable_diffusion_train" +// sh "pip install webdataset==0.2.48" +// sh "python examples/multimodal/text_to_image/stable_diffusion/sd_train.py \ +// trainer.precision=16 \ +// trainer.num_nodes=1 \ +// trainer.devices=1 \ +// ++exp_manager.max_time_per_run=00:00:03:00 \ +// trainer.max_steps=20 \ +// model.micro_batch_size=1 \ +// model.global_batch_size=1 \ +// model.data.synthetic_data=True \ +// exp_manager.exp_dir=/home/TestData/multimodal/stable_diffusion_train \ +// model.inductor=False \ +// model.cond_stage_config._target_=nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder \ +// ++model.cond_stage_config.version=openai/clip-vit-large-patch14 \ +// ++model.cond_stage_config.max_length=77 \ +// ~model.cond_stage_config.restore_from_path \ +// ~model.cond_stage_config.freeze \ +// ~model.cond_stage_config.layer \ +// model.unet_config.from_pretrained=null \ +// model.first_stage_config.from_pretrained=null \ +// model.unet_config.use_flash_attention=False \ +// " +// sh "pip install 'webdataset>=0.1.48,<=0.1.62'" +// sh "rm -rf /home/TestData/multimodal/stable_diffusion_train" +// } +// } +// stage('L2: Multimodal ControlNet Train') { +// when { +// anyOf { +// branch 'main' +// changeRequest target: 'main' +// } +// } +// failFast true +// steps { +// sh "rm -rf /home/TestData/multimodal/controlnet_train" +// sh "pip install webdataset==0.2.48" +// sh "python examples/multimodal/text_to_image/controlnet/controlnet_train.py \ +// trainer.precision=16 \ +// trainer.num_nodes=1 \ +// trainer.devices=1 \ +// ++exp_manager.max_time_per_run=00:00:03:00 \ +// trainer.max_steps=20 \ +// model.micro_batch_size=1 \ +// model.global_batch_size=1 \ +// model.data.synthetic_data=True \ +// exp_manager.exp_dir=/home/TestData/multimodal/controlnet_train \ +// model.inductor=False \ +// model.image_logger.max_images=0 \ +// model.control_stage_config.params.from_pretrained_unet=null \ +// model.unet_config.from_pretrained=null \ +// model.first_stage_config.from_pretrained=null \ +// model.unet_config.use_flash_attention=False \ +// " +// sh "pip install 'webdataset>=0.1.48,<=0.1.62'" +// sh "rm -rf /home/TestData/multimodal/controlnet_train" +// } +// } +// stage('L2: Multimodal DreamBooth Train') { +// when { +// anyOf { +// branch 'main' +// changeRequest target: 'main' +// } +// } +// failFast true +// steps { +// sh "rm -rf /home/TestData/multimodal/dreambooth_train" +// sh "pip install webdataset==0.2.48" +// sh "python examples/multimodal/text_to_image/dreambooth/dreambooth.py \ +// trainer.precision=16 \ +// trainer.num_nodes=1 \ +// trainer.devices=1 \ +// ++exp_manager.max_time_per_run=00:00:03:00 \ +// trainer.max_steps=20 \ +// model.micro_batch_size=1 \ +// model.global_batch_size=1 \ +// exp_manager.exp_dir=/home/TestData/multimodal/dreambooth_train \ +// model.inductor=False \ +// model.cond_stage_config._target_=nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder \ +// ++model.cond_stage_config.version=openai/clip-vit-large-patch14 \ +// ++model.cond_stage_config.max_length=77 \ +// ~model.cond_stage_config.restore_from_path \ +// ~model.cond_stage_config.freeze \ +// ~model.cond_stage_config.layer \ +// model.unet_config.from_pretrained=null \ +// model.first_stage_config.from_pretrained=null \ +// model.data.instance_dir=/home/TestData/multimodal/tiny-dreambooth \ +// model.unet_config.use_flash_attention=False \ +// " +// sh "pip install 'webdataset>=0.1.48,<=0.1.62'" +// sh "rm -rf /home/TestData/multimodal/dreambooth_train" +// } +// } +// stage('L2: Vision ViT Pretrain TP=1') { +// when { +// anyOf { +// branch 'main' +// changeRequest target: 'main' +// } +// } +// failFast true +// steps { +// sh "rm -rf /home/TestData/vision/vit_pretrain_tp1" +// sh "pip install webdataset==0.2.48" +// sh "python examples/vision/vision_transformer/megatron_vit_classification_pretrain.py \ +// trainer.precision=16 \ +// model.megatron_amp_O2=False \ +// trainer.num_nodes=1 \ +// trainer.devices=1 \ +// trainer.val_check_interval=5 \ +// ++exp_manager.max_time_per_run=00:00:03:00 \ +// trainer.max_steps=20 \ +// model.micro_batch_size=2 \ +// model.global_batch_size=4 \ +// model.tensor_model_parallel_size=1 \ +// model.pipeline_model_parallel_size=1 \ +// model.data.num_workers=0 \ +// exp_manager.create_checkpoint_callback=False \ +// model.data.data_path=[/home/TestData/multimodal/tiny-imagenet/train,/home/TestData/multimodal/tiny-imagenet/val] \ +// exp_manager.exp_dir=/home/TestData/vision/vit_pretrain_tp1 " +// sh "pip install 'webdataset>=0.1.48,<=0.1.62'" +// sh "rm -rf /home/TestData/vision/vit_pretrain_tp1" +// } +// } +// +// stage('L2: Multimodal CLIP Pretrain TP=1') { +// when { +// anyOf { +// branch 'main' +// changeRequest target: 'main' +// } +// } +// failFast true +// steps { +// sh "rm -rf /home/TestData/multimodal/clip_pretrain_tp1" +// sh "pip install webdataset==0.2.48" +// sh "python examples/multimodal/vision_language_foundation/clip/megatron_clip_pretrain.py \ +// trainer.precision=16 \ +// model.megatron_amp_O2=False \ +// trainer.num_nodes=1 \ +// trainer.devices=1 \ +// trainer.val_check_interval=10 \ +// ++exp_manager.max_time_per_run=00:00:03:00 \ +// trainer.max_steps=20 \ +// model.micro_batch_size=1 \ +// model.global_batch_size=1 \ +// model.tensor_model_parallel_size=1 \ +// model.pipeline_model_parallel_size=1 \ +// exp_manager.create_checkpoint_callback=False \ +// model.data.num_workers=0 \ +// model.vision.num_layers=2 \ +// model.text.num_layers=2 \ +// model.vision.patch_dim=32 \ +// model.vision.encoder_seq_length=49 \ +// model.vision.class_token_length=7 \ +// model.data.train.dataset_path=[/home/TestData/multimodal/tiny-clip/00000.tar] \ +// model.data.validation.dataset_path=[/home/TestData/multimodal/tiny-clip/00000.tar] \ +// model.data.webdataset.local_root_path=/ \ +// exp_manager.exp_dir=/home/TestData/multimodal/clip_pretrain_tp1 " +// sh "pip install 'webdataset>=0.1.48,<=0.1.62'" +// sh "rm -rf /home/TestData/multimodal/clip_pretrain_tp1" +// } +// } +// +// stage('L2: Multimodal NeVA Pretrain TP=1') { +// when { +// anyOf { +// branch 'main' +// changeRequest target: 'main' +// } +// } +// failFast true +// steps { +// sh "rm -rf /home/TestData/multimodal/neva_pretrain_tp1" +// sh "pip install webdataset==0.2.48" +// sh "python examples/multimodal/multimodal_llm/neva/neva_pretrain.py \ +// trainer.precision=bf16 \ +// model.megatron_amp_O2=False \ +// trainer.num_nodes=1 \ +// trainer.devices=1 \ +// trainer.val_check_interval=10 \ +// trainer.limit_val_batches=5 \ +// trainer.log_every_n_steps=1 \ +// ++exp_manager.max_time_per_run=00:00:03:00 \ +// trainer.max_steps=20 \ +// model.micro_batch_size=2 \ +// model.global_batch_size=4 \ +// model.tensor_model_parallel_size=1 \ +// model.pipeline_model_parallel_size=1 \ +// exp_manager.create_checkpoint_callback=False \ +// model.data.data_path=/home/TestData/multimodal/tiny-neva/dummy.json \ +// model.data.image_folder=/home/TestData/multimodal/tiny-neva/images \ +// model.tokenizer.library=sentencepiece \ +// model.tokenizer.model=/home/TestData/multimodal/tiny-neva/tokenizer_add_special.model \ +// model.num_layers=2 \ +// model.hidden_size=5120 \ +// model.ffn_hidden_size=13824 \ +// model.num_attention_heads=40 \ +// model.normalization=rmsnorm \ +// model.data.num_workers=0 \ +// model.data.conv_template=llama_2 \ +// model.mm_cfg.vision_encoder.from_pretrained='openai/clip-vit-large-patch14' \ +// model.mm_cfg.llm.from_pretrained=null \ +// model.use_flash_attention=false \ +// exp_manager.exp_dir=/home/TestData/multimodal/neva_pretrain_tp1 " +// sh "pip install 'webdataset>=0.1.48,<=0.1.62'" +// sh "rm -rf /home/TestData/multimodal/neva_pretrain_tp1" +// } +// } // TODO: this requires TE >= v0.11 which is not available in 23.06. // please uncomment this test once mcore CI is ready. @@ -4826,6 +5076,7 @@ assert_frame_equal(training_curve, gt_curve, rtol=1e-3, atol=1e-3)"''' } } } + stage('L2: TTS Fast dev runs 1') { when { anyOf { @@ -4971,7 +5222,27 @@ assert_frame_equal(training_curve, gt_curve, rtol=1e-3, atol=1e-3)"''' } } } - + stage('L2: NeRF') { + when { + anyOf { + branch 'r1.21.0' + changeRequest target: 'r1.21.0' + } + } + parallel { + stage('DreamFusion') { + steps { + sh 'python examples/multimodal/text_to_image/nerf/main.py \ + trainer.num_nodes=1 \ + trainer.devices="[0]" \ + trainer.max_steps=1000 \ + model.prompt="a DSLR photo of a delicious hamburger" \ + exp_manager.exp_dir=examples/multimodal/text_to_image/nerf/dreamfusion_results' + sh 'rm -rf examples/multimodal/text_to_image/nerf/dreamfusion_results' + } + } + } + } stage('L??: Speech Checkpoints tests') { when { anyOf { diff --git a/docs/source/conf.py b/docs/source/conf.py index 952e25332ca4..586f6cf47675 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -61,6 +61,9 @@ 'ipadic', 'psutil', 'regex', + 'PIL', + 'boto3', + 'taming', ] _skipped_autodoc_mock_imports = ['wrapt', 'numpy'] @@ -125,6 +128,8 @@ 'tts/tts_all.bib', 'text_processing/text_processing_all.bib', 'core/adapters/adapter_bib.bib', + 'multimodal/mm_all.bib', + 'vision/vision_all.bib', ] intersphinx_mapping = { diff --git a/docs/source/index.rst b/docs/source/index.rst index 86ad55d1709b..7407886eefc8 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -47,7 +47,7 @@ NVIDIA NeMo User Guide nlp/api nlp/megatron_onnx_export nlp/models - + .. toctree:: :maxdepth: 1 @@ -71,6 +71,23 @@ NVIDIA NeMo User Guide text_processing/g2p/g2p common/intro +.. toctree:: + :maxdepth: 3 + :caption: Multimodal (MM) + :name: Multimodal + + multimodal/mllm/intro + multimodal/vlm/intro + multimodal/text2img/intro + multimodal/nerf/intro + multimodal/api + +.. toctree:: + :maxdepth: 2 + :caption: Vision + :name: vision + + vision/intro .. toctree:: :maxdepth: 3 diff --git a/docs/source/multimodal/api.rst b/docs/source/multimodal/api.rst new file mode 100644 index 000000000000..ef517d6bdd5a --- /dev/null +++ b/docs/source/multimodal/api.rst @@ -0,0 +1,81 @@ +NeMo Megatron API +======================= + +Model Classes +------------- + +.. autoclass:: nemo.collections.nlp.models.language_modeling.megatron_base_model.MegatronBaseModel + :show-inheritance: + :no-members: + :members: __init__, configure_optimizers + + +.. autoclass:: nemo.collections.multimodal.models.stable_diffusion.ldm.ddpm.MegatronLatentDiffusion + :show-inheritance: + :no-members: + :members: __init__, training_step, validation_step, setup, build_train_valid_test_datasets + + +.. autoclass:: nemo.collections.multimodal.models.dreambooth.dreambooth.MegatronDreamBooth + :show-inheritance: + :no-members: + :members: __init__, training_step, validation_step, setup, build_train_valid_test_datasets + + +.. autoclass:: nemo.collections.multimodal.models.controlnet.controlnet.MegatronControlNet + :show-inheritance: + :no-members: + :members: __init__, training_step, validation_step, setup, build_train_valid_test_datasets + +.. autoclass:: nemo.collections.multimodal.models.imagen.imagen.MegatronImagen + :show-inheritance: + :no-members: + :members: __init__, training_step, validation_step, setup, build_train_valid_test_datasets + + + +Modules +------- + +.. autoclass:: nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.openaimodel.UNetModel + :show-inheritance: + :no-members: + +.. autoclass:: nemo.collections.multimodal.modules.imagen.diffusionmodules.nets.UNetModel + :show-inheritance: + :no-members: + +.. autoclass:: nemo.collections.multimodal.modules.imagen.diffusionmodules.nets.EfficientUNetModel + :show-inheritance: + :no-members: + +.. autoclass:: nemo.collections.multimodal.models.stable_diffusion.ldm.autoencoder.AutoencoderKL + :show-inheritance: + :no-members: + :members: __init__, encode, decode + +.. autoclass:: nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenMegatronCLIPEmbedder + :show-inheritance: + :no-members: + :members: __init__, forward + +.. autoclass:: nemo.collections.multimodal.modules.imagen.encoder.t5encoder.T5Encoder + :show-inheritance: + :no-members: + :members: __init__, encode + + +.. autoclass:: nemo.collections.multimodal.models.controlnet.controlnet.ControlledUnetModel + :show-inheritance: + :no-members: + :members: forward + +Datasets +--------- + +.. autoclass:: nemo.collections.multimodal.data.common.webdataset.WebDatasetCommon + :show-inheritance: + +.. autoclass:: nemo.collections.multimodal.data.dreambooth.dreambooth_dataset.DreamBoothDataset + :show-inheritance: + diff --git a/docs/source/multimodal/mllm/checkpoint.rst b/docs/source/multimodal/mllm/checkpoint.rst new file mode 100644 index 000000000000..8ccb520bda4b --- /dev/null +++ b/docs/source/multimodal/mllm/checkpoint.rst @@ -0,0 +1,114 @@ +Checkpoints +=========== + +In this section, we present four key functionalities of NVIDIA NeMo related to checkpoint management: + +1. **Checkpoint Loading**: Load local ``.nemo`` checkpoint files with the :code:`restore_from()` method. +2. **Partial Checkpoint Conversion**: Convert partially-trained ``.ckpt`` checkpoints to the ``.nemo`` format. +3. **Community Checkpoint Conversion**: Transition checkpoints from community sources, like HuggingFace, into the ``.nemo`` format. +4. **Model Parallelism Adjustment**: Modify model parallelism to efficiently train models that exceed the memory of a single GPU. NeMo employs both tensor (intra-layer) and pipeline (inter-layer) model parallelisms. Dive deeper with "Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM" (`link `_). This tool aids in adjusting model parallelism, accommodating users who need to deploy on larger GPU arrays due to memory constraints. + +Understanding Checkpoint Formats +-------------------------------- + +A ``.nemo`` checkpoint is fundamentally a tar file that bundles the model configurations (given as a YAML file), model weights, and other pertinent artifacts like tokenizer models or vocabulary files. This consolidated design streamlines sharing, loading, tuning, evaluating, and inference. + +On the other hand, the ``.ckpt`` file is a product of PyTorch Lightning training. It stores model weights and optimizer states, and it's generally used for resuming training. + +Subsequent sections delve into each of the previously listed functionalities, emphasizing the loading of fully trained checkpoints for evaluation or additional fine-tuning. + + +Loading Local Checkpoints +------------------------- + +NeMo inherently saves any model's checkpoints in the ``.nemo`` format. To manually save a model at any stage: + +.. code-block:: python + + model.save_to(.nemo) + +To load a local ``.nemo`` checkpoint: + +.. code-block:: python + + import nemo.collections.multimodal as nemo_multimodal + model = nemo_multimodal.models..restore_from(restore_path="") + +Replace `` with the appropriate MM model class. + +Converting Local Checkpoints +---------------------------- + +The training script only auto-converts the final checkpoint into the ``.nemo`` format. To evaluate intermediate training checkpoints, conversion to ``.nemo`` might be needed. For this: + +.. code-block:: python + + python -m torch.distributed.launch --nproc_per_node= * \ + examples/multimodal/convert_ckpt_to_nemo.py \ + --checkpoint_folder \ + --checkpoint_name \ + --nemo_file_path \ + --tensor_model_parallel_size \ + --pipeline_model_parallel_size + +Converting Community Checkpoints +-------------------------------- + +NeVA Checkpoints +^^^^^^^^^^^^^^^^ + +Currently, the conversion mainly supports LLaVA checkpoints based on "llama-2 chat" checkpoints. As a reference, we'll consider the checkpoint `llava-llama-2-13b-chat-lightning-preview `_. + +After downloading this checkpoint and saving it at `/path/to/llava-llama-2-13b-chat-lightning-preview`, undertake the following procedures: + +Modifying the Tokenizer +""""""""""""""""""""""" + +NeMo mandates adding specific tokens to the tokenizer model for peak performance. To modify an existing tokenizer located in `/path/to/llava-llama-2-13b-chat-lightning-preview/tokenizer`, execute the following in the NeMo container: + +.. code-block:: bash + + cd /opt/sentencepiece/src/ + protoc --python_out=/opt/NeMo/scripts/tokenizers/ sentencepiece_model.proto + python /opt/NeMo/scripts/tokenizers/add_special_tokens_to_sentencepiece.py \ + --input_file /path/to/llava-llama-2-13b-chat-lightning-preview/tokenizer.model \ + --output_file /path/to/llava-llama-2-13b-chat-lightning-preview/tokenizer_neva.model \ + --is_userdefined \ + --tokens "" "" "" "" \ + "" "" "" "" + +Checkpoint Conversion +""""""""""""""""""""" + +For conversion: + +.. code-block:: python + + python examples/multimodal/mllm/neva/convert_hf_llava_to_neva.py \ + --in-file /path/to/llava-llama-2-13b-chat-lightning-preview \ + --out-file /path/to/neva-llava-llama-2-13b-chat-lightning-preview.nemo \ + --tokenizer-model /path/to/llava-llama-2-13b-chat-lightning-preview/tokenizer_add_special.model + --conv-template llama_2 + + +Model Parallelism Adjustment +------------------------- + +NeVA Checkpoints +^^^^^^^^^^^^^^^^ + +Adjust model parallelism with: + +.. code-block:: python + + python examples/nlp/language_modeling/megatron_change_num_partitions.py \ + --model_file=/path/to/source.nemo \ + --target_file=/path/to/target.nemo \ + --tensor_model_parallel_size=??? \ + --target_tensor_model_parallel_size=??? \ + --pipeline_model_parallel_size=??? \ + --target_pipeline_model_parallel_size=??? \ + --model_class="nemo.collections.multimodal.models.neva.neva_model.MegatronNevaModel" \ + --precision=32 \ + --tokenizer_model_path=/path/to/tokenizer.model \ + --tp_conversion_only diff --git a/docs/source/multimodal/mllm/configs.rst b/docs/source/multimodal/mllm/configs.rst new file mode 100644 index 000000000000..38ee65da9dd3 --- /dev/null +++ b/docs/source/multimodal/mllm/configs.rst @@ -0,0 +1,143 @@ +Common Configuration Files +========================== + +This section provides a detailed overview of the NeMo configuration file setup specific to models within the NeMo Multimodal Language Model collection. For foundational knowledge about setting up and executing experiments common to all NeMo models, such as the Experiment Manager and PyTorch Lightning trainer parameters, refer to the :doc:`../core/core` section. + +Within the configuration files of the NeMo Multimodal Language Model, details concerning dataset(s), augmentation, optimization parameters, and model architectural specifications are central. This page explores each of these aspects. + +Discover exemplary configuration files for all NeMo Multimodal Language Model scripts in the `config directory of the examples `_. + +Dataset Configuration +--------------------- + +The NeMo multimodal language model currently supports a conversation data format, inspired by and designed from https://github.com/haotian-liu/LLaVA/tree/main. To explore a sample dataset, visit https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md. + +The configuration file allows setting any initialization parameter accepted by the Dataset class used in the experiment. For a comprehensive list of Datasets and their parameters, visit the `Datasets <./api.html#Datasets>`__ section of the API. + +A typical training configuration is as follows: + +.. code-block:: yaml + + data: + num_workers: 8 + dataloader_type: cyclic + data_path: path/to/conversations.json + lazy_preprocess: True + is_multimodal: True + conv_template: llama_2 + image_token_len: 256 + image_folder: path/to/images + image_aspect_ratio: 'square' + +Key parameters include: + +- ``data_path``: The path to the dataset in JSON format. +- ``is_multimodal``: Indicates if the dataset has multiple modalities (e.g., text and images). +- ``conv_template``: The template used for conversation format. Supports values like 'nvgpt' and 'llama_2'. +- ``image_token_len``: Specifies how many tokens in the language model word embedding each image will occupy. +- ``image_folder``: The path to the folder containing images related to the dataset. +- ``image_aspect_ratio``: Specifies whether to pad or crop the image to maintain the aspect ratio, such as 'square'. + +Trainer Configuration +--------------------- + +This section outlines arguments for the Pytorch Lightning Trainer Object. + +.. code-block:: yaml + + trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + +For a detailed list of arguments, refer to the `Pytorch Lightning Trainer `__ API section. + +Experiment Manager Configurations +--------------------------------- + +The NeMo Experiment Manager provides a streamlined approach to manage various tasks such as logging, saving, and resuming. + +.. code-block:: yaml + + exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: True + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: training-session + project: text2img + group: nemo + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a modelcheckpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + +Optimizer Configurations +------------------------- + +.. code-block:: yaml + + optim: + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null + +The default optimizer used is ``fused_adam``. For details on all supported optimizers, refer to the NeMo user guide. The learning rate scheduler can be specified in the ``optim.sched`` section. + +Model Configurations +-------------------- + +Each configuration file should detail the model architecture used for the experiment. + +The parameters commonly shared across most multimodal language models include: + ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| **Parameter** | **Datatype** | **Description** | ++===========================+==============+=======================================================================================+ +| :code:`micro_batch_size` | int | micro batch size that fits on each GPU | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| :code:`global_batch_size` | int | global batch size that takes consideration of gradient accumulation, data parallelism | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| :code:`tensor_model_parallel_size` | int | intra-layer model parallelism | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| :code:`pipeline_model_parallel_size` | int | inter-layer model parallelism | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| :code:`seed` | int | seed used in training | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ + +NeVA +~~~~~~~~ + +For model-specific configurations, refer to `Neva <./neva.html#neva>`_. diff --git a/docs/source/multimodal/mllm/datasets.rst b/docs/source/multimodal/mllm/datasets.rst new file mode 100644 index 000000000000..1c64c4d317d2 --- /dev/null +++ b/docs/source/multimodal/mllm/datasets.rst @@ -0,0 +1,99 @@ +Multimodal Language Model Datasets +================================== + +The NeMo multimodal language model supports the conversation data format, drawing inspiration from and designed based on `LLaVA `_. Sample datasets can be explored at `LLaVA's data documentation `_. + +Preparing the Training Dataset +------------------------------ + +The NeVA model training encompasses two phases: pretraining and finetuning. Each phase mandates a unique dataset. + +For **pretraining**, utilize the *LAION/CC/SBU BLIP-Caption Concept-balanced 558K* dataset. Access this dataset via `LLaVA's GitHub `_. After procuring the dataset, extract it to: + +.. code-block:: bash + + /path/to/neva/datasets/LLaVA-Pretrain-LCS-558K/blip_laion_cc_sbu_558k.json + +Acquire the image data from `HuggingFace `_ and extract to: + +.. code-block:: bash + + /path/to/neva/datasets/LLaVA-Pretrain-LCS-558K/images + +For **fine-tuning**, deploy the *LLaVA-Instruct-150K* dataset. This is also available on `LLaVA's GitHub `_. You can download the prompts from `HuggingFace `_: + +.. code-block:: bash + + /path/to/neva/datasets/LLaVA-Instruct-150K/ + +Image data for this phase can be obtained from the `COCO Dataset `_. Once downloaded, extract the images to: + +.. code-block:: bash + + /path/to/neva/datasets/LLaVA-Instruct-150K/images + +Additional Preparation for NeVA Model +------------------------------------- + +The following instructions are specific to the NeVA model within the NeMo Multimodal Language Models. + +Setting Up LLaMA-2 Chat Checkpoints +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Support is available for both the 7B and 13B chat models. Both can be downloaded from `LLaVA's Model Zoo `_. After downloading the desired HuggingFace checkpoint, extract and store it on your local system to prep for pretraining. + +To convert the LLaMA-2 checkpoints to NeMo's format, follow these steps: + +1. Adjust the default yaml file at `megatron_llama_config.yaml `_. Ensure ``model.mcore_gpt`` and ``model.transformer_engine`` are set to `False` before the checkpoint conversion. + +2. For the 7B chat model, use this conversion command: + +.. code-block:: bash + + python /opt/NeMo/scripts/nlp_language_modeling/convert_hf_llama_to_nemo.py \ + --in-file \ + --out-file /path/to/neva/checkpoints/llama-2-7b-chat.nemo + +For the 13B model, adjust the paths in the `--in-file` and `--out-file` parameters accordingly. + +3. Execute the subsequent command to divide the checkpoint for tensor model parallel sizes of 4 or 8. It's advisable to use TP=4 for the 7B model and TP=8 for the 13B model to ensure both pretraining and finetuning operate without memory complications. + +.. code-block:: bash + + # Instructions for the 7B model partitioning provided here. + # Adjust parameters for the 13B model as needed. + python /opt/NeMo/examples/nlp/language_modeling/megatron_change_num_partitions.py \ + --model_file=/path/to/neva/checkpoints/llama-2-7b-chat.nemo \ + --target_file=/path/to/neva/checkpoints/llama-2-7b-chat-tp4.nemo \ + --tensor_model_parallel_size=1 \ + --target_tensor_model_parallel_size=4 \ + --pipeline_model_parallel_size=1 \ + --target_pipeline_model_parallel_size=1 \ + --tp_conversion_only \ + --model_class="nemo.collections.nlp.models.language_modeling.megatron_gpt_model.MegatronGPTModel" \ + --tokenizer_model_path=/tokenizer.model + +Tokenizer Configuration +^^^^^^^^^^^^^^^^^^^^^^^ + +For NeVA training, integrating special tokens into the tokenizer is vital. After obtaining the 7B/13B model from Huggingface, also procure the corresponding tokenizer model. Referring to the 7B-chat model: + +1. Download the `tokenizer.model `_ to: + +.. code-block:: bash + + /path/to/neva/tokenizers/tokenizer.model + +2. Executing the next script necessitates the NeMo dependency. It's more convenient to run the script within the NeMo container. + +3. 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NeMo's implementation of LLaVA is called NeVA. + +- Model Structure: + - Visual Encoder: Utilizes CLIP’s ViT-L/14. + - Text Decoder: Employs LLaMA. + - Connection: A simple linear mapping layer connects the visual encoder's output to the text decoder's word embedding space (v1.0 version). + +- Training: + 1. Cross-modal Pre-training: Utilizes 595k image-text data from CC3M, training only the linear mapping layer while keeping the visual encoder and text decoder frozen. + 2. Instruction Fine-tuning: Custom-built 158k multimodal instruction dataset employed for fine-tuning targeting multimodal chatbot scenarios, with a variant targeting the Science QA dataset. + +Flamingo: A Visual Language Model for Few-Shot Learning +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Flamingo :cite:`mm-models-flamingo` addresses inconsistent visual feature map sizes by generating fixed-length feature sequences, enhancing visual relevance generation. + +- Model Structure: + - Resampler: Utilizes a Perceiver Resampler for generating fixed-length feature sequences. + - Attention: Adds cross-attention layers before each LLM layer to enhance visual relevance generation. + +- Training: + - Dataset: Utilizes data from various datasets like M3W, ALIGN, LTIP, and VTP emphasizing multimodal in-context learning. + +Kosmos-1: Language Is Not All You Need: Aligning Perception with Language Models +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Kosmos-1 :cite:`mm-models-kosmos1` by Microsoft is a Multimodal Large Language Model (MLLM) aimed at melding language, perception, action, and world modeling. + +- Model Structure: + - Core Backbone: Transformer-Based Causal Language Model. + - Architecture: Utilizes MAGNETO, a nuanced Transformer variant. + - Position Encoding: Employs XPOS relative position encoding for long-context modeling. + - Resampler: Employs Flamingo's Perceiver Resampler + +- Training: + - Dataset: Encompasses web-scale multimodal corpora including monomodal, cross-modal paired, and interleaved multimodal data. + - Objective: Focused on next-token prediction to maximize log-likelihood of tokens within examples. + +BLIP-2: Bootstrapping Language-Image Pre-training +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +BLIP-2 :cite:`mm-models-blip2` adopts a two-phase training strategy focusing on learning key visual information and adapting visual encoding structure to LLMs. + +- Model Structure: + - Visual Encoder: Combines a pre-trained image encoder with a Querying Transformer (Q-Former). + - Bridging: The Q-Former acts as the bridge between the image encoder and the Large Language Model (LLM). + +- Training: + 1. Phase 1: Focuses on tasks like Image-Text Contrastive Learning, Image-grounded Text Generation, and Image-Text Matching. + 2. Phase 2: Aims at adapting the visual encoding structure's output to LLMs with language modeling as the training task. + +Mini-GPT4: Enhancing Vision-Language Understanding +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Mini-GPT4 :cite:`mm-models-minigpt4` emphasizes the importance of multimodal instruction data for model performance in multimodal open-ended scenarios. + +- Model Structure: + - Visual Encoder: Employs BLIP2’s ViT and Q-Former. + - Text Decoder: Uses Vicuna (a fine-tuned version of LLaMA). + - Connection: A linear mapping layer projects visual features into text representation space. + +- Training: + 1. Cross-modal Learning: Focuses on learning the relationship between vision and language using data from CC+SBU+LAION datasets. + 2. Fine-tuning: Utilizes a multimodal fine-tuning dataset built using ChatGPT to enhance text descriptions generated in phase 1. + +.. note:: + NeMo Megatron has an Enterprise edition which proffers tools for data preprocessing, hyperparameter tuning, containers, scripts for various clouds, and more. With the Enterprise edition, you also garner deployment tools. Apply for `early access here `_ . + +For more information, see additional sections in the NeMo multimodal language model docs on the left-hand-side menu or in the list below: + +.. toctree:: + :maxdepth: 1 + + datasets + configs + checkpoint + neva + +References +---------- + +.. bibliography:: ../mm_all.bib + :style: plain + :filter: docname in docnames + :labelprefix: MM-MODELS + :keyprefix: mm-models- \ No newline at end of file diff --git a/docs/source/multimodal/mllm/neva.rst b/docs/source/multimodal/mllm/neva.rst new file mode 100644 index 000000000000..9e98bb97ed1d --- /dev/null +++ b/docs/source/multimodal/mllm/neva.rst @@ -0,0 +1,160 @@ +NeVA +==== + +Model Introduction +------------------ + +Emerging from the roots of LLaVA (Large Language and Vision Assistant) :cite:`mm-models-llava`, NeVA stands as a pioneering model in the NeMo Multimodal ecosystem. It adeptly fuses large language-centric models, such as NVGPT or LLaMA, with a vision encoder. The training utilizes machine-generated multimodal language-image instruction-following data. Remarkably, even with a limited dataset, NeVA exhibits profound capabilities in deciphering images and adeptly answering queries about them. Its prowess is especially evident in tasks requiring intricate visual comprehension and instruction-following. Intriguingly, NeVA mirrors the capabilities of advanced multimodal models like GPT-4, even when faced with novel images and instructions. + +Building upon LLaVA's foundational principles, NeVA amplifies its training efficiency by harnessing the NeMo LLM framework's features, including model parallelism, activation checkpointing, AMP O2, Flash Attention, and more. + + .. image:: images/llava_arch.jpg + :align: center + :alt: LLaVA model + :scale: 30% + + +Main Language Model +^^^^^^^^^^^^^^^ + +The original LLaVA model incorporates the LLaMA architecture, renowned for its prowess in open-source, language-only instruction-tuning endeavors. LLaMA refines textual input through a process of tokenization and embedding. To these token embeddings, positional embeddings are integrated, and the combined representation is channeled through multiple transformer layers. The output from the concluding transformer layer, associated with the primary token, is designated as the text representation. + +In NeMo, the text encoder is anchored in the :class:`~nemo.collections.nlp.models.language_modeling.megatron_gpt_model.MegatronGPTModel` class. This class is versatile, supporting not only NVGPT models but also LLaMA, LLaMA-2 and other community models, complete with a checkpoint conversion script. Concurrently, the vision model and projection layers enhance the primary language model's word embedding component. For a comprehensive understanding of the implementation, one can refer to the :class:`~nemo.collections.multimodal.models.multimodal_llm.neva.neva_model.MegatronNevaModel` class. + + +Vision Model +^^^^^^^^^^ + +For visual interpretation, NeVA harnesses the power of the pre-trained CLIP visual encoder, ViT-L/14, recognized for its visual comprehension acumen. Images are first partitioned into standardized patches, for instance, 16x16 pixels. These patches are linearly embedded, forming a flattened vector that subsequently feeds into the transformer. The culmination of the transformer's processing is a unified image representation. In the NeMo framework, the NeVA vision model, anchored on the CLIP visual encoder ViT-L/14, can either be instantiated via the :class:`~nemo.collections.multimodal.models.multimodal_llm.clip.megatron_clip_models.CLIPVisionTransformer` class or initiated through the `transformers` package from Hugging Face. + +Projection and Integration +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The encoder retrieves visual features from images and intertwines them with language embeddings using a modifiable projection matrix. This intricate projection translates visual cues into language embedding tokens, seamlessly merging text and imagery. LLaVA-1.5 :cite:`mm-models-liu2023improvedllava` introduces two pivotal enhancements. The integration of an MLP vision-language connector amplifies the system's prowess. Building on the triumphs of MLPs in self-supervised learning, LLaVA-1.5 undergoes a transformative design shift. Transitioning from a linear to a dual-layer MLP projection markedly bolsters LLaVA-1.5's multimodal faculties, empowering the model to adeptly navigate and synergize language and visual elements. + +Architecture Table +------------------ + ++------------------+---------------+------------+--------------------+-----------------+------------+----------------+--------------------------+ +| Base LLM | Vision Encoder| Projection | Encoder Seq Length | Number of Layers| Hidden Size| FFN Hidden Size| Number of Attention Heads| ++==================+===============+============+====================+=================+============+================+==========================+ +| LLaMA-2-13B-Chat | CLIP-L | Linear | 4096 | 40 | 5120 | 13824 | 40 | ++------------------+---------------+------------+--------------------+-----------------+------------+----------------+--------------------------+ +| LLaMA-2-7B-Chat | CLIP-L | Linear | 4096 | 32 | 4096 | 11008 | 32 | ++------------------+---------------+------------+--------------------+-----------------+------------+----------------+--------------------------+ + +Model Configuration +------------------ + +Multimodal Configuration +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + mm_cfg: + use_im_start_end: False + +- ``use_im_start_end``: If set to `True`, image start and end tokens will be used before and after image embeddings. + +Language Model Configuration within Multimodal +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + mm_cfg: + llm: + from_pretrained: ${data_dir}/neva/checkpoints/llama-2-13b-chat-tp8.nemo + freeze: False + model_type: llama_2 + +- ``from_pretrained``: Path to the pretrained NeMo language model checkpoint. +- ``freeze``: If set to `True`, the model parameters will not be updated during training. +- ``model_type``: Specifies the type of model, either `nvgpt` or `llama_2`. + +Vision Encoder Configuration within Multimodal +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + mm_cfg: + vision_encoder: + from_pretrained: "openai/clip-vit-large-patch14" + from_hf: True + patch_dim: 14 + hidden_size: 1024 + vision_select_layer: -2 + class_token_length: 1 + freeze: True + +- ``from_pretrained``: Path or name of the pretrained vision encoder. +- ``from_hf``: If set to `True`, the model will be loaded from the Hugging Face model hub. +- ``patch_dim``: Size of the patches the image is divided into. +- ``hidden_size``: Dimensionality of the hidden layers. +- ``vision_select_layer``: Specifies which layer to select from the vision model. +- ``class_token_length``: Length of the classification token. + +Main Language Model Configuration +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + mcore_gpt: False + encoder_seq_length: 4096 + position_embedding_type: rope + num_layers: 40 + hidden_size: 5120 + ffn_hidden_size: 13824 + num_attention_heads: 40 + hidden_dropout: 0.0 + attention_dropout: 0.0 + ffn_dropout: 0.0 + normalization: rmsnorm + bias: False + activation: 'fast-swiglu' + +- ``mcore_gpt``: If set to `True`, the GPTModel from `megatron.core` will be used. +- ``encoder_seq_length``: Sequence length for the main language model encoder. +- ``position_embedding_type``: Type of position embedding used. +- ``num_layers``, ``hidden_size``, ``ffn_hidden_size``, ``num_attention_heads``: Parameters defining the architecture of the main language model. The ``ffn_hidden_size`` is typically 4 times the ``hidden_size``. +- ``hidden_dropout``, ``attention_dropout``, ``ffn_dropout``: Dropout probabilities for the hidden state, attention, and feed-forward layers in the transformer respectively. +- ``normalization``: Type of normalization layers used. +- ``bias``: If set to `True`, bias terms will be used in all weight matrices. +- ``activation``: Activation function used in the model. + +Optimizations +^^^^^^^^^^^^^^ + ++------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Feature | Description | To Enable | ++====================================+=========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================+==================================================================================================================================================================================================================+ +| Data parallelism | Dataset is read concurrently across multiple GPUs or nodes, allowing for faster data loading and processing. | Automatically when training on multi GPUs/nodes | ++------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Tensor parallelism | Each tensor is split up into multiple chunks, allowing for horizontal parallelism across GPUs. This technique, known as TensorParallel (TP), distributes the model's tensors across multiple GPUs. During processing, each shard gets processed separately and in parallel on different GPUs, and the results are synced at the end of the step. This approach is inspired by NVIDIA's Megatron implementation. [Reference](https://github.com/NVIDIA/Megatron-LM#distributed-pretraining) | ``model.tensor_model_parallel_size={parallel_size}`` | ++------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Activation Checkpointing | To reduce memory usage, activations of certain layers are cleared and recomputed during a backward pass. This technique is particularly useful for training large models that wouldn't fit in GPU memory using traditional methods. | ``model.activations_checkpoint_granularity=full``, ``model.activations_checkpoint_method=block``, ``model.activations_checkpoint_num_layers={num_layers_to_check}`` | ++------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Selective Activation Checkpointing | Selective granularity version of activation checkpointing. See our paper for details. [Reference](https://arxiv.org/pdf/2205.05198.pdf) | ``model.activations_checkpoint_granularity=selective``, ``model.activations_checkpoint_method=uniform`` | ++------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Bfloat16 Training | Training is conducted in Bfloat16 precision, which offers a balance between the higher precision of FP32 and the memory savings and speed of FP16. | ``trainer.precision=bf16`` | ++------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| BF16 O2 | Enables O2-level automatic mixed precision, optimizing Bfloat16 precision for better performance. | ``model.megatron_amp_O2=True`` | ++------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Flash Attention V2 | FlashAttention is a fast and memory-efficient algorithm to compute exact attention. It speeds up model training and reduces memory requirement by being IO-aware. This approach is particularly useful for large-scale models and is detailed further in the repository linked. [Reference](https://github.com/Dao-AILab/flash-attention) | ``model.use_flash_attention=True`` | ++----------------------------------- +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + +NeVA Training +-------------- + +NeVA's training encompasses two essential stages that enhance its capacity to comprehend user instructions, understand both language and visual content, and generate accurate responses: + +1. Pre-training for Feature Alignment: In this initial stage, NeVA aligns visual and language features to ensure compatibility. +2. Fine-tuning End-to-End: The second training stage focuses on fine-tuning the entire model, end-to-end. While the visual encoder's weights remain unchanged, both the projection layer's pre-trained weights and the LLM's parameters become subjects of adaptation. This fine-tuning can be tailored to different application scenarios, yielding versatile capabilities. + +References +---------- + +.. bibliography:: ../mm_all.bib + :style: plain + :filter: docname in docnames + :labelprefix: MM-MODELS + :keyprefix: mm-models- \ No newline at end of file diff --git a/docs/source/multimodal/mm_all.bib b/docs/source/multimodal/mm_all.bib new file mode 100644 index 000000000000..3930484d71e5 --- /dev/null +++ b/docs/source/multimodal/mm_all.bib @@ -0,0 +1,206 @@ +# Imagen +@inproceedings{saharia2022photorealistic, + title={Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding}, + author={Chitwan Saharia and William Chan and Saurabh Saxena and Lala Li and Jay Whang and Emily Denton and Seyed Kamyar Seyed Ghasemipour and Burcu Karagol Ayan and S. Sara Mahdavi and Rapha Gontijo Lopes and Tim Salimans and Jonathan Ho and David J Fleet and Mohammad Norouzi}, + booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, + year={2022}, + doi={10.48550/arXiv.2205.11487} +} + +# DDPM +@misc{ho2020denoising, + title={Denoising Diffusion Probabilistic Models}, + author={Jonathan Ho and Ajay Jain and Pieter Abbeel}, + year={2020}, + eprint={2006.11239}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} + +# EDM +@misc{karras2022elucidating, + title={Elucidating the Design Space of Diffusion-Based Generative Models}, + author={Tero Karras and Miika Aittala and Timo Aila and Samuli Laine}, + year={2022}, + eprint={2206.00364}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +# Make-A-Scene +@misc{gafni2022makeascene, + title={Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors}, + author={Oran Gafni and Adam Polyak and Oron Ashual and Shelly Sheynin and Devi Parikh and Yaniv Taigman}, + year={2022}, + eprint={2203.13131}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +# Stable Diffusion +@misc{rombach2022highresolution, + title={High-Resolution Image Synthesis with Latent Diffusion Models}, + author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer}, + year={2022}, + eprint={2112.10752}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +# Parti +@misc{yu2022scaling, + title={Scaling Autoregressive Models for Content-Rich Text-to-Image Generation}, + author={Jiahui Yu and Yuanzhong Xu and Jing Yu Koh and Thang Luong and Gunjan Baid and Zirui Wang and Vijay Vasudevan and Alexander Ku and Yinfei Yang and Burcu Karagol Ayan and Ben Hutchinson and Wei Han and Zarana Parekh and Xin Li and Han Zhang and Jason Baldridge and Yonghui Wu}, + year={2022}, + eprint={2206.10789}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +# MUSE +@misc{chang2023muse, + title={Muse: Text-To-Image Generation via Masked Generative Transformers}, + author={Huiwen Chang and Han Zhang and Jarred Barber and AJ Maschinot and Jose Lezama and Lu Jiang and Ming-Hsuan Yang and Kevin Murphy and William T. Freeman and Michael Rubinstein and Yuanzhen Li and Dilip Krishnan}, + year={2023}, + eprint={2301.00704}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +# Ins P2P +@misc{insp2p, + Author = {Tim Brooks and Aleksander Holynski and Alexei A. Efros}, + Title = {InstructPix2Pix: Learning to Follow Image Editing Instructions}, + Year = {2022}, + Eprint = {arXiv:2211.09800}, +} + +# Dream Booth +@misc{ruiz2023dreambooth, + title={DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation}, + author={Nataniel Ruiz and Yuanzhen Li and Varun Jampani and Yael Pritch and Michael Rubinstein and Kfir Aberman}, + year={2023}, + eprint={2208.12242}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +# Control Net +@misc{zhang2023adding, + title={Adding Conditional Control to Text-to-Image Diffusion Models}, + author={Lvmin Zhang and Anyi Rao and Maneesh Agrawala}, + year={2023}, + eprint={2302.05543}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +# LLAva +@misc{llava, + Author = {Haotian Liu and Chunyuan Li and Qingyang Wu and Yong Jae Lee}, + Title = {Visual Instruction Tuning}, + Year = {2023}, + Eprint = {arXiv:2304.08485}, +} + +@misc{liu2023improvedllava, + title={Improved Baselines with Visual Instruction Tuning}, + author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae}, + publisher={arXiv:2310.03744}, + year={2023}, +} + +@misc{minigpt4, + Author = {Deyao Zhu and Jun Chen and Xiaoqian Shen and Xiang Li and Mohamed Elhoseiny}, + Title = {MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models}, + Year = {2023}, + Eprint = {arXiv:2304.10592}, +} + +@misc{flamingo, + Author = {Jean-Baptiste Alayrac and Jeff Donahue and Pauline Luc and Antoine Miech and Iain Barr and Yana Hasson and Karel Lenc and Arthur Mensch and Katie Millican and Malcolm Reynolds and Roman Ring and Eliza Rutherford and Serkan Cabi and Tengda Han and Zhitao Gong and Sina Samangooei and Marianne Monteiro and Jacob Menick and Sebastian Borgeaud and Andrew Brock and Aida Nematzadeh and Sahand Sharifzadeh and Mikolaj Binkowski and Ricardo Barreira and Oriol Vinyals and Andrew Zisserman and Karen Simonyan}, + Title = {Flamingo: a Visual Language Model for Few-Shot Learning}, + Year = {2022}, + Eprint = {arXiv:2204.14198}, +} + +@misc{blip2, + Author = {Junnan Li and Dongxu Li and Silvio Savarese and Steven Hoi}, + Title = {BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models}, + Year = {2023}, + Eprint = {arXiv:2301.12597}, +} + +@misc{kosmos1, + Author = {Shaohan Huang and Li Dong and Wenhui Wang and Yaru Hao and Saksham Singhal and Shuming Ma and Tengchao Lv and Lei Cui and Owais Khan Mohammed and Barun Patra and Qiang Liu and Kriti Aggarwal and Zewen Chi and Johan Bjorck and Vishrav Chaudhary and Subhojit Som and Xia Song and Furu Wei}, + Title = {Language Is Not All You Need: Aligning Perception with Language Models}, + Year = {2023}, + Eprint = {arXiv:2302.14045}, +} + +# DECLIP +@misc{li2021declip, + title={Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm}, + author={Yangguang Li and Feng Liang and Lichen Zhao and Yufeng Cui and Wanli Ouyang and Jing Shao and Fengwei Yu and Junjie Yan}, + year={2021}, + eprint={2110.05208}, + archivePrefix={arXiv}, + primaryClass={cs.CV}, + url={https://ar5iv.org/abs/2110.05208} +} + +# CLIP +@misc{radford2021learning, + title={Learning Transferable Visual Models From Natural Language Supervision}, + author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, + year={2021}, + eprint={2103.00020}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} + +# FLAVA +@inproceedings{singh2022flava, + title={FLAVA: A Foundational Language And Vision Alignment Model}, + author={Amanpreet Singh and Ronghang Hu and Vedanuj Goswami and Guillaume Couairon and Wojciech Galuba and Marcus Rohrbach and Douwe Kiela}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + pages={15638--15650}, + year={2022} +} + +# ControlNet GITHUB +@misc{controlnetgithub, + title={Lllyasviel/controlnet}, + url={https://github.com/lllyasviel/ControlNet}, + journal={GitHub}, + author={Lllyasviel, Zhang}, + year={2023} +} + +#DreamBooth Github +@misc{dreamboothdataset, + title={DreamBooth}, + url={https://github.com/google/dreambooth/tree/main/dataset}, + journal={GitHub}, + author={Google}, + year={2023} +} + +#DreamBooth Paper +@misc{dreamboothpaper, + title={DreamBooth: Fine Tuning Text-to-Image Diffusion Models +for Subject-Driven Generation}, + url={https://arxiv.org/abs/2208.12242}, + author={Nataniel Ruiz and Yuanzhen Li and Varun Jampani and Yael Pritch and Michael Rubinstein and Kfir Aberman}, + year={2022}, + archivePrefix={arXiv} +} + +# DreamFusion paper +@misc{poole2022dreamfusion, + title={DreamFusion: Text-to-3D using 2D Diffusion}, + url={https://arxiv.org/abs/2209.14988}, + author={Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben}, + year={2022}, + archivePrefix={arXiv}, +} \ No newline at end of file diff --git a/docs/source/multimodal/nerf/configs.rst b/docs/source/multimodal/nerf/configs.rst new file mode 100644 index 000000000000..96dac7694f6d --- /dev/null +++ b/docs/source/multimodal/nerf/configs.rst @@ -0,0 +1,142 @@ +Common Configuration Files +============================ + +This section describes the NeMo configuration file setup that is specific to models in the MM NeRF collection. For general information +about how to set up and run experiments that is common to all NeMo models (e.g. Experiment Manager and PyTorch Lightning trainer +parameters), see the `Core Documentation <../../core/core.html>`_ section. + +The model section of the NeMo Multimodal NeRF configuration files generally requires information about the dataset, +the background and/or foreground NeRF networks, renderer and the guidance model being used. The sections on +this page cover each of these in more detail. + +Example configuration files for all of the NeMo Multimodal NeRF scripts can be found in the +config directory of the examples ``{NEMO_ROOT/examples/multimodal/generative/nerf/conf}``. + + +Trainer Configuration +--------------------- + +Trainer configuration specifies the arguments for Pytorch Lightning Trainer Object. + +.. code-block:: yaml + + trainer: + devices: 1 # Number of GPUs for distributed, or the list of the GPUs to use e.g. [0, 1] + num_nodes: 1 # Number of nodes for distributed training + precision: 16 # Use 16 to enable or 32 for FP32 precision + max_steps: 10000 # Number of training steps to perform + accelerator: gpu # accelerator to use, only "gpu" is officially supported + enable_checkpointing: False # Provided by exp_manager + logger: False # Provided by exp_manager + log_every_n_steps: 1 # Interval of logging + val_check_interval: 100 # Interval of validation + accumulate_grad_batches: 1 # Accumulates gradients over k batches before stepping the optimizer. + benchmark: False # Enable the inbuilt cudnn auto-tuner to find the best algorithm to use for your hardware. + enable_model_summary: True # Enable or disable the model summarization + + +Refer to the `Pytorch Lightning Trainer `__ API section +for all possible arguments + + +Experiment Manager Configurations +--------------------------------- + +NeMo Experiment Manager provides convenient way to configure logging, saving, resuming options and more. + +.. code-block:: yaml + + exp_manager: + name: ${name} # The name of the experiment. + exp_dir: /results # Directory of the experiment, if None, defaults to "./nemo_experiments" + create_tensorboard_logger: False # Whether you want exp_manger to create a TensorBoard logger + create_wandb_logger: False # Whether you want exp_manger to create a Wandb logger + wandb_logger_kwargs: # Wandb logger arguments + project: dreamfusion + group: nemo-df + name: ${name} + resume: True + create_checkpoint_callback: True # Whether you want Experiment manager to create a model checkpoint callback + checkpoint_callback_params: # Model checkpoint callback arguments + every_n_epochs: 0 + every_n_train_steps: + monitor: loss + filename: '${name}-{step}' + save_top_k: -1 + always_save_nemo: False + resume_if_exists: True # Whether this experiment is resuming from a previous run + resume_ignore_no_checkpoint: True # Experiment manager errors out if resume_if_exists is True and no checkpoint could be found. This behavior can be disabled, in which case exp_manager will print a message and continue without restoring, by setting resume_ignore_no_checkpoint to True + +Model Configuration +------------------- + +Dataset Configuration +^^^^^^^^^^^^^^^^^^^^^ + +Training, validation, and test parameters are specified using the ``data`` sections in the model +configuration file. Depending on the task, there may be arguments specifying the augmentations +for the dataset, the image resolution, camera parameters and so on. + +Any initialization parameter that is accepted for the Dataset class used in the experiment can be set in the config file. +Refer to the `Datasets <./datasets.html#Datasets>`__ section of the API for a list of Datasets and their respective parameters. + +An example NeRF dataset configuration should look similar to the following: + +.. code-block:: yaml + + model: + data: + train_batch_size: 1 + train_shuffle: false + train_dataset: + _target_: a pytorch Dataset or IterableDataset class + + val_batch_size: 1 + val_shuffle: false + val_dataset: + _target_: a pytorch Dataset or IterableDataset class + + test_batch_size: 1 + test_shuffle: false + test_dataset: + _target_: a pytorch Dataset or IterableDataset class + + +Model Architecture Configurations +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Each configuration file should describe the model pipeline and architecture being used for the experiment. + +Here is a list of modules a nerf pipeline might use: + ++--------------------+-----------------------------------------------------+ +| **Module** | **Description** | ++====================+=====================================================+ +| :code:`guidance` | guidance model | ++--------------------+-----------------------------------------------------+ +| :code:`nerf` | the main network for foreground density and color | ++--------------------+-----------------------------------------------------+ +| :code:`background` | a complimentary layer for background color | ++--------------------+-----------------------------------------------------+ +| :code:`material` | materials network for lightning and shading effects | ++--------------------+-----------------------------------------------------+ +| :code:`renderer` | rendering layer | ++--------------------+-----------------------------------------------------+ + +Refer to `DreamFusion <./dreamfusion.html#dreamfusion>`_ for model specific configurations. + + +Optimizer Configurations +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + optim: + name: adan + lr: 5e-3 + eps: 1e-8 + weight_decay: 2e-5 + max_grad_norm: 5.0 + foreach: False + + +By default we use ``adan`` as the optimizer, refer to NeMo user guide for all supported optimizers. diff --git a/docs/source/multimodal/nerf/datasets.rst b/docs/source/multimodal/nerf/datasets.rst new file mode 100644 index 000000000000..3981cef34c9f --- /dev/null +++ b/docs/source/multimodal/nerf/datasets.rst @@ -0,0 +1,81 @@ +Datasets +======== + +.. note:: It is the responsibility of each user to check the content of the dataset, review the applicable licenses, and determine if it is suitable for their intended use. Users should review any applicable links associated with the dataset before placing the data on their machine. + + +Rays dataset +------------ +Ray datasets are specialized data structures designed for applications in computer graphics, notably in 3D reconstruction, neural rendering, and ray tracing. + +Ray datasets are characterized by their detailed representation of rays, each defined by an origin point (rays_o) and a direction vector (rays_d). +These datasets are closely tied to specific image dimensions, including height and width, which dictate the resolution and aspect ratio of the target images. +Alongside the core ray data, these datasets typically include additional metadata such as camera parameters, depth values, and color information. +The diversity and complexity of the dataset, encompassing a range of viewpoints and lighting conditions, play a crucial role in capturing the nuances of real-world light behavior. + + +Random Poses Dataset +^^^^^^^^^^^^^^^^^^^^ +The Random Poses Dataset randomly generates camera poses, each translating to a unique set of rays characterized by their origins and directions. +This randomization is key to covering a wide range of potential viewpoints and angles, mimicking a comprehensive exploration of a 3D scene. +This diverse sampling is essential for training robust NeRF models capable of accurately reconstructing and rendering 3D environments from previously unseen angles. + +The dataset inherently accounts for the necessary parameters of ray generation, such as the height and width of the target images, +ensuring that the rays are compatible with the specific requirements of the rendering or reconstruction algorithms. +In addition to the ray origins and directions, the dataset may also include other relevant metadata like camera intrinsic and extrinsic parameters, +contributing to a more detailed and versatile training process. + +An example of RandomPosesDataset usage as a training dataset is shown below: + +.. code-block:: yaml + + model: + data: + train_batch_size: 1 + train_shuffle: false + train_dataset: + _target_: nemo.collections.multimodal.data.nerf.random_poses.RandomPosesDataset + internal_batch_size: 100 + width: 512 + height: 512 + radius_range: [3.0, 3.5] + theta_range: [45, 105] + phi_range: [-180, 180] + fovx_range: [10, 30] + fovy_range: [10, 30] + jitter: False + jitter_center: 0.2 + jitter_target: 0.2 + jitter_up: 0.02 + uniform_sphere_rate: 0 + angle_overhead: 30 + angle_front: 60 + + +Circle Poses Dataset +^^^^^^^^^^^^^^^^^^^^ +Circle Poses Dataset is a specialized ray dataset designed for generating samples of rays in a circular pattern. +The key feature of this dataset is its ability to simulate camera positions arranged along a circular path, focusing on a central point. +This arrangement is particularly useful for capturing scenes from multiple, evenly spaced angles, ensuring a comprehensive view around a central axis. + +The defining parameter of the Circle Poses Dataset is its size, which dictates the number of samples or camera poses around the circle. +A larger size results in more camera positions being generated, offering finer granularity and coverage of the circle. +Each camera pose corresponds to a unique set of rays, with origins and directions calculated based on the position around the circle and the focus on the central point. + +The Circle Poses Dataset is particularly valuable during validation and testing to generate a holistic view of the reconstructed scene. + +An example of CirclePosesDataset usage as a validation dataset is shown below: + +.. code-block:: yaml + + model: + data: + val_batch_size: 1 + val_shuffle: false + val_dataset: + _target_: nemo.collections.multimodal.data.nerf.circle_poses.CirclePosesDataset + size: 5 + width: 512 + height: 512 + angle_overhead: 30 + angle_front: 60 diff --git a/docs/source/multimodal/nerf/dreamfusion.rst b/docs/source/multimodal/nerf/dreamfusion.rst new file mode 100644 index 000000000000..a9f2f630bcdd --- /dev/null +++ b/docs/source/multimodal/nerf/dreamfusion.rst @@ -0,0 +1,310 @@ +DreamFusion +=========== + +Model Introduction +------------------- +DreamFusion :cite:`mm-models-poole2022dreamfusion` uses a pretrained text-to-image diffusion model to perform +text-to-3D synthesis. The model uses a loss based on probability density distillation that enables the use of a 2D +diffusion model as a prior for optimization of a parametric image generator. + +Using this loss in a DeepDream-like procedure, the model optimizes a randomly-initialized 3D model +(a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low +loss. The resulting 3D model of the given text can be viewed from any angle, relit by arbitrary illumination, or composited +into any 3D environment. This approach requires no 3D training data and no modifications to the image diffusion model, +demonstrating the effectiveness of pretrained image diffusion models as priors. + +Dreamfusion models can be instantiated using the :class:`~nemo.collections.multimodal.models.nerf.dreamfusion.DreamFusion` class. + +.. image:: images/dreamfusion_model_overview.png + :align: center + :width: 800px + :alt: DreamFsuion, overview of the model + + +Image guidance +^^^^^^^^^^^^^^ +This section of DreamFusion pertains to the initial phase where the model interprets and translates text inputs into visual concepts. +Utilizing a diffusion based text-to-image model, DreamFusion processes the text input, extracts key visual elements, and translates these into initial 2D images. +The process ensures that the generated 3D models are not only accurate in terms of the text description but also visually coherent and detailed by conditioning +the 2D image based on the view angle. + + +NeRF (foreground) network +^^^^^^^^^^^^^^^^^^^^^^^^^ +The Neural Radiance Fields (NeRF) network is at the heart of DreamFusion's 3D rendering capabilities. +In DreamFusion, the NeRF network takes the 2D images generated from the textual description and constructs a 3D model. +This model is represented as a continuous volumetric scene function, which encodes the color and density of points in space, +allowing for highly detailed and photorealistic renderings. + +Background layer +^^^^^^^^^^^^^^^^ +DreamFusion can leverage a background layer dedicated to background modeling. + +In scenarios where a dynamic background is needed, DreamFusion can be configured to use a secondary NeRF network to generate a background. +This network functions in parallel to the primary NeRF network, focusing on creating a coherent and contextually appropriate backdrop for the main scene. +It dynamically adjusts to lighting and perspective changes, maintaining consistency with the foreground model. + +Alternatively, DreamFusion allows for the integration of a static background color, which is particularly useful in scenarios where the focus is predominantly on the object being generated, and a non-distracting backdrop is desirable. +Implementing a static color background involves setting a uniform chromatic value that encompasses the periphery of the 3D model. +This approach simplifies the rendering process and can be beneficial in reducing computational load while maintaining focus on the primary object. + +Materials network +^^^^^^^^^^^^^^^^^ +The material network in DreamFusion is responsible for adding realism to the 3D models by accurately simulating the physical properties of different materials. +This network takes into account various aspects like texture, reflectivity, and transparency. +By doing so, it adds another layer of detail, making the objects generated by DreamFusion not just structurally accurate but also visually and tactilely realistic. + + +Renderer layer +^^^^^^^^^^^^^^ +The renderer layer functions as the culminating stage in DreamFusion's processing pipeline. +It translates the synthesized volumetric data from the NeRF and material networks into perceptible imagery. +Employing ray-tracing algorithms, this layer computes the interaction of light with the 3D scene, +producing images that exhibit sophisticated attributes like accurate shadow casting, +dynamic lighting, and perspective-correct renderings. + + + +Model Configuration +------------------- + +DreamFusion models can be instantiated using the :class:`~nemo.collections.multimodal.models.nerf.dreamfusion.DreamFusion` class. +The model configuration file is organized into the following sections: + +.. code-block:: yaml + + _target_: nemo.collections.multimodal.models.nerf.dreamfusion.DreamFusion + defaults: + - nerf: torchngp + - background: static + - material: basic_shading + - renderer: torchngp_raymarching + - guidance: sd_huggingface + - optim: adan + - loss: dreamfusion + - data: data + - _self_ + + ### model options + resume_from_checkpoint: + prompt: 'a hamburger' + negative_prompt: '' + front_prompt: ', front view' + side_prompt: ', side view' + back_prompt: ', back view' + update_extra_interval: 16 + guidance_scale: 100 + export_video: False + + iters: ${trainer.max_steps} + latent_iter_ratio: 0.2 + albedo_iter_ratio: 0.0 + min_ambient_ratio: 0.1 + textureless_ratio: 0.2 + + data: + train_dataset: + width: 64 + height: 64 + val_dataset: + width: 800 + height: 800 + test_dataset: + width: 800 + height: 800 + +- ``defaults``: Defines default modules for different components like nerf, background, material, etc. +- ``resume_from_checkpoint``: Path to a checkpoint file to initialize the model with. +- ``prompt``: Main textual input for the model describing the object to generate. +- ``negative_prompt``: Textual input describing what to avoid in the generated object. +- ``front_prompt``, ``side_prompt``, ``back_prompt``: Textual inputs that are appended to the prompts for more detailed orientation guidance. +- ``update_extra_interval``: Interval for updating internal module parameters. +- ``guidance_scale``: The guidance scaled used with the diffusion model. +- ``export_video``: Boolean to determine whether to export a 360 video of the generated object. +- ``iters``, ``latent_iter_ratio``, ``albedo_iter_ratio``, ``min_ambient_ratio``, ``textureless_ratio``: Various ratios and parameters defining iteration behavior and visual characteristics of the output. +- ``data``: Defines dataset dimensions for training, validation, and testing. + +The behavior of the pipeline can be precisely adjusted by fine-tuning the parameters of various components in the default section. +Some components support different backends and implementations, the full components catalog can be viewed in the config directory ``{NEMO_ROOT/examples/multimodal/generative/nerf/conf/model}``. + +Image guidance +^^^^^^^^^^^^^^ + +.. code-block:: yaml + + _target_: nemo.collections.multimodal.modules.nerf.guidance.stablediffusion_huggingface_pipeline.StableDiffusion + precision: ${trainer.precision} + model_key: stabilityai/stable-diffusion-2-1-base + t_range: [0.02, 0.98] + +- ``precision``: Sets the precision of computations (e.g., FP32 or FP16). +- ``model_key``: Specifies the pre-trained model to use for image guidance. +- ``t_range``: Range of threshold values for guidance stability. + + +NeRF (foreground) network +^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + _target_: nemo.collections.multimodal.modules.nerf.geometry.torchngp_nerf.TorchNGPNerf + num_input_dims: 3 + bound: 1 + density_activation: exp + blob_radius: 0.2 + blob_density: 5 + normal_type: central_finite_difference + + encoder_cfg: + encoder_type: 'hashgrid' + encoder_max_level: + log2_hashmap_size: 19 + desired_resolution: 2048 + interpolation: smoothstep + + sigma_net_num_output_dims: 1 + sigma_net_cfg: + num_hidden_dims: 64 + num_layers: 3 + bias: True + + features_net_num_output_dims: 3 + features_net_cfg: + num_hidden_dims: 64 + num_layers: 3 + bias: True + +Describes the NeRF network's architecture, including the density activation function, network configuration, and the specification of the sigma and features networks. + +Background layer +^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + _target_: nemo.collections.multimodal.modules.nerf.background.static_background.StaticBackground + background: [0, 0, 1] + +Static background, where the background key is the RGB color. + +.. code-block:: yaml + + _target_: nemo.collections.multimodal.modules.nerf.background.torchngp_background.TorchNGPBackground + + encoder_type: "frequency" + encoder_input_dims: 3 + encoder_multi_res: 6 + + num_output_dims: 3 + net_cfg: + num_hidden_dims: 32 + num_layers: 2 + bias: True + +Dynamic background, where the background is generated by a NeRF network. + + +Materials network +^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + _target_: nemo.collections.multimodal.modules.nerf.materials.basic_shading.BasicShading + +Defines the basic shading model for the material network. The basic shading model supports textureless, lambertian and phong shading. + + +Renderer layer +^^^^^^^^^^^^^^ + +.. code-block:: yaml + + _target_: nemo.collections.multimodal.modules.nerf.renderers.torchngp_volume_renderer.TorchNGPVolumeRenderer + bound: ${model.nerf.bound} + update_interval: 16 + grid_resolution: 128 + density_thresh: 10 + max_steps: 1024 + dt_gamma: 0 + +Configures the renderer, specifying parameters like update interval, grid resolution, and rendering thresholds. + + +DreamFusion-DMTet +----------------- +NeRF models integrate geometry and appearance through volume rendering. As a result, +using NeRF for 3D modeling can be less effective when it comes to capturing both the intricate details of a surface as well as +its material and texture. + +DMTet finetunning disentangles the learning of geometry and appearance models, such that both a fine surface and a rich +material/texture can be generated. To enable such a disentangled learning, a hybrid scene representation of +[DMTet](https://nv-tlabs.github.io/DMTet/) is used. + +The DMTet model maintains a deformable tetrahedral grid that encodes a discretized signed distance function and a +differentiable marching tetrahedra layer that converts the implicit signed distance representation to the explicit +surface mesh representation. + + +Model Configuration +^^^^^^^^^^^^^^^^^^^ + +DreamFusion models can be instantiated using the same class as DreamFusion :class:`~nemo.collections.multimodal.models.nerf.dreamfusion.DreamFusion`. +However, the following changes to the training pipeline are necessary: + +.. code-block:: yaml + + _target_: nemo.collections.multimodal.models.nerf.dreamfusion.DreamFusion + defaults: + - nerf: torchngp + - background: torchngp + - material: basic_shading + - renderer: nvdiffrast # (1) + - guidance: sd_huggingface + - optim: adan + - loss: dmtet # (2) + - data: data + - _self_ + + ### model options + resume_from_checkpoint: "/results/DreamFusion/checkpoints/DreamFusion-step\=10000-last.ckpt" # (3) + prompt: 'a hamburger' + negative_prompt: '' + front_prompt: ', front view' + side_prompt: ', side view' + back_prompt: ', back view' + update_extra_interval: 16 + guidance_scale: 100 + export_video: False + + iters: ${trainer.max_steps} + latent_iter_ratio: 0.0 + albedo_iter_ratio: 0 + min_ambient_ratio: 0.1 + textureless_ratio: 0.2 + + data: + train_dataset: + width: 512 # (4) + height: 512 # (4) + val_dataset: + width: 800 + height: 800 + test_dataset: + width: 800 + height: 800 + + +We note the following changes: +1. The rendering module was changed from a volumetric based one to a rasterization based one (nvdiffrast). +2. The model loss is changed to account for the changes in the geometry representation. +3. DreamFusion-DMTet finetunes a pretrained DreamFusion model, the pretrained checkpoint is provided using ``resume_from_checkpoint``. +4. The training shape is incrased to 512x512 + + +References +---------- + +.. bibliography:: ../mm_all.bib + :style: plain + :filter: docname in docnames + :labelprefix: MM-MODELS + :keyprefix: mm-models- diff --git a/docs/source/multimodal/nerf/images/dreamfusion_model_overview.png b/docs/source/multimodal/nerf/images/dreamfusion_model_overview.png new file mode 100644 index 0000000000000000000000000000000000000000..f70b84e932afaecbc520f10b5016bcff905b846c GIT binary patch literal 1653618 zcmZs@1z42Z`vr=EfJjJ4H!3JdOP91r2#R!fHw+B|QUW5+Nyo^y`leBXR~zp>VO*CzO_yc8A&DFzY}64q;JaU~=qOe`cM)JS!u^K5H(E zso!D04d|>itV(tyCv1Lf8Cw}rkeOA-95EZzZs{6Waev3H7?&d*E2}w~OG0Re3_#@zm|jgYty9|5zoSot-W7zTzQ&N*KB}`G2;$SNe7nBN=_H z;EVRW;pDtc{TWiv2ry9mUUe?9GsjwJwfYkM>ce2(EYh%Asv0rKEn5-IUlCAhtl94|CLo0^)+ zlau)0kde>0jmdVSd_~P~^4al8Nrv|JU(HKpTwJ*4Y$0NFSV_5g>ah5dX0DQ|p>i`i zD_ASIRR(j5^LUwDrKYi&S#UtWJxh4o!rntn)SO}SydDX0t{)$A3o9z5pGWJ1tL5Z8 zW9qs~jTiizpVL-IGw7r*%skM_)G5biP;w$A6kJ|X^QTo2Q>b}q=fD8o!NI|h`OCY$ zNS>aaeBA#VsgcG1#0mQ{5B8FMydz)F>MBE~QjVJK`@9c(XC4}aq^y_*Jx9M^de?Fd zc>mvR9dNn*e-(9`nWHj`vA;W=u?1_fBbUj<1DDfe9u;u@HT=9)9~WMxQ5_WYJ8^+$ z@xV1JJDY@pB2zt2UyZO-9SdHz=T=)WrQuNdbMLw=hC|T#DcM$ehjHy&= zhQEK_3NGDYSfthmhr{2fsEo!uItXcBf|gnBp9vTk7#OmXfNioP2bYlNBsGGAY{kLR zv7^H(q1%c*s>VAWzPuC|92^93$HvOqhk1^PiODI2jsJhXBEE9sP2y^P0?GN7ft8o{ zHCJNhTLz@rlZ)V1eMe{Kf${Oz3xU~$_2Ph>bL{;eSF25>I07?xaJLqh~h_9Q+c)FQW`Ok6OUJ5L+Q z1@C>+Hdv{yRcrC`~o&0}N4 zV8feP(NX;zot*kdN2Su=rhUjQb$%2X`h%@Wge%d=(9mB!Z$!Yl+zKxFG)m9Qi}EG_ zLxPf&>15;NZ=$cYY|EG-r@TBCJWr-lW6|%Cd|La+u`)TKR1-J1gpQ65<39R_=&S+) z-G_&q^zuojTTlPDkQY7u+bWpx!x!G>eIOwtql@a`lp@}}xO$9tAB0z{zM>1=BQp-L zI3SWvPkBJ3L+w^KHy_8#va_-_TwZ!u5aC8TnGsM%L_|b&7$(eRNIw>3kAF-V!NJcD zA9y3qT-Mqup_D^_kB{HOs%K(C-`v~`o_MX||11#JzYEk?@$PJK@$tB2jA*)#9`UJ% z(5FwIK)HkNoVW~!(Q}&sph9X% zEYgBX3Bt_S*tmV=ATH}B=7YsQ*3}7l2&ML}-Ii#>cS(;1I}zM<1rc%(bM4%=OUddU zzR~O3ZMC=lyku64IV)Q(s99+_8nUC3e`4A7<$=`S7W5`=f#;jdlcI5^jEszLT}Ek? z#H(9dMkm`-5CKZe=C{6)(ut;@S^Idb$y{);QSRO>S_Jey%H6yvaq*E%+^4eHgP+60 z_2FDuW_TC@;ORjpo!f(`R|mIC7{a;EJW@akWo2bC#CBC^GJkBO273(_6_f$Hq|#28 z+ZMAZGpK%8zh4j#eL$vT)u4V|x99DC=5BH3{-N7ucy&~@&6r2T>MZvb30T%j7G8Ji zUx%@)RqxgmUP|&b6kmE8c+oNRskU|cGrY-~%6g;qNxNYADcAEiS+R*?< z4@5v-fRdgZJGVmmM;L&Cm%=}e8yTiB` z#40Gg>2HG!*7X{j)YSpyVW8u9&WF6zomRgl2C(4^ zKXGr{1D|BML$y%9JD(cY0@@jc_XTFAU3*M2Eci!PY?u^Giu$ei$OcWdR@_GZbbjE{ z7R`oDahfDQU{~iFW*e!#$}7n0;fKRKr!-0~-E>yo3HO|hp#~o9oP28I8jbZXdzmyo zp3*GyYLI$%Vz#Z*s9=xJMwaVHMC1-bUflCA?7z+E?hxXWx&QAj#$~c0DVp6oUerh^ zv4|Ny^TufTe=3*%E%a%FLO~-2B@EDuaMnMb7tZ%k#V6i^obK;e{NJ1giTix{(d}x{ z)6;|QJm!3*?H4KS@9TT#Q(&N7r6Uejq;qjeNek$U00pVW1n3cq)8aaBO-OK(4p;i# zRG)UE?kq}lhWxnBAwT3W>CW?vXMex%{9CG=G-*NqS`p80cc_k#8at_9Ef8Xp9pnd&AZBf(v~M&4D1nrz_L#VF)_t66u}l=U0q?E2eoH` z{VP;8baea{A5Q`v=#w6&*y}=7Oi+P5#V3Lnc$k=&zQ|;@8?pnOgxQ=1TE94LKPKkt zU(T_(Kfs{-4C!pCKzZE-S`tHl@7`@diud+(;$m8@ds{^35Or4P1fqQu335xDmAT}U zOr;D3&sxF@FY8#c2bnamc4+47D^C0Bztgm}W)G62$|T83YzeSL5p#@DVM&QNU-DB> zg`P*-R%!xT^R?qKK9-*X02D^RT~IkevlU8(a>`XzR|i8E4v&uJ4$eU9xLxWe zo#~XArm|1g^?YfMB2m{3?){~L?E!u0HX1_+R1TgaZnfa^c4X}avDz=!G9=Yne?x-{ zpngy%0k*q^R5>KXL9P0hGj&?Gzy<6C13?1R3ZNs-oqnHKB_v%%p!LcsC`8F*%0D;<{jDDk51rYu`3E?~QoFnUvNwP4+xDX$+dOiPP+aYegb(lRt4azr5cu_09 zsNc0@58eVypwNn}nKek9c5|XM)RgnrU+NIq<&XR=hbWy%sou}X%C+w9KT^R~<9EvA ze540M$jG>9QRnC=u?Ihq|0)6{H1M)#(K4VGMfMmgkm>0HB5-(e!pKTJU<|%qDvSew zdcI_9RMhYN40{kwXkJhfepe9#VJKKeb`B0Pu)wUCe0+S;Pou!+7VNcpWB-UV%Q-kW z+$^Sib|KS)W>$bUoA|wBU(<>c$haSj>B#|nnNqhddV{vBp7kZhoFg8y;S@+!` zKiU=f;s%+uCx`#L-Ty-QH+;!is!UwS$e_D}c;q4fU>7M(W=3$3b<@)@&phhXy2<_z z`~tiE=FOWQgJyplYHXJTD>1;a0LP&Rh=S_4NDH9jKM>Bi82ks2GCtkEI6vP#V}^G1 z+~&SY`12$FcQHRBnBV>o8t^GCio6M5kUuXiT%W(g!TI(R4Xw#1)-C;NiG8H{uSR19 zxMWBxEr7Izg@uEXH}MHL*22LV0r5jKy4wf^uu@i5HpzJs8xyl{W~M-28+7n!y(UoB zn7W!c)H9egMz}LOM1^%Y{;LRc(?|;)s*&mW3 z?)!Q7Z_SD=!#lEt=$&1#9wGTgxY^Ds6^#7l%|GNoK?0Zw$Sd!kOXR$7x(!l(=go$N zJ%*e82j6|oF<}83@h*25zzka+S@w8CTU)?`;1d$!hEw4EckkZG%F6?Miv*Uk#{qe; zMswSO#{R7R|HLVJVDsj5ZQ{RiI!*w!bJTkK18eAK1j8R`Pe)&N@ZJ6m)>yPeH(T9s zH)#P(@fdpUSZ7<2e-d67&INcmSQ>zn8KPy7da5Ja7VK_1!RK3M{uESvK-U^X0DK0k z1~R8cy8xj2aRLyqAjUs`W(5Izi~IiuY|Db3CbJ4a9hL1s1p?gy#0Xe+KnvX5h>3|! 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z>t87Z)bT_w|F^d-pR~TuR-g%R$0WNGY5LFq%O5()Y?bM>MSx;~zrMO^3Lqde3w7ZC zOilh9#N}`4WU2qTwg0|sP`0uDuZR9$Z2iU8ya)gLsQ8kk|JP)6PyY8||6fcteox?E WnCdODO7I>FW02SC^5w6ff&T~0e!W8g literal 0 HcmV?d00001 diff --git a/docs/source/multimodal/nerf/intro.rst b/docs/source/multimodal/nerf/intro.rst new file mode 100644 index 000000000000..eca057215a75 --- /dev/null +++ b/docs/source/multimodal/nerf/intro.rst @@ -0,0 +1,55 @@ +NeRF +==== +NeMO NeRF is a collection of models and tools for training 3D and 4D models. + +The library is designed with a modular approach, enabling developers to explore and find the most suitable solutions for their requirements, +and allowing researchers to accelerate their experimentation process. + + +Supported Models +----------------- +NeMo NeRF currently supports the following models: + ++----------------------------------------+------------+ +| Model | Categories | ++========================================+============+ +| `DreamFusion <./dreamfusion.html>`_ | text to 3D | ++----------------------------------------+------------+ + + +Spotlight Models +----------------- + +DreamFusion +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +The `DreamFusion `_ model utilizing pre-trained 2D text-to-image diffusion models to create detailed 3D objects from textual descriptions. +This approach overcomes the limitations of traditional 3D synthesis, which typically requires extensive labeled 3D data and sophisticated denoising architectures. +At the core of DreamFusion is the optimization of a Neural Radiance Field (NeRF), a parametric model for rendering 3D scenes. +The optimization process is driven by a loss function based on probability density distillation, which enables the 2D diffusion model to act as an effective prior. +DreamFusion is capable of producing 3D models that are not only accurate representations of the input text but also offer versatility in terms of rendering from any viewpoint, +relighting under diverse lighting conditions, and integration into various 3D environments. Importantly, this method achieves these results without the need for +specific 3D training data or modifications to the existing image diffusion model. + +- Model Structure: + - Text-to-image model: a pretrained text-to-image diffusion model is used to generate a 2D image from a given text. + - NeRF: a neural radiance field (NeRF) that can generate novel views of complex 3D scenes, based on a partial set of 2D images. + - Renderer: A volume rendering layer is used to render the NeRF model from a given viewpoint. + + +For more information, see additional sections in the NeRF docs on the left-hand-side menu or in the list below: + +.. toctree:: + :maxdepth: 1 + + datasets + configs + dreamfusion + +References +---------- + +.. bibliography:: ../mm_all.bib + :style: plain + :filter: docname in docnames + :labelprefix: MM-MODELS + :keyprefix: mm-models- diff --git a/docs/source/multimodal/text2img/checkpoint.rst b/docs/source/multimodal/text2img/checkpoint.rst new file mode 100644 index 000000000000..7e8f7149896d --- /dev/null +++ b/docs/source/multimodal/text2img/checkpoint.rst @@ -0,0 +1,80 @@ +Checkpoints +=========== + +There are three main ways to load pretrained checkpoints in NeMo: + +* Using the :code:`restore_from()` method to load a local checkpoint file (``.nemo``), or +* Converting a partially trained ``.ckpt`` (intermediate) checkpoint to ``.nemo`` format. +* Converting HuggingFace public checkpoints to ``.nemo`` format. + +Refer to the following sections for instructions and examples for each. + +Note that these instructions are for loading fully trained checkpoints for evaluation or fine-tuning. + +Loading ``.nemo`` Checkpoints +------------------------- + +NeMo automatically saves checkpoints of a model that is trained in a ``.nemo`` format. Alternatively, to manually save the model at any +point, issue :code:`model.save_to(.nemo)`. + +If there is a local ``.nemo`` checkpoint that you'd like to load, use the :code:`restore_from()` method: + +.. code-block:: python + + import nemo.collections.multimodal as nemo_multimodal + model = nemo_multimodal.models..restore_from(restore_path="") + +Where the model base class is the MM model class of the original checkpoint. + +Converting Intermediate Checkpoints +--------------------------- +To evaluate a partially trained checkpoint, you may need to convert it to ``.nemo`` format. +`script to convert the checkpoint `. + +.. code-block:: python + + python -m torch.distributed.launch --nproc_per_node= * \ + convert_ckpt_to_nemo.py \ + --checkpoint_folder \ + --checkpoint_name \ + --nemo_file_path \ + --tensor_model_parallel_size \ + --pipeline_model_parallel_size + + +Converting HuggingFace Checkpoints +--------------------------------- + +To fully utilize the optimized training pipeline and framework/TRT inference pipeline +of NeMo, we provide scripts to convert popular checkpoints on HuggingFace into NeMo format. +Once converted, you can perform fine-tuning or inference on such checkpoints. + +Stable Diffusion & ControlNet +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We provide `script to convert the Huggingface checkpoint ` to ``.nemo`` format, which can then be used within our inference pipeline. + + +.. code-block:: python + + python convert_hf_ckpt_to_nemo.py \ + --ckpt_path \ + --hparams_file \ + --nemo_file_path \ + --model_type \ + --nemo_clip_path + + +- ``hparams_file``: Config file to be combined with model weights to generate ``.nemo`` checkpoint. It can be generated from a dummy run and can be found at, for example, ``nemo_experiments/stable-diffusion-train/version_0/hparams.yaml``. + +- ``model_type``: We support converting `stable_diffusion` and `controlnet` checkpoint in this script. + +- ``nemo_clip_path``: It's required only when the ``cond_stage_config`` in ``hparams_file`` refer to a NeMo CLIP model. It will be ignored when ``cond_stage_config`` refer to Hugginface CLIP. See :ref:`sd-config-section` for more details. + + +Imagen +^^^^^^^^^^^^^^ + +We will provide conversion script if Imagen research team releases their checkpoint +in the future. Conversion script for DeepFloyd IF models will be provided in the +next release. \ No newline at end of file diff --git a/docs/source/multimodal/text2img/configs.rst b/docs/source/multimodal/text2img/configs.rst new file mode 100644 index 000000000000..3c534044ba40 --- /dev/null +++ b/docs/source/multimodal/text2img/configs.rst @@ -0,0 +1,166 @@ +Common Configuration Files +============================ + +This section describes the NeMo configuration file setup that is specific to models in the MM Text2Img collection. For general information +about how to set up and run experiments that is common to all NeMo models (e.g. Experiment Manager and PyTorch Lightning trainer +parameters), see the `Core Documentation <../../core/core.html>`_ section. + +The model section of the NeMo Multimodal Text2Img configuration files generally requires information about the dataset(s) being used, +the text and image encoder, parameters for any augmentation being performed, as well as the model architecture specification. The sections on +this page cover each of these in more detail. + +Example configuration files for all of the NeMo Multimodal Text2Img scripts can be found in the +`config directory of the examples `_. + + +Dataset Configuration +--------------------- + +Training, validation, and test parameters are specified using the ``train``, ``validation``, and +``test`` sections in the configuration file, respectively. Depending on the task, there may be arguments specifying the augmentations +for the dataset, the resolution filter for filtering out images, and so on. + +Any initialization parameter that is accepted for the Dataset class used in the experiment can be set in the config file. +Refer to the `Datasets <../api.html#Datasets>`__ section of the API for a list of Datasets and their respective parameters. + +An example Text2Img train configuration should look similar to the following: + +.. code-block:: yaml + + model: + data: + num_workers: 16 # The number of workers for dataloader process + train: + dataset_path: # List of wdinfo files for the datasets to train on + - dataset1.pkl + - dataset2.pkl + augmentations: + resize_samllest_side: 64 # Resize the smallest side of the image to the specified resolution + center_crop_h_w: 64, 64 # Center cropping + horizontal_flip: False # Whether to perform horizontal flip + filterings: + resolution: + method: larger + value: 64 + webdataset: + use_webdataset: True + infinite_sampler: false + local_root_path: ??? # Path that stores the dataset + verbose: False # Whether to print detail debugging information + +Currently, our diffusion-based Text2Img models do not require validation steps for faster convergence. +As discussed in `Datasets <./datasets.html>`_, storing training dataset in webdataset format is the requirement for all +text2img training pipeline. Using ``webdataset.infinite_sampler=True`` is the preferred way for training especially if the dataset +is large as suggested by `Webdataset Multinode Training Guideline `_ . + +Enabling ``train.filterings`` allows one to filter out images (and corresponding text pairs) based on some common use cases (e.g., minimum resolution) +without having to create a redundant subset of the webdataset on the disk prior to training. The example above showcases how to filter the dataset so that only images with a resolution +larger than 64x64 will be used for training. Concatenating multiple webdataset is as easy as listing all wdinfo files in +``train.dataset_path``. + + + + +Trainer Configuration +-------------------------- + +Trainer configuration specifies the arguments for Pytorch Lightning Trainer Object. + +.. code-block:: yaml + + trainer: + devices: 1 # number of GPUs (0 for CPU), or list of the GPUs to use e.g. [0, 1] + num_nodes: 1 + max_epochs: -1 + max_steps: 2500000 # precedence over max_epochs + logger: False # Provided by exp_manager + precision: bf16 # Should be set to 16 for O1 and O2 to enable the AMP. + accelerator: gpu + log_every_n_steps: 5 # Interval of logging. + resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. + num_sanity_val_steps: 10 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it + enable_checkpointing: False # Provided by exp_manager + accumulate_grad_batches: 1 # do not modify, grad acc is automatic for training megatron models + gradient_clip_val: 1.0 + benchmark: False + enable_model_summary: True + +Refer to the `Pytorch Lightning Trainer `__ API section +for all possible arguments + + +Experiment Manager Configurations +--------------------------- + +NeMo Experiment Manager provides convenient way to configure logging, saving, resuming options and more. + +.. code-block:: yaml + + exp_manager: + exp_dir: null # exp_dir for your experiment, if None, defaults to "./nemo_experiments" + name: ${name} + create_wandb_logger: True + wandb_logger_kwargs: # Whether you want exp_manger to create a Wandb logger + name: training-session + project: text2img + group: nemo + resume: True + create_tensorboard_logger: True # Whether you want exp_manger to create a tb logger + create_checkpoint_callback: True # Whether you want exp_manager to create a model checkpoint callback + checkpoint_callback_params: + monitor: reduced_train_loss + save_top_k: 5 + every_n_epochs: 0 # Save checkpoint frequency. + every_n_train_steps: 1000 # Mutually exclusive with every_n_epochs. It is recommended to set this if training on large-scale dataset. + filename: '${name}--{reduced_train_loss:.2f}-{step}-{consumed_samples}' + resume_if_exists: True + resume_ignore_no_checkpoint: True + resume_from_checkpoint: ${model.resume_from_checkpoint} + ema: + enable: True + decay: 0.9999 + validate_original_weights: False + every_n_steps: 1 + cpu_offload: False + +EMA feature can be enabled by setting ``exp_manager.ema.enable=True``. + +Optimizer Configurations +------------------------- + +.. code-block:: yaml + + optim: + name: fused_adam + lr: 0.0001 + eps: 1e-8 + betas: [ 0.9, 0.999 ] + weight_decay: 0.01 + sched: + name: WarmupPolicy + warmup_steps: 10000 + warmup_ratio: null + +By default we use ``fused_adam`` as the optimizer, refer to NeMo user guide for all supported optimizers. +Learning rate scheduler can be specified in ``optim.sched`` section. + +Model Architecture Configurations +------------------------ + +Each configuration file should describe the model architecture being used for the experiment. + +Here is the list of the parameters in the model section which are shared among most of the MM Text2Img models: + ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| **Parameter** | **Datatype** | **Description** | ++===========================+==============+=======================================================================================+ +| :code:`micro_batch_size` | int | micro batch size that fits on each GPU | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| :code:`global_batch_size` | int | global batch size that takes consideration of gradient accumulation, data parallelism | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| :code:`inductor` | bool | enable TorchInductor optimization | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| :code:`channels_last` | bool | enable NHWC training format | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ +| :code:`seed` | int | seed used in training | ++---------------------------+--------------+---------------------------------------------------------------------------------------+ diff --git a/docs/source/multimodal/text2img/controlnet.rst b/docs/source/multimodal/text2img/controlnet.rst new file mode 100644 index 000000000000..8f3155770f82 --- /dev/null +++ b/docs/source/multimodal/text2img/controlnet.rst @@ -0,0 +1,106 @@ +ControlNet +=================== + +Model Introduction +-------------------- + +ControlNet :cite:`mm-models-controlnetgithub` is a neural network structure to control diffusion models by adding extra conditions. +It copies the weights of neural network blocks into a "locked" copy and a "trainable" copy. The "trainable" one learns your condition. The "locked" one preserves your model. In this way, the ControlNet can reuse the SD encoder as a deep, strong, robust, and powerful backbone to learn diverse controls. +NeMo Multimodal provides a training pipeline and example implementation for generating images based on segmentation maps. Users have the flexibility to explore other implementations using their own control input dataset and recipe. + +.. image:: ./images/controlnet-structure.png + :alt: ControlNet structure on stable diffusion (See :cite:`mm-models-controlnetgithub`) + + +ControlNet Dataset +____________________ + +ControlNet employs the WebDataset format for data ingestion. (See :doc:`Datasets<./datasets>`) Beyond the essential image-text pairs saved in tarfiles with matching names but distinct extensions (like 000001.jpg and 000001.txt), ControlNet also requires control input within the tarfiles, identifiable by their specific extension. By default, the control input should be stored as 000001.png for correct loading and identification in NeMo's implementation. + +Model Configuration +-------------------- + +Even though the original copy of Stable Diffusion weights is locked, proper configuration settings toghether with a compatible pre-trained checkpoint are required for initialization. See :ref:`sd-config-section` for more details about ``unet_config``, ``first_stage_config`` and ``cond_stage_config``. + +Contol Stage Config +^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + control_stage_config: + _target_: nemo.collections.multimodal.models.controlnet.controlnet.ControlNet + params: + from_pretrained_unet: /ckpts/v1-5-pruned.ckpt + from_NeMo: False + image_size: 32 # unused + in_channels: 4 + hint_channels: 3 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + use_linear_in_transformer: False + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + use_flash_attention: True + +- ``from_pretrained_unet``: Same logic as ``unet_config.from_pretrained``, adjust the from_NeMo based on the checkpoint's source, whether it's from Huggingface or NeMo. + + +- ``control_stage_config``: Outlines the architecture for the trainable copy of U-Net. It's essential that all parameters align with the U-Net checkpoint specified in this section. + +- ``hint_channels``: Represents the channels of input controls, which is 3 in the mentioned example due to the RGB image input having a shape of (H, W, 3). + +ControlNet Training Options +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + model: + control_key: hint + only_mid_control: False + sd_locked: True + ... + + +- ``contorl_key``: Identifier of the control input, ``.png`` files will be converted to dictionary for dataloaders with their keys being ``hint``. + +- ``only_mid_control``: When set to True, during training, only the output from the middle block of the trainable copy will be incorporated into the locked copy. + +- ``sd_locked``: Whether to lock the original stable diffusion weights during training. + + +Optimization related configurations +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++--------------------------+-----------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------+ +| Feature | Description | To Enable | ++==========================+===========================================================================================================+============================================================================================================+ +| Data parallelism | Dataset read concurrently | Automatically when training on multi GPUs/nodes | ++--------------------------+-----------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------+ +| Activation Checkpointing | Reduce memory usage by clearing activations of certain layers and recomputing them during a backward pass | ``model.unet_config.use_checkpoint=True`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------+ +| Bfloat16 Training | Training in Bfloat16 precision | ``trainer.precision=bf16`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------+ +| Flash Attention | Fast and Memory-Efficient Exact Attention with IO-Awareness | ``model.unet_config.use_flash_attention=True`` && ``model.control_stage_config.use_flash_attention=True`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------+ +| Channels Last | Ordering NCHW tensors in memory preserving dimensions ordering. | ``model.channels_last=True`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------+ +| Inductor | TorchInductor compiler | ``model.inductor=True`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------+ + + + + +Reference +----------- + +.. bibliography:: ../mm_all.bib + :style: plain + :filter: docname in docnames + :labelprefix: MM-MODELS + :keyprefix: mm-models- \ No newline at end of file diff --git a/docs/source/multimodal/text2img/datasets.rst b/docs/source/multimodal/text2img/datasets.rst new file mode 100644 index 000000000000..d1efa0322863 --- /dev/null +++ b/docs/source/multimodal/text2img/datasets.rst @@ -0,0 +1,40 @@ +Datasets +======== + +Data pipeline overview +----------------- + +.. note:: It is the responsibility of each user to check the content of the dataset, review the applicable licenses, and determine if it is suitable for their intended use. Users should review any applicable links associated with the dataset before placing the data on their machine. + +For all text2img multimodal models, we provide a generic pipeline as detailed below to download and prepare the dataset. +The pipeline is suitable for any multimodal datasets hosted on the HuggingFace data repository +where the data is stored as one or more parquet files. The pipeline processes the dataset into the +WebDataset format, consisting of tar files of equal sizes for efficient training. + +The 6 sub-stages are as follows. + + #. download_parquet: Parquet files consisting of text (captions) and image URLs are downloaded from a HuggingFace repository. + + #. download_images: The images are downloaded from their respective URLs and, along with the captions, are packed into tar files following the Webdataset format. + + #. reorganize_tar: (Optional) Due to a variety of reasons (such as unstable network or removal of images), some images may fail to download, resulting in uneven tar files with varying number of examples each. If you are using a training sampler that does not support uneven tar files, you need to re-organize the contents of the tar files so that each one contains an equal number of image-text pairs. + + #. precache_encodings: (Optional) If you are training a model with frozen encoders (e.g. Stable Diffusion), you have the option to precache (precompute) image and/or text encodings (embeddings) in this sub-stage. Precaching these encodings can significantly enhance training throughput. + + #. generate_wdinfo: (Optional) The wdinfo.pkl file, which stores information on dataset shards, is generated. + +Depending on your specific circumstance, not all sub-stages need to be run all at once. +For example, for parquet datasets not hosted on HuggingFace or those whose format is not parquet, +sub-stages 2-5 can be used to process locally downloaded datasets. +For webdatasets already downloaded locally, sub-stages 4-5 can be used to precache the encoding to reduce training time. +For models that encode image and text on-the-fly, only sub-stages 1-3 need to be run. + +Instruction for configuring each sub-stage is provided as a comment next to each field in +`download_multimodal.yaml `_ + + +Examples of Preparing a Dataset for Training Text2Img Model +----------------------- + +Refer to the `Dataset Tutorial `_` for details on how to prepare the training dataset for Training Text2Img models. + diff --git a/docs/source/multimodal/text2img/dreambooth.rst b/docs/source/multimodal/text2img/dreambooth.rst new file mode 100644 index 000000000000..438615676c62 --- /dev/null +++ b/docs/source/multimodal/text2img/dreambooth.rst @@ -0,0 +1,132 @@ +DreamBooth +=================== + + +Model Introduction +-------------------- + +DreamBooth :cite:`mm-models-dreamboothpaper` is a fine-tuning technique and a solution to personalize large diffusion models like Stable Diffusion, which are powerful but lack the +ability to mimic subjects of a given reference set. With DreamBooth, you only need a few images of a specific subject to +fine-tune a pretrained text-to-image model, so that it learns to bind a unique identifier with a special subject. This +unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in +different scenes. + +NeMo's Dreambooth is built upon the Stable Diffusion framework. While its architecture mirrors Stable Diffusion (refer to :ref:`sd-config-section`), the distinction lies in its training process, specifically when utilizing a different dataset and incorporating the prior preservation loss when necessary. + +- Prior Preservation Loss +When finetuning large pretrained language models on specific tasks or text-to-image diffusion models on a small dataset, problems like language drift and decreased output variety often arise. The concept of the prior preservation loss is straightforward: it guides the model using its self-generated samples and incorporates the discrepancy between the model-predicted noise on these samples. The influence of this loss component can be adjusted using model.prior_loss_weight. + +.. code-block:: python + + model_pred, model_pred_prior = torch.chunk(model_output, 2, dim=0) + target, target_prior = torch.chunk(target, 2, dim=0) + loss = torch.nn.functional.mse_loss(model_pred.float(), target.float(), reduction="mean") + prior_loss = torch.nn.functional.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") + loss = loss + prior_loss * self.prior_loss_weight + + +- Training Dataset +NeMo's Dreambooth model dataset is different from other NeMo multimodal models in that it doesn't necessitate data stored in the webdataset format. You can find a sample dataset at :cite:`mm-models-dreamboothdataset`. For each object you aim to integrate into the model, just place its images (typically 3-5) in a folder and specify its path in ``model.data.instance_dir``. When training with the prior preservation loss, store images produced by the original model in a distinct folder and reference its path in ``model.data.regularization_dir``. This process is automated in NeMo's DreamBooth implementation. + +Model Configuration +-------------------- + +Pleaser refer to :ref:`sd-config-section` for how to configure Stable Diffusion. Here we show DreamBooth-specific configurations. + +Prior Preservation Loss +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + model: + with_prior_preservation: False + prior_loss_weight: 0.5 + train_text_encoder: False + restore_from_path: /ckpts/nemo-v1-5-188000-ema.nemo #This ckpt is only used to generate regularization images, thus .nemo ckpt is needed + + data: + instance_dir: /datasets/instance_dir + instance_prompt: a photo of a sks dog + regularization_dir: /datasets/nemo_dogs + regularization_prompt: a photo of a dog + num_reg_images: 10 + num_images_per_prompt: 4 + resolution: 512 + center_crop: True + + +- ``train_text_encoder``: Dictates if the text encoder should be finetuned alongside the U-Net. + +- ``with_prior_preservation``: Depending on its setting, this influences how the model behaves with respect to the regularization data. If set to ``False``, both ``model.prior_loss_weight`` and ``model.restore_from_path`` will be disregarded. If set to ``True``, the actions will differ based on the number of images present in ``model.data.regularization_dir``: + + #. If the count is fewer than ``model.data.num_reg_images``: + + + ``model.restore_from_path`` should be provided with a `.nemo` checkpoint, allowing the inference pipeline to produce regularization images. + + ``model.data.num_images_per_prompt`` is analogous to the inference batch size and indicates the number of images generated in one pass, restricted by GPU capabilities. + + ``model.regularization_prompt`` determines the text prompt for the inference pipeline to generate images. It's generally a variant of ``model.data.instance_prompt`` minus the unique token. + + Once all above parameters are satisfied, the inference pipeline will run until the required image count is achieved in the regularization directory. + + #. If the count matches or exceeds ``model.data.num_reg_images`` + + + Training will proceed without calling inference pipeline, and the parameters mentioned above will be ignored. + +Optimization related configurations +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ++--------------------------+-----------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Feature | Description | To Enable | ++==========================+===========================================================================================================+=================================================+ +| Data parallelism | Dataset read concurrently | Automatically when training on multi GPUs/nodes | ++--------------------------+-----------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Activation Checkpointing | Reduce memory usage by clearing activations of certain layers and recomputing them during a backward pass | ``model.unet_config.use_checkpoint=True`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Bfloat16 Training | Training in Bfloat16 precision | ``trainer.precision=bf16`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Flash Attention | Fast and Memory-Efficient Exact Attention with IO-Awareness | ``model.unet_config.use_flash_attention=True`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Channels Last | Ordering NCHW tensors in memory preserving dimensions ordering. | ``model.channels_last=True`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Inductor | TorchInductor compiler | ``model.inductor=True`` | ++--------------------------+-----------------------------------------------------------------------------------------------------------+-------------------------------------------------+ + + +Training with Cached Latents +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + model: + use_cached_latents: True + + data: + num_workers: 4 + instance_dir: /datasets/instance_dir + instance_prompt: a photo of a sks dog + regularization_dir: /datasets/nemo_dogs + regularization_prompt: a photo of a dog + cached_instance_dir: #/datasets/instance_dir_cached + cached_reg_dir: #/datasets/nemo_dogs_cached + + +- ``use_cached_latents``: Determines whether to train using online encoding or pre-cached latents. + +- ``cached_instance_dir``: + + + If ``use_cached_latents`` is enabled and these directories with latents in `.pt` format are specified, training will utilize the latents rather than the original images. + + If a cached directory isn't provided or the number of latent files doesn't match the original image count, the Variational Auto Encoder will compute the image latents before training, and the results will be saved on the disk. + +- ``cached_reg_dir``: + + The logic is consistent with above, contingent on the model.with_prior_preservation setting. + + + + + +Reference +----------- + +.. bibliography:: ../mm_all.bib + :style: plain + :filter: docname in docnames + :labelprefix: MM-MODELS + :keyprefix: mm-models- diff --git a/docs/source/multimodal/text2img/imagen.rst b/docs/source/multimodal/text2img/imagen.rst new file mode 100644 index 000000000000..1e065c738bb4 --- /dev/null +++ b/docs/source/multimodal/text2img/imagen.rst @@ -0,0 +1,287 @@ +Imagen +======== + +Model Introduction +------------------- + +Imagen :cite:`mm-models-saharia2022photorealistic` is a multi-stage text-to-image diffusion model with an unprecedented +degree of photorealism and a deep level of language understanding. Given a text prompt, +Imagen first generates an image at a 64x64 resolution and then upsamples the generated image to 256x256 and 1024x1024 +resolutions, all using diffusion models. + + .. image:: images/imagen_arch.png + :align: center + :alt: imagen model + :scale: 50% + +Imagen models can be instantiated using the :class:`~nemo.collections.multimodal.models.text_to_image.imagen.imagen.MegatronImagen` class. + +Text Encoder +^^^^^^^^^^^^^^^ + +Imagen employs a text encoder, typically T5, to encode textual features. +To enhance efficiency, we strongly recommend preprocessing the training dataset with pre-cached embeddings, +given the substantial size of T5 encoders. Loading encoders during training can lead to a notable reduction in training time. + +UNet +^^^^^^^^^^ + +Imagen has two types of UNet: Regular UNet and EfficientUNet. + +Regular UNet +~~~~~~~~~~~~ +Regular UNet is used for Imagen base64 model. You can also use regular UNet for SR models +(see example config file `sr256-400m-edm.yaml `_), but this typically +results in a larger memory footprint during training for the same model size. + +Recommended UNet size for base64 and SR256 models are listed below: + ++--------------+------------+-----------------------------+------------------------------------+---------------+ +| Model | Resolution | Hidden Size (``embed_dim``) | Text Condition Size (``cond_dim``) | UNet Size (M) | ++==============+============+=============================+====================================+===============+ +| 500m_res_64 | 64x64 | 256 | 512 | 524 | ++--------------+------------+-----------------------------+------------------------------------+---------------+ +| 2b_res_64 | 64x64 | 512 | 2048 | 2100 | ++--------------+------------+-----------------------------+------------------------------------+---------------+ +| 400m_res_256 | 256x256 | 128 | 512 | 429 | ++--------------+------------+-----------------------------+------------------------------------+---------------+ + + +Efficient UNet +~~~~~~~~~~~~~ + +Efficient UNet is based on Regular UNet with the following modifications: + + #. Shift the model parameters from the high resolution blocks to the low resolution blocks, via adding more residual blocks for the lower resolutions + #. Scaling skip connection by 1/sqrt(2) + #. perform downsampling operation **before** convolution and perform upsampling operation **after** convolution. + +With the aforementioned modifications, Efficient UNet can converge more rapidly and with greater memory efficiency. +The Imagen paper states that such a modification has no discernible impact on convergence. +However, our empirical findings reveal that the Regular UNet yields slightly better visual quality. +Metric-wise, they exhibit similar quality based on FID-CLIP evaluation. + +Recommended Efficient UNet size for SR256 and SR1024 models are listed below: + ++---------------+------------+-----------------------------+------------------------------------+-----------------+---------------+ +| Model | Resolution | Hidden Size (``embed_dim``) | Text Condition Size (``cond_dim``) | Attention Block | UNet Size (M) | ++===============+============+=============================+====================================+=================+===============+ +| 600m_res_256 | 256x256 | 128 | 512 | Fuse Attention | 646 | ++---------------+------------+-----------------------------+------------------------------------+-----------------+---------------+ +| 400m_res_1024 | 1024x1024 | 128 | 512 | Cross Attention | 427 | ++---------------+------------+-----------------------------+------------------------------------+-----------------+---------------+ + + +Noise Scheduling / Sampler +^^^^^^^^^^^^^^ + +NeMo Imagen supports two types of noise scheduling: Continous DDPM :cite:`mm-models-nichol2021improved` and EDM :cite:`mm-models-karras2022elucidating`. + +Denoising diffusion probabilistic models (DDPM) :cite:`mm-models-ho2020denoising` +represents the most widely adopted noise scheduling approach among all diffusion models. +Continuous DDPM introduces several modifications to the standard DDPM framework, +with the most significant change being the transition from a discrete noise space to a continuous space. + +Elucidating the Design Space of Diffusion-Based Generative Models" (EDM) proposes an enhanced noise level distribution +strategy during training. It also identifies the optimal time discretization for sampling and +incorporates a higher-order Runge-Kutta method for the sampling process. + +Model Configuration +------------------ + +Text Encoder +^^^^^^^^^^^^^^^^ + +.. code-block:: yaml + + model: + conditioning: + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + precached_key: embeddings_t5_xxl + out_key: t5_text + +``embed_dim`` represents text feature dimension after encoding. For T5, dimensions are either 1024 or 4096. +``token_length`` specifies the maximum context lnegth. All precached text features will be either trimmed or padded to match this specified length. +``drop_rate`` defines the rate at which random text segments are dropped during training. +``embeddings_t5_xxl`` specifies the key name associated with the precached features in the dataset. + +When using online encoding: + +.. code-block:: yaml + + model: + conditioning: + online_encoding: True + encoder_path: ??? + embed_dim: 1024 + token_length: 128 + drop_rate: 0.1 + +Set ``online_encoding=True`` and set the text encoder path ``encoder_path``. It will load the text encoder +during training to generate text embedding for the raw text from the dataset. + +Regular UNet +^^^^^^^^^^^^ +.. code-block:: yaml + + unet_type: base + unet: + embed_dim: 256 + image_size: 64 + channels: 3 + num_res_blocks: 3 + channel_mult: [ 1, 2, 3, 4 ] + num_attn_heads: 4 + per_head_channels: 64 + cond_dim: 512 + attention_type: fused + feature_pooling_type: attention + learned_sinu_pos_emb_dim: 0 + attention_resolutions: [ 8, 16, 32 ] + dropout: False + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: True + flash_attention: False + resblock_updown: False + resample_with_conv: True + + +To configure the UNet model, set ``unet_type`` to ``base`` for the regular UNet base model or ``sr-unet`` for +the super-resolution (SR) model. The ``embed_dim`` parameter denotes the base number of channels in each ResBlock. + +At each level in the UNet architecture, ``num_res_blocks`` defines the number of ResBlocks for that level, +while ``channel_mult`` is employed in combination with ``embed_dim`` to determine the number of channels at different levels. +``cond_dim`` specifies the size of the conditioning projection. + +Imagen supports two methods of time embedding: either learned time positional embedding or unlearned (fixed). +To use unlearned embedding, set ``learned_sinu_pos_emb_dim`` to 0; for learned embedding, use a positive number. + +The ``feature_pooling_type`` parameter specifies the pooling method, which can be either ``attention`` or ``mean``. + +If you wish to enable model dropout (note that this is different from the text dropout in conditioning), +set the ``dropout`` parameter. When ``resblock_updown`` is set to False, it indicates the use of ResBlocks for +downsampling and upsampling, as opposed to Torch's upsample and downsample functions without learnable weights. +If ``resblock_updown`` is ``False``, you can use ``resample_with_conv`` to determine whether an additional convolutional layer +is needed in addition to pooling and convolution transpose operations. + + +Efficient UNet +^^^^^^^^^^^^^^ + +.. code-block:: yaml + + unet_type: sr + unet: + embed_dim: 128 + image_size: 256 + channels: 3 + channel_mult: [ 1, 2, 4, 8, 8 ] + num_attn_heads: 8 + per_head_channels: 64 + attention_type: stacked + atnn_enabled_at: [ 0, 0, 0, 1, 1 ] + feature_pooling_type: attention + stride: 2 + num_resblocks: [ 2, 4, 8, 8, 8 ] + learned_sinu_pos_emb_dim: 0 + use_null_token: False + init_conv_kernel_size: 3 + gradient_checkpointing: False + scale_shift_norm: True + stable_attention: False + flash_attention: False + skip_connection_scaling: True + +Many of the arguments remain consistent with those for the Regular UNet. +To configure the Efficient UNet SR model training, you should set ``unet_type`` to ``sr``. +When using the Efficient UNet SR model, ``num_resblocks`` can be specified as a list to define varying numbers +of ResBlocks for each level. Additionally, you have the option to enable ``skip_connection_scaling``, +which scales the skip connections, as detailed in the Imagen paper. + +Attention Blocks +^^^^^^^^^^^^^ + +Imagen's UNet incorporates multiple attention blocks to effectively handle text embeddings. +The following arguments in the UNet configurations pertain to these attention blocks: + +.. code-block:: yaml + + unet: + attention_type: stacked + attention_resolutions: [8, 16, 32] + stable_attention: False + flash_attention: False + +NeMo Imagen has the following ``attention_type`` implemented: + + #. ``self``: Multi-head self attention block + #. ``cross``: Multi-head cross attention block. Imagen paper uses this implementation for SR1024 model. + #. ``stacked``: Attention blocks that stack one ``self`` attention and ``cross`` attention + #. ``fused``: Attention blocks that fuses one ``self`` attention and ``cross`` attention. Imagen paper uses this implementation for base64 and SR256 model. + +Attention blocks can be integrated at various levels within the UNet by specifying the attention_resolutions. +The option ``stable_attention`` facilitates the computation of attention block backpropagation in a more +numerically stable manner. You can control whether to utilize the optimized FlashAttention by setting the ``flash_attention`` parameter. + + +Scheduling +^^^^^^^^^^^^ + +To train NeMo Imagen with EDM, set ``preconditioning_type=EDM`` and use the suggested parameters from EDM paper: + +.. code-block:: yaml + + preconditioning_type: EDM + preconditioning: + loss_type: l2 + sigma_data: 0.5 + p_mean: -1.2 + p_std: 1.2 + +Note for EDM scheduling, UNet is trained to predict the denoise image rather than the noise itself. supported ``loss_type`` are ``l1``, ``l2``, +and ``huber``. + +.. code-block:: yaml + + preconditioning_type: DDPM + preconditioning: + loss_type: l2 + pred_objective: noise + noise_schedule: cosine + timesteps: 1000 + +Setting ``preconditioning_type=DDPM`` allows user to train UNet with continous DDPM scheduling. ``pred_objective`` can +be either ``noise`` or ``x_start``. We currently support ``linear`` and ``cosine`` modes for ``noise_schedule``. + +Training Optimizations +^^^^^^^^^^^^^^ ++--------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Feature | Description | To Enable | ++==========================+==============================================================================================================================================================================================================================================================================================================================================+=================================================+ +| Data parallelism | Dataset is read concurrently across multiple GPUs or nodes, allowing for faster data loading and processing. | Automatically when training on multi GPUs/nodes | ++--------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Activation Checkpointing | To reduce memory usage, activations of certain layers are cleared and recomputed during a backward pass. This technique is particularly useful for training large models that wouldn't fit in GPU memory using traditional methods. | ``model.unet.gradient_checkpointing=True`` | ++--------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Bfloat16 Training | Training is conducted in Bfloat16 precision, which offers a balance between the higher precision of FP32 and the memory savings and speed of FP16. | ``trainer.precision=bf16`` | ++--------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Flash Attention | FlashAttention is a fast and memory-efficient algorithm to compute exact attention. It speeds up model training and reduces memory requirement by being IO-aware. This approach is particularly useful for large-scale models and is detailed further in the repository linked. [Reference](https://github.com/Dao-AILab/flash-attention) | ``model.unet.flash_attention=True`` | ++--------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Channels Last | ordering NCHW tensors in memory preserving dimensions ordering. | ``model.channels_last=True`` | ++--------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------+ +| Inductor | TorchInductor compiler | ``model.inductor=True`` | ++--------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------------------------------+ + + +Reference +----------- + +.. bibliography:: ../mm_all.bib + :style: plain + :filter: docname in 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