o wi@sddlmZddlmZddlZddlmZddlm Z ddl m Z ddl m Z ddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddgZdejdeddfddZ dej!dej!fddZ"    d0de de#de#dede#de f ddZ$   !    d1d"ed#e%d$e%d%e%dBd&e%dBd'e%d(e%d)e%d*e#d+e#d,e&dBd-e&dBd.e&dBde'ej(ej)ffd/dZ*dS)2)partialN)StrictHandling)get_gpt_modelopt_spec) ModuleSpec)TransformerConfig) lightning)vlm)get_llama4_layer_spec) _setup_trainer_and_restore_model)ckpt_to_context_subdir)ckpt_to_weights_subdir)logging#set_modelopt_spec_if_exists_in_ckpt2setup_trainer_and_restore_model_with_modelopt_specmodelpathreturncCsnt|d}t|ddd}|rt|drdSt|tjr+t|j d|j _ dSt t |ddS)zdSet model.config.transformer_layer_spec to modelopt spec if modelopt_state exists in the checkpoint.znemo://F) is_savingmodelopt_statemoduleNz= is not a Llama4OmniModel. Modelopt state will not be loaded.)str removeprefixr existshasattr isinstancerLlama4OmniModel_set_llama4_modelopt_specconfiggradient_accumulation_fusionr warningtype)rrmodelopt_state_pathr"f/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/nemo/collections/vlm/modelopt/model_utils.pyr$s    model_cfgcCs<tdt|tjrttdd|j_|St dt |)af Set the model layer spec to a modelopt spec variant. This function updates the model config with the appropriate modelopt layer specification based on the model type. Args: model_cfg (vlm.Llama4OmniConfig): The model config. Returns: vlm.Llama4OmniConfig: The model config updated for the modelopt layer specification. zr?rMtrainerrkvr"r"r#rYsp#        )FFr,F) r2r2NNr2r2r2TFNNN)+ functoolsrlightning.pytorchr@LrS+megatron.core.dist_checkpointing.validationr4megatron.core.post_training.modelopt.gpt.model_specsr$megatron.core.transformer.spec_utilsr,megatron.core.transformer.transformer_configrnemorrQnemo.collectionsr+nemo.collections.llm.gpt.model.llama4_utilsr #nemo.collections.vlm.inference.baser nemo.lightning.ckpt_utilsr nemo.lightning.io.plr nemo.utilsr __all__LightningModulerrr'rboolr(intdicttuplerrVrr"r"r"r#s