o }oiv@@sxddlmZmZddlmZddlZddlZddl m Z ddl m Z ddl mZddlmZmZddlmZmZddlmZdd lmZdd lmZdd lmZdd lmZm Z m!Z!dd l"m#Z#ddl$m%Z%ddl&m'Z'ddl(m)Z)ddl*m+Z+ddl,m-Z-dZ.ej/j0e.ddej1ej2fddZ3ddej4dddddddf de5de5d eej6d!ee5d"e5d#e7d$e5d%e5d&e5d'ee8ej1e dej1ej9fd(d)Z:ej/j0ee.d*dd+d,dd-efd.ee;d/e;d$e5d%e5d0e7d1edejej/j0ee.d*  +   7 8   -dAd.ee;d/e;d$e5d%e5d9e;d:ee;d;ee5dZ?d4ej>> model_config = model() >>> print(model_config) )config)runConfigr seq_lengthr)confr"e/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/llm/recipes/llama31_nemotron_70b.pymodel,s r$Ti{tensor_parallelismpipeline_parallelismpipeline_parallelism_typevirtual_pipeline_parallelismcontext_parallelismsequence_parallelism num_nodesnum_gpus_per_node max_steps callbacksc Csbtjtj||||||dddtjtddddddd } tjtjdd| |ddd||t| d d d } | S) aS Configure the NeMo Lightning Trainer for Llama-3.1-Nemotron-70B model. This function sets up the distributed training strategy optimized for the large 70B model. Args: tensor_parallelism (int): Degree of tensor model parallelism. pipeline_parallelism (int): Degree of pipeline model parallelism. pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism. virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism. context_parallelism (int): Degree of context parallelism. sequence_parallelism (bool): Whether to use sequence parallelism. num_nodes (int): Number of compute nodes to use. num_gpus_per_node (int): Number of GPUs per node. max_steps (int): Maximum number of training steps. callbacks (Optional[list[run.Config[Callback]]]): List of callback configurations. Returns: run.Config[nl.Trainer]: Configuration for the NeMo Lightning Trainer. Examples: CLI usage: $ nemo llm pretrain trainer=llama31_nemotron_70b ... Python API usage: >>> trainer_config = trainer(num_nodes=4, num_gpus_per_node=8) >>> print(trainer_config) Note: This configuration uses extensive parallelism to handle the large model size efficiently. Tcheck_for_nan_in_gradgrad_reduce_in_fp32overlap_grad_reduceoverlap_param_gatheraverage_in_collective) tensor_model_parallel_sizepipeline_model_parallel_sizepipeline_dtype$virtual_pipeline_model_parallel_sizecontext_parallel_sizesequence_parallelgradient_as_bucket_viewckpt_async_saveckpt_parallel_loadddpgpu2 Fi) acceleratoraccumulate_grad_batchesr2deviceslimit_test_batcheslimit_val_batcheslog_every_n_stepsr1r/pluginsstrategyuse_distributed_samplerval_check_interval)rrnlMegatronStrategyrTrainerr) r)r*r+r,r-r.r/r0r1r2rOtrainerr"r"r#rUBsJ+rU)targetrdefaultrDFdirrperformance_modefnc Csbtj|tt||ttgdtjtddddt||t|ddt dd t d }|r/t |}|S) a Create a pre-training recipe for Llama-3.1-Nemotron-70B model. This function sets up a complete configuration for pre-training, including model, trainer, data, logging, optimization, and resumption settings. Args: dir (Optional[str]): Directory for saving logs and checkpoints. name (str): Name of the pre-training run. num_nodes (int): Number of compute nodes to use. num_gpus_per_node (int): Number of GPUs per node. performance_mode (bool): If true, enables optimizations for maximum performance. fn (Callable): The pre-training function to use. Returns: run.Partial: Partial configuration for pre-training. Examples: CLI usage: $ nemo llm pretrain --factory llama31_nemotron_70b $ nemo llm pretrain --factory "llama31_nemotron_70b(num_nodes=4, name='my_70b_pretrain')" Python API usage: >>> recipe = pretrain_recipe(name="llama31_nemotron_70b_pretrain", num_nodes=4) >>> print(recipe) Note: This recipe is optimized for the large 70B model and requires significant computational resources. )r/r0r2rirD)r global_batch_sizemicro_batch_sizer)rXrrga2U0*3?)max_lr)r$rUdatalogoptimresume) rPartialr$rUrrr rrrr"pretrain_performance_optimizations)rXrr/r0rYrZreciper"r"r#pretrain_recipes & rerdc Cs&|jjtjtdtddddd|S)a Create a performance-optimized pre-training recipe for Llama-3.1-Nemotron-70B model. This method enables performance optimizations that may not be suitable for all use cases. It builds upon the standard pre-training recipe and adds additional performance enhancements. Args: recipe (run.Partial): Base pre-train recipe to which performance optimizations will be added Returns: run.Partial: Partial configuration for performance-optimized pre-training. Note: Use this method with caution and only when you need maximum performance. It may not be suitable for all hardware configurations or use cases. TrEF)tp_comm_overlaptp_comm_overlap_cfgdefer_embedding_wgrad_computewgrad_deferral_limit(overlap_param_gather_with_optimizer_stepalign_param_gather)rUr2appendrrrr)rdr"r"r#rcs rcinstructlora model_type peft_schemer packed_sequencec Cs~|dur|}|dur|rdnd}|dur)|dus|dkr!d}n|dvr)d}|dkr2d } n|d kr;d } ntd |d tt| |||||} |dusX|dkrhd| jj_d| jj_d| jj _ n4|dvrt t || _d| j_d| j_d| jj _d| jj _d| jj_d| jj _ ntd||| jj _|| j_|rddi| j_t j t|d| j_|rt| |} | S)a Create a fine-tuning recipe for Llama-3.1-Nemotron-70B model. This can be used for both Llama-3.1-Nemotron-70B-Instruct and Llama-3.1-Nemotron-70B-Reward. By default it fine-tuned on Llama-3.1-Nemotron-70B-Instruct model. This function sets up a complete configuration for fine-tuning, including model, trainer, data, logging, optimization, and resumption settings. The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None. Args: dir (Optional[str]): Directory for saving logs and checkpoints. name (str): Name of the fine-tuning run. num_nodes (int): Number of compute nodes to use. num_gpus_per_node (int): Number of GPUs per node. model_type (str): Type of model to fine-tune. Either 'instruct' or 'reward'. peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning. Allowed values: 'lora'/'dora'/'none'/None. seq_length (int): Maximum number of tokens per microbatch. packed_sequence (Optional[bool]): If true, fine-tuning sequences will be packed into batches up to the given maximum seq_length for better efficiency. By default, this value equals performance_mode. performance_mode (bool): If true, enables optimizations for maximum performance. Returns: run.Partial: Partial configuration for fine-tuning. Examples: CLI usage: $ nemo llm finetune --factory llama31_nemotron_70b $ nemo llm finetune --factory "llama31_nemotron_70b(num_nodes=4, name='my_70b_finetune')" Python API usage: >>> recipe = finetune_recipe(name="llama31_nemotron_70b_finetune", num_nodes=4) >>> print(recipe) Note: This recipe uses the SQuAD dataset for fine-tuning. Be aware that fine-tuning a 70B model requires substantial computational resources. Niinoner%)rndorarDrmz)nvidia/Llama-3.1-Nemotron-70B-Instruct-HFrewardz'nvidia/Llama-3.1-Nemotron-70B-Reward-HFzUnknown model_type z). Expected one of "instruct" or "reward".r(gh㈵>rFFg-C6?zUnrecognized peft scheme: pad_to_max_lengthT)packed_sequence_size)lower ValueErrorrr$rUrOr9r:r`rlrrrr peftdimalphause_distributed_optimizercross_entropy_loss_fusionr r^dataset_kwargsr packed_sequence_specs"finetune_performance_optimizations) rXrr/r0rorpr rqrY resume_pathrdr"r"r#finetune_recipesH4               rcCst|jds g|j_|dus|dkrEd|jj_d|jj_d|jj_d|jj_ t j t dddddd|jj_ |jjt j tddd d nd |jj_d|jj_d|jj_d g|j_d|jj_|jjt t|jjt td d |S)a Modify the given recipe to optimize settings for performance. This method enables performance optimizations that may not be suitable for all use cases. Intended to build upon the standard fine-tuning recipe. Args: recipe (run.Partial): Base fine-tuning recipe to which performance optimizations will be added peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning. Allowed values: 'lora'/'dora'/'none'/None. Returns: run.Partial: Partial configuration for performance-optimized fine-tuning. Note: Use this method with caution and only when you need maximum performance. It may not be suitable for all hardware configurations or use cases. r2Nrrr%r&FTr3)rfrhrir' linear_qkvd)hasattrrUr2rxrOr9r:r<rNr5rrrrBrlrr{target_modulesr>rr)rdrpr"r"r#r_sJ          r) NrWNr(rmrnNNF)Atypingrrlightning.pytorchpytorchplnemo_runrtorch$lightning.pytorch.callbacks.callbackrmegatron.core.distributedrnemorrRnemo.collections.llmrrnemo.collections.llm.apir r "nemo.collections.llm.gpt.data.mockr -nemo.collections.llm.gpt.data.packed_sequencer nemo.collections.llm.peftr -nemo.collections.llm.recipes.finetune_defaultr(nemo.collections.llm.recipes.log.defaultrrr'nemo.collections.llm.recipes.optim.adamr6nemo.collections.llm.recipes.precision.mixed_precisionr;nemo.collections.llm.recipes.tp_overlap_configs.userbuffersr nemo.lightning.pytorch.callbacksr6nemo.lightning.pytorch.callbacks.megatron_comm_overlaprnemo.utils.exp_managerrNAMEclifactoryrLightningModuler$bfloat16intdtypeboollistrTrUstrrbrercrrr"r"r"r#s                  T 9'  g