import{s as Ke,n as Qe,o as Xe}from"../chunks/scheduler.defa9a21.js";import{S as Ye,i as Ze,g as s,s as r,r as i,A as et,h as l,f as a,c as o,j as _,u as c,x as P,k as f,y as p,a as n,v as d,d as g,t as m,w as u}from"../chunks/index.fe795e71.js";import{D as T}from"../chunks/Docstring.90d6fe5c.js";import{H as N,E as tt}from"../chunks/getInferenceSnippets.2234a8dd.js";function at(Be){let h,ne,te,re,D,oe,C,se,v,S,Oe,U,Ve=`Plugin for Megatron-LM to enable tensor, pipeline, sequence and data parallelism. Also to enable selective
activation recomputation and optimized fused kernels.`,le,O,ie,M,q,qe,J,Fe=`Dummy scheduler presents model parameters or param groups, this is primarily used to follow conventional training
loop when scheduler config is specified in the deepspeed config file.`,ce,k,de,$,z,ke,K,We="Dummy dataloader presents model parameters or param groups, this is primarily used to follow conventional training",ge,E,me,b,A,ze,Q,je="Abstract class for batching, forward pass and loss handler.",ue,I,pe,y,G,Ee,X,He="GPT train step class.",_e,B,fe,x,V,Ae,Y,Re="Bert train step class.",he,F,ve,L,W,Ie,Z,Ue="T5 train step class.",Me,j,$e,w,H,Ge,ee,Je="Average losses across data parallel group.",be,R,ye,ae,xe;return D=new N({props:{title:"Megatron-LM utilities",local:"megatron-lm-utilities",headingTag:"h1"}}),C=new N({props:{title:"MegatronLMPlugin",local:"accelerate.utils.MegatronLMPlugin",headingTag:"h2"}}),S=new T({props:{name:"class accelerate.utils.MegatronLMPlugin",anchor:"accelerate.utils.MegatronLMPlugin",parameters:[{name:"tp_degree",val:": int = None"},{name:"pp_degree",val:": int = None"},{name:"num_micro_batches",val:": int = None"},{name:"gradient_clipping",val:": float = None"},{name:"sequence_parallelism",val:": bool = None"},{name:"recompute_activations",val:": bool = None"},{name:"use_distributed_optimizer",val:": bool = None"},{name:"pipeline_model_parallel_split_rank",val:": int = None"},{name:"num_layers_per_virtual_pipeline_stage",val:": int = None"},{name:"is_train_batch_min",val:": str = True"},{name:"train_iters",val:": int = None"},{name:"train_samples",val:": int = None"},{name:"weight_decay_incr_style",val:": str = 'constant'"},{name:"start_weight_decay",val:": float = None"},{name:"end_weight_decay",val:": float = None"},{name:"lr_decay_style",val:": str = 'linear'"},{name:"lr_decay_iters",val:": int = None"},{name:"lr_decay_samples",val:": int = None"},{name:"lr_warmup_iters",val:": int = None"},{name:"lr_warmup_samples",val:": int = None"},{name:"lr_warmup_fraction",val:": float = None"},{name:"min_lr",val:": float = 0"},{name:"consumed_samples",val:": list = None"},{name:"no_wd_decay_cond",val:": typing.Optional[typing.Callable] = None"},{name:"scale_lr_cond",val:": typing.Optional[typing.Callable] = None"},{name:"lr_mult",val:": float = 1.0"},{name:"megatron_dataset_flag",val:": bool = False"},{name:"seq_length",val:": int = None"},{name:"encoder_seq_length",val:": int = None"},{name:"decoder_seq_length",val:": int = None"},{name:"tensorboard_dir",val:": str = None"},{name:"set_all_logging_options",val:": bool = False"},{name:"eval_iters",val:": int = 100"},{name:"eval_interval",val:": int = 1000"},{name:"return_logits",val:": bool = False"},{name:"custom_train_step_class",val:": typing.Optional[typing.Any] = None"},{name:"custom_train_step_kwargs",val:": typing.Optional[dict[str, typing.Any]] = None"},{name:"custom_model_provider_function",val:": typing.Optional[typing.Callable] = None"},{name:"custom_prepare_model_function",val:": typing.Optional[typing.Callable] = None"},{name:"custom_megatron_datasets_provider_function",val:": typing.Optional[typing.Callable] = None"},{name:"custom_get_batch_function",val:": typing.Optional[typing.Callable] = None"},{name:"custom_loss_function",val:": typing.Optional[typing.Callable] = None"},{name:"other_megatron_args",val:": typing.Optional[dict[str, typing.Any]] = None"}],parametersDescription:[{anchor:"accelerate.utils.MegatronLMPlugin.tp_degree",description:`<strong>tp_degree</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
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Pipeline parallelism degree.`,name:"pp_degree"},{anchor:"accelerate.utils.MegatronLMPlugin.num_micro_batches",description:`<strong>num_micro_batches</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Number of micro-batches.`,name:"num_micro_batches"},{anchor:"accelerate.utils.MegatronLMPlugin.gradient_clipping",description:`<strong>gradient_clipping</strong> (<code>float</code>, defaults to <code>None</code>) &#x2014;
Gradient clipping value based on global L2 Norm (0 to disable).`,name:"gradient_clipping"},{anchor:"accelerate.utils.MegatronLMPlugin.sequence_parallelism",description:`<strong>sequence_parallelism</strong> (<code>bool</code>, defaults to <code>None</code>) &#x2014;
Enable sequence parallelism.`,name:"sequence_parallelism"},{anchor:"accelerate.utils.MegatronLMPlugin.recompute_activations",description:`<strong>recompute_activations</strong> (<code>bool</code>, defaults to <code>None</code>) &#x2014;
Enable selective activation recomputation.`,name:"recompute_activations"},{anchor:"accelerate.utils.MegatronLMPlugin.use_distributed_optimizr",description:`<strong>use_distributed_optimizr</strong> (<code>bool</code>, defaults to <code>None</code>) &#x2014;
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Rank where encoder and decoder should be split.`,name:"pipeline_model_parallel_split_rank"},{anchor:"accelerate.utils.MegatronLMPlugin.num_layers_per_virtual_pipeline_stage",description:`<strong>num_layers_per_virtual_pipeline_stage</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Number of layers per virtual pipeline stage.`,name:"num_layers_per_virtual_pipeline_stage"},{anchor:"accelerate.utils.MegatronLMPlugin.is_train_batch_min",description:`<strong>is_train_batch_min</strong> (<code>str</code>, defaults to <code>True</code>) &#x2014;
If both tran &amp; eval dataloaders are specified, this will decide the <code>micro_batch_size</code>.`,name:"is_train_batch_min"},{anchor:"accelerate.utils.MegatronLMPlugin.train_iters",description:`<strong>train_iters</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Total number of samples to train over all training runs. Note that either train-iters or train-samples
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Total number of samples to train over all training runs. Note that either train-iters or train-samples
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Weight decay increment function. choices=[&#x201C;constant&#x201D;, &#x201C;linear&#x201D;, &#x201C;cosine&#x201D;].`,name:"weight_decay_incr_style"},{anchor:"accelerate.utils.MegatronLMPlugin.start_weight_decay",description:`<strong>start_weight_decay</strong> (<code>float</code>, defaults to <code>None</code>) &#x2014;
Initial weight decay coefficient for L2 regularization.`,name:"start_weight_decay"},{anchor:"accelerate.utils.MegatronLMPlugin.end_weight_decay",description:`<strong>end_weight_decay</strong> (<code>float</code>, defaults to <code>None</code>) &#x2014;
End of run weight decay coefficient for L2 regularization.`,name:"end_weight_decay"},{anchor:"accelerate.utils.MegatronLMPlugin.lr_decay_style",description:`<strong>lr_decay_style</strong> (<code>str</code>, defaults to <code>&apos;linear&apos;</code>) &#x2014;
Learning rate decay function. choices=[&#x2018;constant&#x2019;, &#x2018;linear&#x2019;, &#x2018;cosine&#x2019;].`,name:"lr_decay_style"},{anchor:"accelerate.utils.MegatronLMPlugin.lr_decay_iters",description:`<strong>lr_decay_iters</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Number of iterations for learning rate decay. If None defaults to <code>train_iters</code>.`,name:"lr_decay_iters"},{anchor:"accelerate.utils.MegatronLMPlugin.lr_decay_samples",description:`<strong>lr_decay_samples</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Number of samples for learning rate decay. If None defaults to <code>train_samples</code>.`,name:"lr_decay_samples"},{anchor:"accelerate.utils.MegatronLMPlugin.lr_warmup_iters",description:`<strong>lr_warmup_iters</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Number of iterations to linearly warmup learning rate over.`,name:"lr_warmup_iters"},{anchor:"accelerate.utils.MegatronLMPlugin.lr_warmup_samples",description:`<strong>lr_warmup_samples</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Number of samples to linearly warmup learning rate over.`,name:"lr_warmup_samples"},{anchor:"accelerate.utils.MegatronLMPlugin.lr_warmup_fraction",description:`<strong>lr_warmup_fraction</strong> (<code>float</code>, defaults to <code>None</code>) &#x2014;
Fraction of lr-warmup-(iters/samples) to linearly warmup learning rate over.`,name:"lr_warmup_fraction"},{anchor:"accelerate.utils.MegatronLMPlugin.min_lr",description:`<strong>min_lr</strong> (<code>float</code>, defaults to <code>0</code>) &#x2014;
Minumum value for learning rate. The scheduler clip values below this threshold.`,name:"min_lr"},{anchor:"accelerate.utils.MegatronLMPlugin.consumed_samples",description:`<strong>consumed_samples</strong> (<code>List</code>, defaults to <code>None</code>) &#x2014;
Number of samples consumed in the same order as the dataloaders to <code>accelerator.prepare</code> call.`,name:"consumed_samples"},{anchor:"accelerate.utils.MegatronLMPlugin.no_wd_decay_cond",description:`<strong>no_wd_decay_cond</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Condition to disable weight decay.`,name:"no_wd_decay_cond"},{anchor:"accelerate.utils.MegatronLMPlugin.scale_lr_cond",description:`<strong>scale_lr_cond</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Condition to scale learning rate.`,name:"scale_lr_cond"},{anchor:"accelerate.utils.MegatronLMPlugin.lr_mult",description:`<strong>lr_mult</strong> (<code>float</code>, defaults to <code>1.0</code>) &#x2014;
Learning rate multiplier.`,name:"lr_mult"},{anchor:"accelerate.utils.MegatronLMPlugin.megatron_dataset_flag",description:`<strong>megatron_dataset_flag</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether the format of dataset follows Megatron-LM Indexed/Cached/MemoryMapped format.`,name:"megatron_dataset_flag"},{anchor:"accelerate.utils.MegatronLMPlugin.seq_length",description:`<strong>seq_length</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Maximum sequence length to process.`,name:"seq_length"},{anchor:"accelerate.utils.MegatronLMPlugin.encoder_seq_length",description:`<strong>encoder_seq_length</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Maximum sequence length to process for the encoder.`,name:"encoder_seq_length"},{anchor:"accelerate.utils.MegatronLMPlugin.decoder_seq_length",description:`<strong>decoder_seq_length</strong> (<code>int</code>, defaults to <code>None</code>) &#x2014;
Maximum sequence length to process for the decoder.`,name:"decoder_seq_length"},{anchor:"accelerate.utils.MegatronLMPlugin.tensorboard_dir",description:`<strong>tensorboard_dir</strong> (<code>str</code>, defaults to <code>None</code>) &#x2014;
Path to save tensorboard logs.`,name:"tensorboard_dir"},{anchor:"accelerate.utils.MegatronLMPlugin.set_all_logging_options",description:`<strong>set_all_logging_options</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to set all logging options.`,name:"set_all_logging_options"},{anchor:"accelerate.utils.MegatronLMPlugin.eval_iters",description:`<strong>eval_iters</strong> (<code>int</code>, defaults to <code>100</code>) &#x2014;
Number of iterations to run for evaluation validation/test for.`,name:"eval_iters"},{anchor:"accelerate.utils.MegatronLMPlugin.eval_interval",description:`<strong>eval_interval</strong> (<code>int</code>, defaults to <code>1000</code>) &#x2014;
Interval between running evaluation on validation set.`,name:"eval_interval"},{anchor:"accelerate.utils.MegatronLMPlugin.return_logits",description:`<strong>return_logits</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
Whether to return logits from the model.`,name:"return_logits"},{anchor:"accelerate.utils.MegatronLMPlugin.custom_train_step_class",description:`<strong>custom_train_step_class</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Custom train step class.`,name:"custom_train_step_class"},{anchor:"accelerate.utils.MegatronLMPlugin.custom_train_step_kwargs",description:`<strong>custom_train_step_kwargs</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Custom train step kwargs.`,name:"custom_train_step_kwargs"},{anchor:"accelerate.utils.MegatronLMPlugin.custom_model_provider_function",description:`<strong>custom_model_provider_function</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Custom model provider function.`,name:"custom_model_provider_function"},{anchor:"accelerate.utils.MegatronLMPlugin.custom_prepare_model_function",description:`<strong>custom_prepare_model_function</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Custom prepare model function.`,name:"custom_prepare_model_function"},{anchor:"accelerate.utils.MegatronLMPlugin.custom_megatron_datasets_provider_function",description:`<strong>custom_megatron_datasets_provider_function</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Custom megatron train_valid_test datasets provider function.`,name:"custom_megatron_datasets_provider_function"},{anchor:"accelerate.utils.MegatronLMPlugin.custom_get_batch_function",description:`<strong>custom_get_batch_function</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Custom get batch function.`,name:"custom_get_batch_function"},{anchor:"accelerate.utils.MegatronLMPlugin.custom_loss_function",description:`<strong>custom_loss_function</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Custom loss function.`,name:"custom_loss_function"},{anchor:"accelerate.utils.MegatronLMPlugin.other_megatron_args",description:`<strong>other_megatron_args</strong> (<code>Optional</code>, defaults to <code>None</code>) &#x2014;
Other Megatron-LM arguments. Please refer Megatron-LM.`,name:"other_megatron_args"}],source:"https://github.com/huggingface/accelerate/blob/main/src/accelerate/utils/dataclasses.py#L2199"}}),O=new N({props:{title:"MegatronLMDummyScheduler",local:"accelerate.utils.MegatronLMDummyScheduler",headingTag:"h2"}}),q=new T({props:{name:"class accelerate.utils.MegatronLMDummyScheduler",anchor:"accelerate.utils.MegatronLMDummyScheduler",parameters:[{name:"optimizer",val:""},{name:"total_num_steps",val:" = None"},{name:"warmup_num_steps",val:" = 0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"accelerate.utils.MegatronLMDummyScheduler.optimizer",description:`<strong>optimizer</strong> (<code>torch.optim.optimizer.Optimizer</code>) &#x2014;
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