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    This is a custom help action that will hide all arguments that are not used in the command line when the help is
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accelerater   launch      Hardware Selection ArgumentsResource Selection ArgumentsTraining Paradigm Argumentszpositional argumentszoptional argumentsc                 S   s   g | ]}|t  kr|qS r0   )options_to_groupkeys.0argr0   r0   r1   
<listcomp>e   s      z._CustomHelpAction.__call__.<locals>.<listcomp>c                 S   s   g | ]}t | qS r0   )r:   )r=   or0   r0   r1   r?   g   s     helpz (currently selected)c                 S   s   g | ]}|j tjkqS r0   )rA   argparseSUPPRESSr<   r0   r0   r1   r?   z   s     )sysargv_actionslenlistmapr2   	enumerate	containertitlesetattrrB   rC   setoption_strings
isdisjointrA   _action_groupsall_group_actionsremovesuper__call__)selfparser	namespacevaluesoption_stringargsoptstitlesZused_platformsZused_titlesir>   group	__class__r0   r1   rV   W   s:     z_CustomHelpAction.__call__)N)__name__
__module____qualname____doc__rV   __classcell__r0   r0   ra   r1   r3   P   s   r3   c                 C   s  | d k	r| j dddd}ntjdddd}|ddt |jdddd	d
 |jdd dd |jddddd
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d3d4 |jd5ddd6d |jd7ddd8d |d9d:}|jd;ddd<d |jd=ddd>d |jd?ddd@d |jdAdddBd |dCdD}|jdEd dFd |jdGdddHd |jdItd dJd( |jdKtd dLd( |jdMtd dNd( |jdOdPdQtdRdS |jdTtd2dUd( |jdVtdWdXd( |jdYtdZd[d( |jd\td]d^d( |jd_td`dad( |jdbdcdddd
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 |jdtddύ |jdtjddҍ | d k	r|jtdӍ |S )Nr4   F)add_helpallow_abbrevzAccelerate launch commandactionrA   z-hz--helpz Show this help message and exit.)rj   rA   z--config_filezFThe config file to use for the default values in the launching script.)defaultrA   z--quietz-q
store_truezSilence subprocess errors from the launch stack trace and only show the relevant tracebacks. (Only applicable to DeepSpeed and single-process configurations)r7   z0Arguments for selecting the hardware to be used.z--cpuz0Whether or not to force the training on the CPU.)rk   rj   rA   z--multi_gpuz=Whether or not this should launch a distributed GPU training.r&   z1Whether or not this should launch a TPU training.z--ipexzLWhether or not this should launch a Intel PyTorch Extension (IPEX) training.r8   z@Arguments for fine-tuning how available hardware should be used.--mixed_precisionnoZfp16bf16Zfp8zWhether or not to use mixed precision training. Choose between FP16 and BF16 (bfloat16) training. BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.)typechoicesrA   --num_processesz9The total number of processes to be launched in parallel.)rp   rk   rA   --num_machinesz3The total number of machines used in this training.z--num_cpu_threads_per_processzLThe number of CPU threads per process. Can be tuned for optimal performance.--dynamo_backendc                 S   s   g | ]}|  qS r0   )lower)r=   br0   r0   r1   r?      s     z)launch_command_parser.<locals>.<listcomp>zkChoose a backend to optimize your training with dynamo, see more at https://github.com/pytorch/torchdynamo.z--dynamo_moderk   z4Choose a mode to optimize your training with dynamo.)rp   rk   rq   rA   z--dynamo_use_fullgraphz[Whether to use full graph mode for dynamo or it is ok to break model into several subgraphsz--dynamo_use_dynamicz(Whether to enable dynamic shape tracing.r9   z;Arguments for selecting which training paradigm to be used.r'   zWhether to use deepspeed.r(   zWhether to use fsdp.r)   zWhether to use Megatron-LM.z	--use_xpuzDWhether to use IPEX plugin to speed up training on XPU specifically.r!   z.Arguments related to distributed GPU training.z	--gpu_idszWWhat GPUs (by id) should be used for training on this machine as a comma-seperated listz--same_networkzQWhether all machines used for multinode training exist on the same local network.z--machine_rankz9The rank of the machine on which this script is launched.z--main_process_ipz(The IP address of the machine of rank 0.z--main_process_portz:The port to use to communicate with the machine of rank 0.z-tz--tee0z4Tee std streams into a log file and also to console.)rk   rp   rA   z--rolez"User-defined role for the workers.z--rdzv_backendZstaticzFThe rendezvous method to use, such as 'static' (the default) or 'c10d'z--rdzv_conf zJAdditional rendezvous configuration (<key1>=<value1>,<key2>=<value2>,...).z--max_restartsr   z7Maximum number of worker group restarts before failing.z--monitor_interval   z6Interval, in seconds, to monitor the state of workers.z-mz--modulezwChange each process to interpret the launch script as a Python module, executing with the same behavior as 'python -m'.z--no_pythonz|Skip prepending the training script with 'python' - just execute it directly. Useful when the script is not a Python script.r"   zArguments related to TPU.z--tpu_clustertpu_use_clusterz*Whether to use a GCP TPU pod for training.)rj   destrA   --no_tpu_clusterstore_falsez?Should not be passed explicitly, this is for internal use only.z--tpu_use_sudozGWhether to use `sudo` when running the TPU training script in each pod.z--vmappendzkList of single Compute VM instance names. If not provided we assume usage of instance groups. For TPU pods.)rp   rj   rA   z--envzOList of environment variables to set on the Compute VM instances. For TPU pods.--main_training_functionzTThe name of the main function to be executed in your script (only for TPU training).z--downcast_bf16zWhether when using bf16 precision on TPUs if both float and double tensors are cast to bfloat16 or if double tensors remain as float32.r#   zArguments related to DeepSpeed.z--deepspeed_config_filezDeepSpeed config file.z--zero_stagez{DeepSpeed's ZeRO optimization stage (useful only when `use_deepspeed` flag is passed). If unspecified, will default to `2`.z--offload_optimizer_devicezDecides where (none|cpu|nvme) to offload optimizer states (useful only when `use_deepspeed` flag is passed). If unspecified, will default to 'none'.z--offload_param_devicezDecides where (none|cpu|nvme) to offload parameters (useful only when `use_deepspeed` flag is passed). If unspecified, will default to 'none'.z--offload_optimizer_nvme_pathzDecides Nvme Path to offload optimizer states (useful only when `use_deepspeed` flag is passed). If unspecified, will default to 'none'.z--offload_param_nvme_pathzDecides Nvme Path to offload parameters (useful only when `use_deepspeed` flag is passed). If unspecified, will default to 'none'.z--gradient_accumulation_stepszNo of gradient_accumulation_steps used in your training script (useful only when `use_deepspeed` flag is passed). If unspecified, will default to `1`.z--gradient_clippingzgradient clipping value used in your training script (useful only when `use_deepspeed` flag is passed). If unspecified, will default to `1.0`.z--zero3_init_flagzDecides Whether (true|false) to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with DeepSpeed ZeRO Stage-3. If unspecified, will default to `true`.z--zero3_save_16bit_modelzDecides Whether (true|false) to save 16-bit model weights when using ZeRO Stage-3. Only applicable with DeepSpeed ZeRO Stage-3. If unspecified, will default to `false`.z--deepspeed_hostfilez@DeepSpeed hostfile for configuring multi-node compute resources.z--deepspeed_exclusion_filterz>DeepSpeed exclusion filter string when using mutli-node setup.z--deepspeed_inclusion_filterz>DeepSpeed inclusion filter string when using mutli-node setup.z--deepspeed_multinode_launcherzMDeepSpeed multi-node launcher to use. If unspecified, will default to `pdsh`.r$   z3Arguments related to Fully Shared Data Parallelism.z--fsdp_offload_paramsfalsezvDecides Whether (true|false) to offload parameters and gradients to CPU. (useful only when `use_fsdp` flag is passed).z--fsdp_min_num_paramsg    חAzlFSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `use_fsdp` flag is passed).z--fsdp_sharding_strategyr5   zGFSDP's Sharding Strategy. (useful only when `use_fsdp` flag is passed).z--fsdp_auto_wrap_policyzFFSDP's auto wrap policy. (useful only when `use_fsdp` flag is passed).z$--fsdp_transformer_layer_cls_to_wrapzTransformer layer class name (case-sensitive) to wrap ,e.g, `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `use_fsdp` flag is passed).z--fsdp_backward_prefetch_policyzNFSDP's backward prefetch policy. (useful only when `use_fsdp` flag is passed).z--fsdp_state_dict_typezEFSDP's state dict type. (useful only when `use_fsdp` flag is passed).z--fsdp_forward_prefetchzIf True, then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass (useful only when `use_fsdp` flag is passed).z--fsdp_use_orig_paramszIf True, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable paramteres. (useful only when `use_fsdp` flag is passed).z--fsdp_sync_module_statestruezIf True, each individually wrapped FSDP unit will broadcast module parameters from rank 0. (useful only when `use_fsdp` flag is passed).r%   z!Arguments related to Megatron-LM.z--megatron_lm_tp_degreezbMegatron-LM's Tensor Parallelism (TP) degree. (useful only when `use_megatron_lm` flag is passed).z--megatron_lm_pp_degreezdMegatron-LM's Pipeline Parallelism (PP) degree. (useful only when `use_megatron_lm` flag is passed).z--megatron_lm_num_micro_batchesznMegatron-LM's number of micro batches when PP degree > 1. (useful only when `use_megatron_lm` flag is passed).z"--megatron_lm_sequence_parallelismzDecides Whether (true|false) to enable Sequence Parallelism when TP degree > 1. (useful only when `use_megatron_lm` flag is passed).z#--megatron_lm_recompute_activationszDecides Whether (true|false) to enable Selective Activation Recomputation. (useful only when `use_megatron_lm` flag is passed).z'--megatron_lm_use_distributed_optimizerzDecides Whether (true|false) to use distributed optimizer which shards optimizer state and gradients across Data Pralellel (DP) ranks. (useful only when `use_megatron_lm` flag is passed).z--megatron_lm_gradient_clippingg      ?zMegatron-LM's gradient clipping value based on global L2 Norm (0 to disable). (useful only when `use_megatron_lm` flag is passed).zAWS ArgumentszArguments related to AWS.z--aws_access_key_idzFThe AWS_ACCESS_KEY_ID used to launch the Amazon SageMaker training jobz--aws_secret_access_keyzKThe AWS_SECRET_ACCESS_KEY used to launch the Amazon SageMaker training job.z--debugzLWhether to print out the torch.distributed stack trace when something fails.training_scriptznThe full path to the script to be launched in parallel, followed by all the arguments for the training script.)rp   rA   training_script_argsz!Arguments of the training script.)nargsrA   )func)
add_parserrB   ArgumentParserregisterr3   add_argumentadd_argument_groupstrintr   r   float	REMAINDERset_defaultslaunch_command)Z
subparsersrX   Zhardware_argsZresource_argsZparadigm_argsZdistributed_argsZtpu_argsZdeepspeed_argsZ	fsdp_argsZmegatron_lm_argsZaws_argsr0   r0   r1   launch_command_parser   s             
             			

r   c                 C   sR   t | \}}tj||d}|  |jdkrN| jsDtj|j|dn
td d S )Nenvr   
returncodecmdr5   )	r   
subprocessPopenwaitr   quietCalledProcessErrorrD   exit)r\   r   current_envprocessr0   r0   r1   simple_launcherm  s    
r   c              	   C   s   dd l m  m} t| }t| dd}t| | d| jd| jg} t	f |X z||  W nB t
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r   t r|rt }|d |jtgdd n Y nX W 5 Q R X d S )Nr   debugF--training_script--training_script_argsE
[bold red]Using --debug, `torch.distributed` Stack Trace:[/bold red]suppressshow_locals)torch.distributed.rundistributedrunr   getattrr   get_args_parserr   r   r   	Exceptionr   r   printprint_exception__file__)r\   distrib_runr   r   consoler0   r0   r1   multi_gpu_launchery  s"    

r   c           
   	   C   sr  dd l m  m} t s tdt| \}}| jdkr| jtd krt	dd@}|
 D ]0\}}d|ksXd|krrqX|| d| d	 qXW 5 Q R X tj||d
}|  |jdkr| jstj|j|dn
td nt| dd}t| | d| jd| jg} tf |^ z||  W nH tk
rb   t r\|r\t }	|	d |	jtgdd n Y nX W 5 Q R X d S )Nr   zSDeepSpeed is not installed => run `pip3 install deepspeed` or build it from source.r5   z.deepspeed_enva; =
r   r   r   Fr   r   r   r   ) r   r   r   r   ImportErrorr   num_machinesZdeepspeed_multinode_launcherr   openitemswriter   r   r   r   r   r   rD   r   r   r   r   r   r   r   r   r   r   r   r   r   )
r\   r   r   r   fkeyvaluer   r   r   r0   r0   r1   deepspeed_launcher  s>    "

r   c              	   C   s   dd l m  m} | jr tdt| i \} }| jr<| j}n&t| j}t	j
t|j  |j}t|}t|| jstd| j d|jg| j t	_t|| j}tf | |jt|d| jd W 5 Q R X d S )Nr   z,--no_python cannot be used with TPU launcherz2Your training script should have a function named zE, or you should pass a different value to `--main_training_function`.r0   )r\   Znprocs)Z)torch_xla.distributed.xla_multiprocessingr   Zxla_multiprocessing	no_python
ValueErrorr   moduler   r   rD   pathr~   r   parentresolvestem	importlibimport_modulehasattrmain_training_functionr   r   rE   r   r   spawnr
   num_processes)r\   Zxmpr   mod_nameZscript_pathmodZmain_functionr0   r0   r1   tpu_launcher  s$    

r   c                 C   s  ddl m} i }t| |d\} }t| dd}| j}| j}t| | d| jddd	g}| j	rbd
g}ng }|ddddt
ddddddt
| jdt
| j|g| 7 }||_d}t|D ]<}	|	drt||	}
|
dkr|
d k	r||	 d|
 d7 }q|dkrtd| dd | D |_|jd z|| W nH tk
r|   t rv|rvt }|d |jtgdd n Y nX d S )Nr   )xla_distTr   Fr&   z--positionalrx   z--restart-tpuvm-pod-serverZsudozaccelerate-launchr|   rs   r5   rm   rn   rt   rr   r   Zdocker_z="z"
zgDocker containers are not supported for TPU pod launcher currently, please remove the following flags:
c                 S   s   g | ]\}}| d | qS )r   r0   )r=   kvr0   r0   r1   r?     s     z$tpu_pod_launcher.<locals>.<listcomp>zACCELERATE_IN_TPU_POD=1zF
[bold red]Using --debug, `torch_xla.xla_dist` Stack Trace:[/bold red]r   )Ztorch_xla.distributedr   r   r   r   r   r   r   Ztpu_nameZtpu_use_sudor   r   r   
positionalvarsr-   r   r   r   r~   Zresolve_and_executer   r   r   r   r   r   )r\   r   r   r   r   r   new_argsZnew_cmdZ	bad_flagsr>   r   r   r0   r0   r1   tpu_pod_launcher  sj      



r   )sagemaker_configc                 C   sf   t  std|js|jr"tdddlm} t| |\}}|f |}|j|d t	d|j
  d S )NzsPlease install sagemaker to be able to launch training on Amazon SageMaker with `pip install accelerate[sagemaker]`z`SageMaker requires a python training script file and cannot be used with --module or --no_pythonr   )HuggingFace)inputsz!You can find your model data at: )r   r   r   r   r   Zsagemaker.huggingfacer   r   fitr   Z
model_data)r   r\   r   Zsagemaker_inputsZhuggingface_estimatorr0   r0   r1   sagemaker_launcher  s    
r   c                 C   s  t | j| j| j| j| jgdkr(td| jrJ| jd k	rJ| jdk rJtdd }g }d}| jd k	svt	j
trl| jslt| j}| js| js| js| js| js| js|jtjk| _|jtjtjtjfkrdnd| _|jtjk| _|jtjk| _|jtjk| _| jr|jnd| _| jd krD|jd k	r>|j| _nd| _| jr`| jd kr`|j| _t| jddk r| jdkr| jr| jdkrtd	|jtjkr|j ! D ]\}}t"|t#r|j$D ]}t%| ||j$|  q|j&D ].}|}d
|kr
d| }t%| ||j&|  q|j'D ]}t%| ||j'|  q&|j(D ]}t%| ||j(|  qF|j)D ]}t%| ||j)|  qfq|dkrt*| |d d krt%| || q| j+s|j+| _+| j,s|j,d krd| _,n|j,| _,d}nld}d}	| j-s| j.rt/j01 rt2dd}nt3d}| j,dkrV|sV| jrDt4 sVt|	j5ddd| j6d krd| _6n:| jd kr| j.rt7 rt/j08 | _n"t9 rt/j:8 | _nt/j;8 | _|<d| j d | j+d krd| _+| js>| j.rt7 rt/j08 dks.t9 rt/j:8 dks.t/j;8 dkr>|<d d| _| jd krZ|<d d| _| j,d krv|<d d| _,t=| ds| j| _-| j6d kr|<d d| _6| j+rt>+d |d kp|d k	o|jtj?k}
|
rL| j@d krLd| _@| j-rL| jdkrLtAdddgd}tBtCjDdd| }|dkrL|| _@|<d | j@ d! tE|rzd"}|d#F|7 }|d$7 }t>G| | ||fS )%Nr5   zcYou can only use one of `--cpu`, `--multi_gpu`, `--tpu`, `--use_deepspeed`, `--use_fsdp` at a time.r6   z:You need to use at least 2 processes to use `--multi_gpu`.FTrR   ,zLess than two GPU ids were configured and tried to run on on multiple GPUs. Please ensure at least two are specified for `--gpu_ids`, or use `--gpu_ids='all'`.ZfsdpZfsdp_)compute_environmentmixed_precisiondistributed_typern   z-{mode} mixed precision requires {requirement}z>=z1.10ro   z'PyTorch >= 1.10 and a supported device.)moderequirementz*	`--num_processes` was set to a value of ``zx		More than one GPU was found, enabling multi-GPU training.
		If this was unintended please pass in `--num_processes=1`.z+	`--num_machines` was set to a value of `1`z1	`--mixed_precision` was set to a value of `'no'`use_cpuz0	`--dynamo_backend` was set to a value of `'no'`zRRunning script in debug mode, expect distributed operations to be slightly slower.ZMPI_LOCALNRANKSZOMPI_COMM_WORLD_LOCAL_SIZEZMV2_COMM_WORLD_LOCAL_SIZE)Zlogicalz-	`--num_cpu_threads_per_process` was set to `z9` to improve out-of-box performance when training on CPUsz[The following values were not passed to `accelerate launch` and had defaults used instead:
r   zh
To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.)Hsum	multi_gpucputpuuse_deepspeeduse_fsdpr   r   config_fileosr   isfiler   r   rz   use_megatron_lmr   r	   Z	DEEPSPEEDZ	MULTI_GPUZ	MULTI_NPUZ	MULTI_XPUr"   ZFSDPZMEGATRON_LMZgpu_idsr   rG   splitr   r   ZLOCAL_MACHINE__dict__r   
isinstancedictdeepspeed_configrM   Zfsdp_configZmegatron_lm_configZdynamo_configZipex_configr   r   r   r   Zuse_xputorchZxpuZis_availabler   r   r   r   Zdynamo_backendr   Zdevice_countr   Znpucudar~   r   loggerAMAZON_SAGEMAKERZnum_cpu_threads_per_processr   r   psutil	cpu_countanyjoinwarning)r\   defaultsZwarnedmp_from_config_flagnameattrr   Z
arg_to_setZ
native_amperrZis_aws_env_disabledZ
local_sizeZthreads_per_processmessager0   r0   r1   _validate_launch_command'  s"     

6





"





 


r  c                 C   s   t | \} }}| jrZ| jsZ|r,t|j ng | _|rB| jd d| j| _t	|  n| j
rp| jspt|  nz| jr| jst|  nd| jr| jst|  nN| jr| js| jrt|  qt|  n(|d k	r|jtjkrt||  nt|  d S )Nr   r   )r  r   r   rH   r   r;   Z'deepspeed_fields_from_accelerate_configr~   r   r   r   r   r   r   r   rz   r   r   r   r   r   r   r   )r\   r   r   r0   r0   r1   r     s(    





r   c                  C   s   t  } |  }t| d S )N)r   
parse_argsr   )rX   r\   r0   r0   r1   main  s    r  __main__)N)@rB   r   loggingr   r   rD   pathlibr   r   r   Zaccelerate.commands.configr   r   Z&accelerate.commands.config.config_argsr   Z'accelerate.commands.config.config_utilsr   Zaccelerate.stater   Zaccelerate.utilsr   r	   r
   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   Zaccelerate.utils.constantsr   r   richr   Zrich.loggingr   ZFORMATbasicConfig	getLoggerrc   r   r:   r2   _HelpActionr3   r   r   r   r   r   r   r   r  r   r  r0   r0   r0   r1   <module>   sX   P
	0
   p); 
