


'''simple docstring'''







import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
   import torch.distributed.checkpoint as dist_cp
   from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
   from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
   from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
   from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


A				:					List[Any]    						=			get_logger(__name__)






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      str  ,lowerCamelCase					:      List[str]  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Tuple=0       ):
       os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
       with FSDP.state_dict_type(
           lowerCamelCase  ,fsdp_plugin.state_dict_type  ,fsdp_plugin.state_dict_config  ,fsdp_plugin.optim_state_dict_config       ):
              _A					:     Optional[Any]     = model.state_dict()
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     _A					:     Any     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
                     _A					:     List[str]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     if accelerator.process_index == 0:
                            logger.info(F'Saving model to {output_model_file}'       )
                            torch.save(lowerCamelCase  ,lowerCamelCase       )
                            logger.info(F'Model saved to {output_model_file}'       )
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     _A					:     int     = (
                         F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     _A					:     List[Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Saving model to {output_model_file}'       )
                     torch.save(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Model saved to {output_model_file}'       )
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     _A					:     List[Any]     = os.path.join(lowerCamelCase  ,F'{MODEL_NAME}_{model_index}'       )
                     os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
                     logger.info(F'Saving model to {ckpt_dir}'       )
                     _A					:     List[str]     = {'model': state_dict}

                     dist_cp.save_state_dict(
                         state_dict=lowerCamelCase  ,storage_writer=dist_cp.FileSystemWriter(lowerCamelCase       )  ,planner=DefaultSavePlanner()  ,)
                     logger.info(F'Model saved to {ckpt_dir}'       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Optional[Any]  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Any  ,lowerCamelCase					:      str=0       ):
       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           lowerCamelCase  ,fsdp_plugin.state_dict_type  ,fsdp_plugin.state_dict_config  ,fsdp_plugin.optim_state_dict_config       ):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if type(lowerCamelCase       ) != FSDP and accelerator.process_index != 0:
                            if not fsdp_plugin.sync_module_states:
                                   raise ValueError(
                                       'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
                                       'initializing FSDP object'       )
                            return
                     _A					:     Optional[Any]     = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
                     _A					:     Any     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Loading model from {input_model_file}'       )
                     _A					:     Dict     = torch.load(lowerCamelCase       )
                     logger.info(F'Model loaded from {input_model_file}'       )
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     _A					:     Dict     = (
                         F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     _A					:     int     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Loading model from {input_model_file}'       )
                     _A					:     Union[str, Any]     = torch.load(lowerCamelCase       )
                     logger.info(F'Model loaded from {input_model_file}'       )
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     _A					:     int     = (
                         os.path.join(lowerCamelCase  ,F'{MODEL_NAME}_{model_index}'       )
                         if F'{MODEL_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(F'Loading model from {ckpt_dir}'       )
                     _A					:     Dict     = {'model': model.state_dict()}
                     dist_cp.load_state_dict(
                         state_dict=lowerCamelCase  ,storage_reader=dist_cp.FileSystemReader(lowerCamelCase       )  ,planner=DefaultLoadPlanner()  ,)
                     _A					:     List[str]     = state_dict['model']
                     logger.info(F'Model loaded from {ckpt_dir}'       )
              model.load_state_dict(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      Any  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      int  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Optional[int]=0       ):
       os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
       with FSDP.state_dict_type(
           lowerCamelCase  ,fsdp_plugin.state_dict_type  ,fsdp_plugin.state_dict_config  ,fsdp_plugin.optim_state_dict_config       ):
              _A					:     List[str]     = FSDP.optim_state_dict(lowerCamelCase  ,lowerCamelCase       )
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if accelerator.process_index == 0:
                            _A					:     Tuple     = (
                                F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                            )
                            _A					:     Optional[int]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                            logger.info(F'Saving Optimizer state to {output_optimizer_file}'       )
                            torch.save(lowerCamelCase  ,lowerCamelCase       )
                            logger.info(F'Optimizer state saved in {output_optimizer_file}'       )
              else:
                     _A					:     Dict     = os.path.join(lowerCamelCase  ,F'{OPTIMIZER_NAME}_{optimizer_index}'       )
                     os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
                     logger.info(F'Saving Optimizer state to {ckpt_dir}'       )
                     dist_cp.save_state_dict(
                         state_dict={'optimizer': optim_state}  ,storage_writer=dist_cp.FileSystemWriter(lowerCamelCase       )  ,planner=DefaultSavePlanner()  ,)
                     logger.info(F'Optimizer state saved in {ckpt_dir}'       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]  ,lowerCamelCase					:      Optional[int]  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      int=0       ):
       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           lowerCamelCase  ,fsdp_plugin.state_dict_type  ,fsdp_plugin.state_dict_config  ,fsdp_plugin.optim_state_dict_config       ):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     _A					:     Any     = None
                     # below check should work but currently it isn't working (mostly opytorch issue),
                     # in the meantime disabling it at the cost of excess memory usage
                     # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
                     _A					:     int     = (
                         F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                     )
                     _A					:     int     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
                     logger.info(F'Loading Optimizer state from {input_optimizer_file}'       )
                     _A					:     Any     = torch.load(lowerCamelCase       )
                     logger.info(F'Optimizer state loaded from {input_optimizer_file}'       )
              else:
                     _A					:     List[str]     = (
                         os.path.join(lowerCamelCase  ,F'{OPTIMIZER_NAME}_{optimizer_index}'       )
                         if F'{OPTIMIZER_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(F'Loading Optimizer from {ckpt_dir}'       )
                     _A					:     Union[str, Any]     = load_sharded_optimizer_state_dict(
                         model_state_dict=model.state_dict()  ,optimizer_key='optimizer'  ,storage_reader=dist_cp.FileSystemReader(lowerCamelCase       )  ,)
                     _A					:     Optional[int]     = optim_state['optimizer']
                     logger.info(F'Optimizer loaded from {ckpt_dir}'       )
              _A					:     str     = FSDP.optim_state_dict_to_load(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )
              optimizer.load_state_dict(lowerCamelCase       )










'''simple docstring'''







import operator as op


A				:					List[str]    						=			'''scaler.pt'''
A				:					Optional[int]    						=			'''pytorch_model'''
A				:					Union[str, Any]    						=			'''random_states'''
A				:					List[Any]    						=			'''optimizer'''
A				:					Optional[Any]    						=			'''scheduler'''
A				:					Optional[int]    						=			'''pytorch_model.bin'''
A				:					List[str]    						=			'''pytorch_model.bin.index.json'''
A				:					List[Any]    						=			'''model.safetensors'''
A				:					int    						=			'''model.safetensors.index.json'''
A				:					List[str]    						=			'''1.10.2'''
A				:					str    						=			'''py38'''
A				:					int    						=			'''4.17.0'''
A				:					Tuple    						=			['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
A				:					Union[str, Any]    						=			['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
A				:					Optional[Any]    						=			['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
A				:					Dict    						=			['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
A				:					Any    						=			['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
A				:					Tuple    						=			'''2.0.1'''
A				:					List[str]    						=			['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
A				:					Union[str, Any]    						=			['''default''', '''reduce-overhead''', '''max-autotune''']

A				:					List[Any]    						=			{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
A				:					Tuple    						=			[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

A				:					List[str]    						=			['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
A				:					Any    						=			['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']






