code stringlengths 3 6.57k |
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dist.get_rank() |
torch.device("cuda") |
_configure_with_arguments(self, args, mpu) |
int(os.environ['LOCAL_RANK']) |
hasattr(args, 'local_rank') |
DeepSpeedConfig(config_file, mpu, param_dict=self.config_params) |
_do_args_sanity_check(self, args) |
hasattr(args, 'deepscale_config') |
hasattr(args, 'deepspeed_config') |
hasattr(args, 'local_rank') |
isinstance(args.local_rank, int) |
type(args.local_rank) |
int(os.environ.get("LOCAL_RANK") |
hasattr(args, 'deepspeed_config') |
os.path.isfile(args.deepspeed_config) |
format(args.deepspeed_config) |
_is_supported_optimizer(self, optimizer_name) |
getattr(torch.optim, optimizer_name, None) |
_do_sanity_check(self) |
self.optimizer_name() |
self._is_supported_optimizer(self.optimizer_name() |
format(self.optimizer_name() |
self.optimizer_name() |
self.dynamic_loss_scale() |
format(self.optimizer_name() |
_broadcast_model(self) |
is_replicated(p) |
hasattr(p, 'ds_status') |
self.module.parameters() |
torch.is_tensor(p) |
is_replicated(p) |
_configure_distributed_model(self, model) |
self.fp16_enabled() |
self.module.half() |
self.module.to(self.device) |
_initialize_parameter_parallel_groups() |
dist.get_world_size() |
self.mpu.get_data_parallel_group() |
self.mpu.get_data_parallel_world_size() |
self.mpu.get_model_parallel_world_size() |
self.mpu.get_data_parallel_group() |
self.amp_enabled() |
self._broadcast_model() |
_configure_optimizer(self, client_optimizer, model_parameters) |
len(pg["params"]) |
logger.info('Using client Optimizer as basic optimizer') |
self._configure_basic_optimizer(model_parameters) |
self.optimizer_name() |
self.zero_optimization() |
self.amp_enabled() |
use (legacy) |
is_zero_supported_optimizer(basic_optimizer) |
self.zero_allow_untested_optimizer() |
self._configure_zero_optimizer(basic_optimizer) |
self.amp_enabled() |
self.fp16_enabled() |
with (legacy) |
self.amp_params() |
logger.info(f"Initializing AMP with these params: {amp_params}") |
logger.info("Initializing Apex amp from: {}".format(amp.__path__) |
amp.initialize(self.module, basic_optimizer, **amp_params) |
self._broadcast_model() |
self.fp16_enabled() |
self._configure_fp16_optimizer(basic_optimizer) |
log_dist('DeepSpeed Final Optimizer = {}'.format(self.optimizer_name() |
_configure_basic_optimizer(self, model_parameters) |
self.optimizer_params() |
print(optimizer_parameters.keys() |
optimizer_parameters.keys() |
self.optimizer_name() |
optimizer_parameters.pop(TORCH_ADAM_PARAM, False) |
optimizer_parameters.pop(ADAM_W_MODE, ADAM_W_MODE_DEFAULT) |
self.zero_cpu_offload() |
self.optimizer_name() |
FusedLamb(model_parameters, **optimizer_parameters) |
self.optimizer_name() |
OnebitAdam(model_parameters, self, **optimizer_parameters) |
self.fp16_enabled() |
getattr(torch.optim, self.optimizer_name() |
torch_optimizer(model_parameters, **optimizer_parameters) |
_configure_fp16_optimizer(self, optimizer) |
self.initial_dynamic_scale() |
self.dynamic_loss_scale_args() |
self.gradient_clipping() |
self.optimizer_name() |
self.dynamic_loss_scale() |
log_dist('Creating fp16 optimizer with dynamic loss scale', ranks=[0]) |
self.wall_clock_breakdown() |
self.optimizer_legacy_fusion() |
self.loss_scale() |
self.loss_scale() |
self.optimizer_legacy_fusion() |
self.loss_scale() |
self.dynamic_loss_scale() |
self.optimizer_name() |
_configure_zero_optimizer(self, optimizer) |
self.zero_optimization_stage() |
log_dist('Creating fp16 ZeRO stage {} optimizer'.format(zero_stage) |
self.allreduce_always_fp32() |
self.wall_clock_breakdown() |
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