def get_dynamic_loss_scale_args(param_dict):
loss_scale_args = None
if get_fp16_enabled(param_dict):
fp16_dict = param_dict[FP16]
dynamic_loss_args = [
FP16_INITIAL_SCALE_POWER, FP16_LOSS_SCALE_WINDOW,
FP16_MIN_LOSS_SCALE, FP16_HYSTERESIS
]
if any(arg in list(fp16_dict.keys()) for arg in dynamic_loss_args):
init_scale = get_scalar_param(fp16_dict, FP16_INITIAL_SCALE_POWER,
FP16_INITIAL_SCALE_POWER_DEFAULT)
scale_window = get_scalar_param(fp16_dict, FP16_LOSS_SCALE_WINDOW,
FP16_LOSS_SCALE_WINDOW_DEFAULT)
delayed_shift = get_scalar_param(fp16_dict, FP16_HYSTERESIS,
FP16_HYSTERESIS_DEFAULT)
min_loss_scale = get_scalar_param(fp16_dict, FP16_MIN_LOSS_SCALE,
FP16_MIN_LOSS_SCALE_DEFAULT)
loss_scale_args = {
INITIAL_LOSS_SCALE: 2**init_scale,
SCALE_WINDOW: scale_window,
DELAYED_SHIFT: delayed_shift,
MIN_LOSS_SCALE: min_loss_scale
}
return loss_scale_args
def get_allgather_size(param_dict):
return get_scalar_param(param_dict,
ALLGATHER_SIZE,
ALLGATHER_SIZE_DEFAULT) if get_scalar_param(
param_dict,
ALLGATHER_SIZE,
ALLGATHER_SIZE_DEFAULT) > 0 else ALLGATHER_SIZE_DEFAULT
def _initialize(self, zero_config_dict):
self.stage = get_scalar_param(zero_config_dict,
ZERO_OPTIMIZATION_STAGE,
ZERO_OPTIMIZATION_STAGE_DEFAULT)
self.contiguous_gradients = get_scalar_param(
zero_config_dict, ZERO_OPTIMIZATION_CONTIGUOUS_GRADIENTS,
ZERO_OPTIMIZATION_CONTIGUOUS_GRADIENTS_DEFAULT)
self.reduce_bucket_size = get_scalar_param(
zero_config_dict, ZERO_OPTIMIZATION_REDUCE_BUCKET_SIZE,
ZERO_OPTIMIZATION_REDUCE_BUCKET_SIZE_DEFAULT)
self.reduce_scatter = get_scalar_param(
zero_config_dict, ZERO_OPTIMIZATION_REDUCE_SCATTER,
ZERO_OPTIMIZATION_REDUCE_SCATTER_DEFAULT)
self.overlap_comm = get_scalar_param(
zero_config_dict, ZERO_OPTIMIZATION_OVERLAP_COMM,
ZERO_OPTIMIZATION_OVERLAP_COMM_DEFAULT)
self.allgather_partitions = get_scalar_param(
zero_config_dict, ZERO_OPTIMIZATION_ALLGATHER_PARTITIONS,
ZERO_OPTIMIZATION_ALLGATHER_PARTITIONS_DEFAULT)
self.allgather_bucket_size = get_scalar_param(
zero_config_dict, ZERO_OPTIMIZATION_ALLGATHER_BUCKET_SIZE,
ZERO_OPTIMIZATION_ALLGATHER_BUCKET_SIZE_DEFAULT)
self.load_from_fp32_weights = get_scalar_param(
zero_config_dict, ZERO_OPTIMIZATION_LOAD_FROM_FP32_WEIGHTS,
ZERO_OPTIMIZATION_LOAD_FROM_FP32_WEIGHTS_DEFAULT)
def get_gradient_clipping(param_dict):
grad_clip = get_optimizer_gradient_clipping(param_dict)
if grad_clip is not None:
return grad_clip
else:
return get_scalar_param(param_dict, GRADIENT_CLIPPING,
GRADIENT_CLIPPING_DEFAULT)
def get_loss_scale(param_dict):
if get_fp16_enabled(param_dict):
return get_scalar_param(param_dict[FP16],
FP16_LOSS_SCALE,
FP16_LOSS_SCALE_DEFAULT)
else:
return FP16_LOSS_SCALE_DEFAULT
def get_tensorboard_job_name(param_dict):
if get_tensorboard_enabled(param_dict):
return get_scalar_param(param_dict[TENSORBOARD],
TENSORBOARD_JOB_NAME,
TENSORBOARD_JOB_NAME_DEFAULT)
else:
return TENSORBOARD_JOB_NAME_DEFAULT
def get_tensorboard_enabled(param_dict):
if TENSORBOARD in param_dict.keys():
return get_scalar_param(param_dict[TENSORBOARD],
TENSORBOARD_ENABLED,
TENSORBOARD_ENABLED_DEFAULT)
else:
return False
def get_tensorboard_output_path(param_dict):
if get_tensorboard_enabled(param_dict):
return get_scalar_param(param_dict[TENSORBOARD],
TENSORBOARD_OUTPUT_PATH,
TENSORBOARD_OUTPUT_PATH_DEFAULT)
else:
return TENSORBOARD_OUTPUT_PATH_DEFAULT
def get_initial_dynamic_scale(param_dict):
if get_fp16_enabled(param_dict):
initial_scale_power = get_scalar_param(
param_dict[FP16], FP16_INITIAL_SCALE_POWER,
FP16_INITIAL_SCALE_POWER_DEFAULT)
else:
initial_scale_power = FP16_INITIAL_SCALE_POWER_DEFAULT
return 2**initial_scale_power
def read_zero_config_deprecated(self, param_dict):
zero_config_dict = {}
zero_config_dict[
ZERO_OPTIMIZATION_STAGE] = 1 if param_dict[ZERO_OPTIMIZATION] else 0
if zero_config_dict[ZERO_OPTIMIZATION_STAGE] > 0:
zero_config_dict[ZERO_OPTIMIZATION_ALLGATHER_BUCKET_SIZE] = get_scalar_param(
param_dict,
ZERO_OPTIMIZATION_ALLGATHER_BUCKET_SIZE_DEPRECATED,
ZERO_OPTIMIZATION_ALLGATHER_BUCKET_SIZE_DEFAULT)
logger.warning(
'DeepSpeedConfig: this format of ZeRO optimization setup is deprecated. Please use the following format: {}'
.format(ZERO_FORMAT))
return zero_config_dict
def _initialize(self, act_chkpt_config_dict):
self.partition_activations = get_scalar_param(
act_chkpt_config_dict, ACT_CHKPT_PARTITION_ACTIVATIONS,
ACT_CHKPT_PARTITION_ACTIVATIONS_DEFAULT)
self.contiguous_memory_optimization = get_scalar_param(
act_chkpt_config_dict, ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION,
ACT_CHKPT_CONTIGUOUS_MEMORY_OPTIMIZATION_DEFAULT)
self.cpu_checkpointing = get_scalar_param(
act_chkpt_config_dict, ACT_CHKPT_CPU_CHECKPOINTING,
ACT_CHKPT_CPU_CHECKPOINTING_DEFAULT)
self.number_checkpoints = get_scalar_param(
act_chkpt_config_dict, ACT_CHKPT_NUMBER_CHECKPOINTS,
ACT_CHKPT_NUMBER_CHECKPOINTS_DEFAULT)
self.profile = get_scalar_param(act_chkpt_config_dict,
ACT_CHKPT_PROFILE,
ACT_CHKPT_PROFILE_DEFAULT)
self.synchronize_checkpoint_boundary = get_scalar_param(
act_chkpt_config_dict, ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY,
ACT_CHKPT_SYNCHRONIZE_CHECKPOINT_BOUNDARY_DEFAULT)
def get_sparse_gradients_enabled(param_dict):
return get_scalar_param(param_dict, SPARSE_GRADIENTS,
SPARSE_GRADIENTS_DEFAULT)
def get_gradient_accumulation_steps(param_dict):
return get_scalar_param(param_dict, GRADIENT_ACCUMULATION_STEPS,
GRADIENT_ACCUMULATION_STEPS_DEFAULT)
def get_prescale_gradients(param_dict):
return get_scalar_param(param_dict, PRESCALE_GRADIENTS,
PRESCALE_GRADIENTS_DEFAULT)
def get_allreduce_always_fp32(param_dict):
return get_scalar_param(param_dict, FP32_ALLREDUCE, FP32_ALLREDUCE_DEFAULT)
def get_zero_max_elements_per_comm(param_dict):
return get_scalar_param(param_dict, ZERO_MAX_ELEMENTS_PER_COMM,
ZERO_MAX_ELEMENTS_PER_COMM_DEFAULT)
def get_steps_per_print(param_dict):
return get_scalar_param(param_dict, STEPS_PER_PRINT,
STEPS_PER_PRINT_DEFAULT)
def get_train_micro_batch_size_per_gpu(param_dict):
return get_scalar_param(param_dict, TRAIN_MICRO_BATCH_SIZE_PER_GPU,
TRAIN_MICRO_BATCH_SIZE_PER_GPU_DEFAULT)
def get_fp16_enabled(param_dict):
if FP16 in param_dict.keys():
return get_scalar_param(param_dict[FP16], FP16_ENABLED,
FP16_ENABLED_DEFAULT)
else:
return False
def get_amp_enabled(param_dict):
if AMP in param_dict.keys():
return get_scalar_param(param_dict[AMP], AMP_ENABLED, AMP_ENABLED_DEFAULT)
else:
return False
def get_train_batch_size(param_dict):
return get_scalar_param(param_dict, TRAIN_BATCH_SIZE,
TRAIN_BATCH_SIZE_DEFAULT)
def get_gradient_clipping(param_dict):
return get_scalar_param(param_dict, GRADIENT_CLIPPING,
GRADIENT_CLIPPING_DEFAULT)
def get_dump_state(param_dict):
return get_scalar_param(param_dict, DUMP_STATE, DUMP_STATE_DEFAULT)
def get_disable_allgather(param_dict):
return get_scalar_param(param_dict, DISABLE_ALLGATHER,
DISABLE_ALLGATHER_DEFAULT)
def get_zero_optimization(param_dict):
return get_scalar_param(param_dict, ZERO_OPTIMIZATION,
ZERO_OPTIMIZATION_DEFAULT)
def get_wall_clock_breakdown(param_dict):
return get_scalar_param(param_dict, WALL_CLOCK_BREAKDOWN,
WALL_CLOCK_BREAKDOWN_DEFAULT)
def get_zero_reduce_scatter(param_dict):
return get_scalar_param(param_dict, ZERO_REDUCE_SCATTER,
ZERO_REDUCE_SCATTER_DEFAULT)
def get_memory_breakdown(param_dict):
return get_scalar_param(param_dict, MEMORY_BREAKDOWN,
MEMORY_BREAKDOWN_DEFAULT)
def get_zero_allow_untested_optimizer(param_dict):
return get_scalar_param(param_dict, ZERO_ALLOW_UNTESTED_OPTIMIZER,
ZERO_ALLOW_UNTESTED_OPTIMIZER_DEFAULT)
def get_gradient_predivide_factor(param_dict):
return get_scalar_param(param_dict, GRADIENT_PREDIVIDE_FACTOR,
GRADIENT_PREDIVIDE_FACTOR_DEFAULT)
