Making large AI models cheaper, faster and more accessible
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from .mixed_precision_base import MixedPrecision
class FP16NaiveMixedPrecision(MixedPrecision):
"""
Precision for mixed precision training in FP16 using naive AMP.
Args:
log_num_zeros_in_grad(bool): return number of zeros in the gradients.
initial_scale(int): initial scale of gradient scaler.
growth_factor(int): the growth rate of loss scale.
backoff_factor(float): the decrease rate of loss scale.
hysteresis(int): delay shift in dynamic loss scaling.
max_scale(int): maximum loss scale allowed.
verbose(bool): if set to `True`, will print debug info.
"""
def __init__(
self,
log_num_zeros_in_grad: bool,
initial_scale: int,
growth_factor: int,
backoff_factor: float,
hysteresis: int,
max_scale: int,
verbose: bool = None,
) -> None:
pass