# Copyright (c) 2020 Ross Wightman
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
"""LAMB optimizer
References: https://github.com/rwightman/pytorch-image-models/blob/master/timm/optim/lamb.py
"""
import os
from typing import Iterable, Tuple, Union
from megengine.core._imperative_rt.core2 import apply
from megengine.core.ops.builtin import LAMBUpdate
from .. import Parameter, tensor
from ..functional import sum
from ..functional.inplace import _inplace_add_
from .optimizer import Optimizer
[docs]class LAMB(Optimizer):
r"""Implements LAMB algorithm.
LAMB is proposed in `"Large Batch Optimization for Deep Learning: Training BERT in 76 minutes"
<https://arxiv.org/abs/1904.00962>`_.
Args:
params: iterable of parameters to optimize or dicts defining parameter groups.
lr: learning rate.
betas: coefficients used for computing running averages of gradient and its square.
Default: ``(0.9, 0.999)``
eps: term added to the denominator to improve numerical stability. Default: ``1e-8``
bias_correction: enables bias correction by ``1 - beta ** step``. Default: ``True``
weight_decay: weight decay (L2 penalty). Default: ``0.0``
always_adapt: apply adaptive lr to ``0.0`` weight decay parameter. Default: ``False``
"""
def __init__(
self,
params: Union[Iterable[Parameter], dict],
lr: float,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-8,
bias_correction: bool = True,
weight_decay: float = 0.0,
always_adapt: bool = False,
):
if lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if weight_decay < 0.0:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
defaults = dict(lr=lr, weight_decay=weight_decay, betas=betas, eps=eps)
super().__init__(params, defaults)
self.bias_correction = bias_correction
self.always_adapt = always_adapt
self._disable_type_convert = True
def _create_state(self, param_group):
for param in param_group["params"]:
self._add_state(param, "exp_avg")
self._add_state(param, "exp_avg_sq")
self._add_state(param, "step", initializer=0.0, dtype="float32")
def _updates(self, param_group):
lr = param_group["lr"]
weight_decay = param_group["weight_decay"]
eps = param_group["eps"]
beta0, beta1 = param_group["betas"]
# since `conver_inputs` is disabled for param updates,
# scalar should be explicitly tansforred to tensor
c1 = tensor(1.0)
for param in param_group["params"]:
if param.grad is None:
continue
grad = param.grad
states = self._state[param]
step, exp_avg, exp_avg_sq = (
states["step"],
states["exp_avg"],
states["exp_avg_sq"],
)
step += c1
op = LAMBUpdate(
beta0,
beta1,
int(step),
lr,
weight_decay,
eps,
self.bias_correction,
self.always_adapt,
)
new_exp_avg, new_exp_avg_sq, new_param = apply(
op, exp_avg, exp_avg_sq, param, grad
)
param._reset(new_param)
exp_avg._reset(new_exp_avg)
exp_avg_sq._reset(new_exp_avg_sq)
[docs]class LAMBFp16(LAMB):
def _create_state(self, param_group):
for param in param_group["params"]:
self._add_state(param, "exp_avg", dtype="float32")
self._add_state(param, "exp_avg_sq", dtype="float32")
self._add_state(param, "step", initializer=0.0, dtype="float32")
self._state[param]["param_fp32"] = param.astype("float32")
def _updates(self, param_group):
lr = param_group["lr"]
weight_decay = param_group["weight_decay"]
eps = param_group["eps"]
beta0, beta1 = param_group["betas"]
c1 = tensor(1.0)
for param in param_group["params"]:
if param.grad is None:
continue
grad = param.grad
states = self._state[param]
step, exp_avg, exp_avg_sq = (
states["step"],
states["exp_avg"],
states["exp_avg_sq"],
)
step += c1
fp32_param = states["param_fp32"]
op = LAMBUpdate(
beta0,
beta1,
step,
lr,
weight_decay,
eps,
self.bias_correction,
self.always_adapt,
)
new_exp_avg, new_exp_avg_sq, new_param = apply(
op, exp_avg, exp_avg_sq, fp32_param, grad
)
fp32_param._reset(new_param)
param._reset(new_param.astype("float16"))
exp_avg._reset(new_exp_avg)
exp_avg_sq._reset(new_exp_avg_sq)