|
import math
|
|
import torch
|
|
from transformers import Adafactor
|
|
|
|
@torch.no_grad()
|
|
def adafactor_step_param(self, p, group):
|
|
if p.grad is None:
|
|
return
|
|
grad = p.grad
|
|
if grad.dtype in {torch.float16, torch.bfloat16}:
|
|
grad = grad.float()
|
|
if grad.is_sparse:
|
|
raise RuntimeError("Adafactor does not support sparse gradients.")
|
|
|
|
state = self.state[p]
|
|
grad_shape = grad.shape
|
|
|
|
factored, use_first_moment = Adafactor._get_options(group, grad_shape)
|
|
|
|
if len(state) == 0:
|
|
state["step"] = 0
|
|
|
|
if use_first_moment:
|
|
|
|
state["exp_avg"] = torch.zeros_like(grad)
|
|
if factored:
|
|
state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
|
|
state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad)
|
|
else:
|
|
state["exp_avg_sq"] = torch.zeros_like(grad)
|
|
|
|
state["RMS"] = 0
|
|
else:
|
|
if use_first_moment:
|
|
state["exp_avg"] = state["exp_avg"].to(grad)
|
|
if factored:
|
|
state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad)
|
|
state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad)
|
|
else:
|
|
state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
|
|
|
|
p_data_fp32 = p
|
|
if p.dtype in {torch.float16, torch.bfloat16}:
|
|
p_data_fp32 = p_data_fp32.float()
|
|
|
|
state["step"] += 1
|
|
state["RMS"] = Adafactor._rms(p_data_fp32)
|
|
lr = Adafactor._get_lr(group, state)
|
|
|
|
beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
|
|
update = (grad ** 2) + group["eps"][0]
|
|
if factored:
|
|
exp_avg_sq_row = state["exp_avg_sq_row"]
|
|
exp_avg_sq_col = state["exp_avg_sq_col"]
|
|
|
|
exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t))
|
|
exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t))
|
|
|
|
|
|
update = Adafactor._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
|
|
update.mul_(grad)
|
|
else:
|
|
exp_avg_sq = state["exp_avg_sq"]
|
|
|
|
exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t))
|
|
update = exp_avg_sq.rsqrt().mul_(grad)
|
|
|
|
update.div_((Adafactor._rms(update) / group["clip_threshold"]).clamp_(min=1.0))
|
|
update.mul_(lr)
|
|
|
|
if use_first_moment:
|
|
exp_avg = state["exp_avg"]
|
|
exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"]))
|
|
update = exp_avg
|
|
|
|
if group["weight_decay"] != 0:
|
|
p_data_fp32.add_(p_data_fp32, alpha=(-group["weight_decay"] * lr))
|
|
|
|
p_data_fp32.add_(-update)
|
|
|
|
if p.dtype in {torch.float16, torch.bfloat16}:
|
|
p.copy_(p_data_fp32)
|
|
|
|
|
|
@torch.no_grad()
|
|
def adafactor_step(self, closure=None):
|
|
"""
|
|
Performs a single optimization step
|
|
|
|
Arguments:
|
|
closure (callable, optional): A closure that reevaluates the model
|
|
and returns the loss.
|
|
"""
|
|
loss = None
|
|
if closure is not None:
|
|
loss = closure()
|
|
|
|
for group in self.param_groups:
|
|
for p in group["params"]:
|
|
adafactor_step_param(self, p, group)
|
|
|
|
return loss
|
|
|
|
def patch_adafactor_fused(optimizer: Adafactor):
|
|
optimizer.step_param = adafactor_step_param.__get__(optimizer)
|
|
optimizer.step = adafactor_step.__get__(optimizer)
|
|
|
