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Zero
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
# AnyPrecisionAdamW: a flexible precision AdamW optimizer
# with optional Kahan summation for high precision weight updates.
# Allows direct control over momentum, variance and auxiliary compensation
# buffer dtypes.
# Optional Kahan summation is used to offset precision reduction for
# the weight updates. This allows full training in BFloat16 (equal or
# better than FP32 results in many cases) due to high precision weight upates.
import torch
from torch.optim.optimizer import Optimizer
class AnyPrecisionAdamW(Optimizer):
def __init__(
self,
params,
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0.0,
use_kahan_summation=False,
momentum_dtype=torch.bfloat16,
variance_dtype=torch.bfloat16,
compensation_buffer_dtype=torch.bfloat16,
):
"""
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay coefficient (default: 1e-2)
# Any Precision specific
use_kahan_summation = creates auxiliary buffer to ensure high precision
model param updates (default: False)
momentum_dtype = dtype for momentum (default: BFloat32)
variance_dtype = dtype for uncentered variance (default: BFloat16)
compensation_buffer_dtype = dtype for Kahan summation
buffer (default: BFloat16)
# Usage
This optimizer implements optimizer states, and Kahan summation
for high precision updates, all in user controlled dtypes.
Defaults are variance in BF16, Momentum in FP32.
This can be run in FSDP mixed precision, amp, or full precision,
depending on what training pipeline you wish to work with.
Setting to use_kahan_summation = False, and changing momentum and
variance dtypes to FP32, reverts this to a standard AdamW optimizer.
"""
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
use_kahan_summation=use_kahan_summation,
momentum_dtype=momentum_dtype,
variance_dtype=variance_dtype,
compensation_buffer_dtype=compensation_buffer_dtype,
)
super().__init__(params, defaults)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
if closure is not None:
with torch.enable_grad():
# to fix linter, we do not keep the returned loss for use atm.
closure()
for group in self.param_groups:
beta1, beta2 = group["betas"]
lr = group["lr"]
weight_decay = group["weight_decay"]
eps = group["eps"]
use_kahan_summation = group["use_kahan_summation"]
momentum_dtype = group["momentum_dtype"]
variance_dtype = group["variance_dtype"]
compensation_buffer_dtype = group["compensation_buffer_dtype"]
for p in group["params"]:
if p.grad is None:
continue
if p.grad.is_sparse:
raise RuntimeError(
"AnyPrecisionAdamW does not support sparse gradients"
)
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = torch.tensor(0.0)
# momentum - EMA of gradient values
state["exp_avg"] = torch.zeros_like(
p,
dtype=momentum_dtype,
)
# variance uncentered - EMA of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(
p,
dtype=variance_dtype,
)
# optional Kahan summation - accumulated error tracker
if use_kahan_summation:
state["compensation"] = torch.zeros_like(
p,
dtype=compensation_buffer_dtype,
)
# main processing -------------------------
# update the steps for each param group update
state["step"] += 1
step = state["step"]
exp_avg = state["exp_avg"]
exp_avg_sq = state["exp_avg_sq"]
grad = p.grad
# weight decay, AdamW style
if weight_decay:
p.data.mul_(1 - lr * weight_decay)
# update momentum
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
# update uncentered variance
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
# adjust using bias1
bias_correction1 = 1 - beta1**step
step_size = lr / bias_correction1
# adjust using bias2
denom_correction = (1 - beta2**step) ** 0.5 # avoids math import
centered_variance = (exp_avg_sq.sqrt() / denom_correction).add_(
eps, alpha=1
)
# lr update to compensation
if use_kahan_summation:
compensation = state["compensation"]
compensation.addcdiv_(exp_avg, centered_variance, value=-step_size)
# update weights with compensation (Kahan summation)
# save error back to compensation for next iteration
temp_buffer = p.detach().clone()
p.data.add_(compensation)
compensation.add_(temp_buffer.sub_(p.data))
else:
# usual AdamW updates
p.data.addcdiv_(exp_avg, centered_variance, value=-step_size) |