File size: 6,937 Bytes
fb11af9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | # 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.
# Copyright 2025 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Optional, Sequence, Tuple
import torch
import torch.nn as nn
from torch.optim import AdamW
from torch.optim.optimizer import Optimizer
from ..utils.import_utils import is_torch_npu_available
# https://github.com/meta-llama/llama-recipes/blob/v0.0.4/src/llama_recipes/policies/anyprecision_optimizer.py
class AnyPrecisionAdamW(Optimizer):
def __init__(
self,
params,
lr=1e-3,
betas=(0.9, 0.95),
eps=1e-8,
weight_decay=0.0,
use_kahan_summation=True,
momentum_dtype=torch.bfloat16,
variance_dtype=torch.bfloat16,
compensation_buffer_dtype=torch.bfloat16,
):
defaults = {
"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():
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
if weight_decay: # weight decay, AdamW style
p.data.mul_(1 - lr * weight_decay)
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # update momentum
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) # update uncentered variance
bias_correction1 = 1 - beta1**step # adjust using bias1
step_size = lr / bias_correction1
denom_correction = (1 - beta2**step) ** 0.5 # adjust using bias2 and avoids math import
centered_variance = (exp_avg_sq.sqrt() / denom_correction).add_(eps, alpha=1)
if use_kahan_summation: # lr update to compensation
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)
def build_optimizer(
model: "nn.Module",
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.95),
eps: float = 1e-8,
weight_decay: float = 1e-2,
fused: bool = False,
optimizer_type: str = "adamw",
param_groups: Optional[Sequence[Dict[str, Any]]] = None,
post_training=False,
) -> "torch.optim.Optimizer":
if param_groups is None:
align_parameters = [
name for name, _ in model.named_parameters() if "depth" in name
]
if len(align_parameters) > 0:
lr_gain = 10.0 if not post_training else 1.0
param_groups = [
{
"params": [
p
for n, p in model.named_parameters()
if (p.requires_grad and n not in align_parameters)
],
"lr": lr,
},
{
"params": [
p
for n, p in model.named_parameters()
if (p.requires_grad and n in align_parameters)
],
"lr": lr * lr_gain,
}
]
else:
param_groups = filter(lambda p: p.requires_grad, model.parameters())
if optimizer_type == "adamw":
foreach = False if is_torch_npu_available() else (not fused)
fused = False if is_torch_npu_available() else fused
optim = AdamW(param_groups, lr, betas, eps, weight_decay, fused=fused, foreach=foreach)
elif optimizer_type == "anyprecision_adamw":
optim = AnyPrecisionAdamW(param_groups, lr, betas, eps, weight_decay)
else:
raise ValueError("Only adamw and anyprecision_adamw are supported as optimizers.")
return optim
|