Distributed Optimizers
Author: Wenxuan Tan, Junwen Duan, Renjie Mao
Related Paper
- Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
- CAME: Confidence-guided Adaptive Memory Efficient Optimization
- GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
- Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
Introduction
Apart from the widely adopted Adam and SGD, many modern optimizers require layer-wise statistics to update parameters, and thus aren't directly applicable to settings where model layers are sharded across multiple devices. We provide optimized distributed implementations with minimal extra communications, and seamless integrations with Tensor Parallel, DDP and ZeRO plugins, which automatically uses distributed optimizers with 0 code change.
Optimizers
Adafactor is a first-order Adam variant using Non-negative Matrix Factorization(NMF) to reduce memory footprint. CAME improves by introducting a confidence matrix to correct NMF. GaLore further reduces memory by projecting gradients into a low-rank space and 8-bit block-wise quantization. Lamb allows huge batch sizes without lossing accuracy via layer-wise adaptive update bounded by the inverse of its Lipschiz constant.
Hands-On Practice
We now demonstrate how to use Distributed Adafactor with booster API combining Tensor Parallel and ZeRO 2 with 4 GPUs. Note that even if you're not aware of distributed optimizers, the plugins automatically casts yours to the distributed version for convenience.
step 1. Import libraries
from transformers import LlamaModel, LlamaConfig
from colossalai.nn.optimizer.distributed_adafactor import DistributedAdaFactor
from colossalai.booster import Booster
from colossalai.booster.plugin import HybridParallelPlugin
import colossalai
import torch
step 2. Initialize Distributed Environment and Parallism Group
We need to initialize distributed environment. For demo purpose, we use colossal run --nproc_per_node 4
. You can refer to Launch Colossal-AI
colossalai.launch_from_torch()
step 3. Initialize Module and Optimizer
Build our model. We created an MLP using two Linear Layer.
# Init Llama from huggingface
configuration = LlamaConfig()
model = LlamaModel(configuration).cuda()
criterion = lambda x: x.mean()
dist_optim = DistributedAdaFactor(model.parameters())
step 4.Init Booster
plugin = HybridParallelPlugin(tp_size=2, zero_stage=2, pp_size=1, enable_all_optimization=True)
booster = Booster(plugin=plugin)
# You should also pass in your own dataset.
model, dist_optim, criterion, dataloader, _ = booster.boost(model, dist_optim, criterion)
step 5.Train Your Model
steps = 10
for step in range(steps):
input_ids = torch.ones(1, 100, device="cuda", dtype=torch.int)
attention_mask = input_ids.clone()
outputs = model(input_ids.cuda(), attention_mask.cuda())
loss = criterion(outputs.last_hidden_state)
booster.backward(loss, dist_optim)
dist_optim.step()
dist_optim.zero_grad()
GaLore special handling
For GaLore, we need to specify projection rank for each parameter group and quantization & paged optimizer params. Please refer to bitandbytes for quantization details. Support for ZeRO is underway.
from colossalai.nn.optimizer.galore import get_galore_param_groups
from colossalai.nn.optimizer import DistGaloreAwamW
optim = DistGaloreAwamW(
get_galore_param_groups(model, decay=1e-2, rank=8),
lr=lr,
betas=(beta1, beta2),
eps=eps,
nbits=8,
percentile_clipping=100,
block_wise=True,
min_8bit_size=4096,
)
Plugin compatibility
Model/Feature | Lamb | GaLore | Adafactor | CAME | ||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Hybrid Parallel Plugin | ✔️ | ✔️ | ✔️ | ✔️ | ||||||||||||||||||||||||||||||||||
Low Level Zero Plugin | ✔️ | ❌ | ✔️ | ✔️ | ||||||||||||||||||||||||||||||||||
Torch DDP Plugin | ✔️ | ✔️ | ✔️ | ✔️ | ||||||||||||||||||||||||||||||||||
Gemini Plugin | ❌ | ❌ | ❌ | ❌ | ||||||||||||||||||||||||||||||||||
Moe Hybrid Plugin | ❌ | ❌ | ❌ | ❌ | ||||||||||||||||||||||||||||||||||
API Reference
class
colossalai.nn.DistributedAdaFactor
function
setup_distributed
tp_group -- The devices group for tensor parallel; dp_group -- The devices group for data parallel;
- shard_to_working_param (Dict) -- ZeRO 2 feeds the optimizer a sharded param view as grads are sharded. This maps from id(view) to working params used in forward & backward. padding_map -- An empty interface placeholder; use_zero -- Whether or not to use zero;
function
step
- closure (callable, optional) -- A closure that reevaluates the model and returns the loss.
Performs a single optimization steps
class
colossalai.nn.DistributedLamb
- 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 (L2 penalty) (default: 0)
.. _Large Batch Optimization for Deep Learning: Training BERT in 76 minutes: https://arxiv.org/abs/1904.00962
function
setup_distributed
- tp_group (dist.ProcessGroup) -- Tensor Parallel process group
- dp_group (dist.ProcessGroup) -- ZeRO 2 process group
- shard_to_working_param (Dict) -- ZeRO 2 feeds the optimizer a sharded param view as grads are sharded. This maps from id(view) to working params used in forward & backward. padding_map -- An empty interface placeholder
- is_zero (bool) -- Whether to use ZeRO 2.
function
step
- closure (callable, optional) -- A closure that reevaluates the model and returns the loss.
class
colossalai.nn.DistGaloreAwamW
- 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 norm. (default: (0.9, 0.999))
- eps (float, optional) -- term added to the denominator to improve numerical stability. (default: 1e-6)
- weight_decay (float, optional) -- weight decay (L2 penalty) (default: 0.01) nbits -- Number of bits for quantization optim states. Only 32 and 8 are supported.
- min_8bit_size (
int
, defaults to 4096) -- The minimum number of elements of the parameter tensors for 8-bit optimization. - percentile_clipping (
int
, defaults to 100) -- Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability. - block_wise (
bool
, defaults toTrue
) -- Whether to independently quantize each block of tensors to reduce outlier effects and improve stability. - is_paged (
bool
, defaults toFalse
) -- Whether the optimizer is a paged optimizer (handle memory spike via CPU-GPU transfer) or not. - args (dict, optional) -- quantization-related arguments. If passed, will override all quantization args above.
function
setup_distributed
- tp_group (dist.ProcessGroup) -- Tensor Parallel process group
- dp_group (dist.ProcessGroup) -- ZeRO 2 process group
- shard_to_working_param (Dict) -- ZeRO 2 feeds the optimizer a sharded param view as grads are sharded. This maps from id(view) to working params used in forward & backward.
- padding_map (Dict) -- Padding size of each param from ZeRO's param store. Required if ZeRO is used.
- is_zero (bool) -- Whether to use ZeRO 2.
function
step
- closure (callable, optional) -- A closure that reevaluates the model and returns the loss.
function
to_master_shape
class
colossalai.nn.DistributedCAME
- params (iterable) -- iterable of parameters to optimize or dicts defining parameter groups
- lr (float, optional) -- external learning rate (default: None)
- eps (tuple[float, float]) -- regularization constants for square gradient and instability respectively (default: (1e-30, 1e-16))
- clip_threshold (float) -- threshold of root-mean-square of final gradient update (default: 1.0)
- betas (tuple[float, float, float]) -- coefficient used for computing running averages of
- update, square gradient and instability (default -- (0.9, 0.999, 0.9999)))
- weight_decay (float, optional) -- weight decay (L2 penalty) (default: 0)
function
setup_distributed
tp_group -- The devices group for tensor parallel; dp_group -- The devices group for data parallel;
- shard_to_working_param (Dict) -- ZeRO 2 feeds the optimizer a sharded param view as grads are sharded. This maps from id(view) to working params used in forward & backward. padding_map -- Interface placeholder use_zero -- Whether or not to use zero;
Inject features to the Optimizer
function
step
- closure (callable, optional) -- A closure that reevaluates the model and returns the loss.