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Fix LR schedule handling for low-bit optimizers #736

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merged 7 commits into from
Aug 23, 2024
Merged

Fix LR schedule handling for low-bit optimizers #736

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af8de67
update benchmark script
gau-nernst Aug 23, 2024
aecc1c4
try CPU Tensor lr
gau-nernst Aug 23, 2024
55077cf
update
gau-nernst Aug 23, 2024
487ede0
Merge branch 'pytorch:main' into optim_lr
gau-nernst Aug 23, 2024
dc0e2f9
consolidate adam
gau-nernst Aug 23, 2024
87d3be7
remove unnecessary requires_grad in subclass
gau-nernst Aug 23, 2024
cdf283c
update README
gau-nernst Aug 23, 2024
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51 changes: 25 additions & 26 deletions benchmarks/benchmark_low_bit_adam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,13 +23,13 @@
import math
from contextlib import nullcontext
from functools import partial
from pathlib import Path

import bitsandbytes as bnb
import datasets
import timm
import torch
import torch.nn.functional as F
import wandb
from torch.utils.data import DataLoader
from torchvision.transforms import v2
from tqdm import tqdm
Expand Down Expand Up @@ -72,22 +72,6 @@ def get_lr(self, step: int) -> float:
return self.final_lr


class WandbLogger:
def __init__(self, args):
if args.project is not None and not args.profile:
import wandb

Path("wandb_logs").mkdir(exist_ok=True)
self.run = wandb.init(project=args.project, name=args.run_name, config=args, dir="wandb_logs")

else:
self.run = None

def log(self, *args, **kwargs):
if self.run is not None:
self.run.log(*args, **kwargs)


def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--model", required=True)
Expand Down Expand Up @@ -190,7 +174,13 @@ def evaluate_model(model, args):
print(f"{k}: {v}")

# wandb is only enabled when args.project is set and args.profile is False
logger = WandbLogger(args)
logger = wandb.init(
project=args.project,
name=args.run_name,
config=args,
dir="/tmp",
mode="disabled" if args.project is None else None,
)
dloader = get_dloader(args, True)
print(f"Train dataset: {len(dloader.dataset):,} images")

Expand Down Expand Up @@ -239,13 +229,15 @@ def evaluate_model(model, args):

lr_schedule = CosineSchedule(args.lr, len(dloader) * args.n_epochs)
grad_scaler = torch.amp.GradScaler("cuda", enabled=args.amp == "fp16")
log_interval = 10

step = 0
for epoch_idx in range(args.n_epochs):
model.train()
pbar = tqdm(dloader, dynamic_ncols=True, desc=f"Epoch {epoch_idx + 1}/{args.n_epochs}")

start_time = datetime.datetime.now()
t0 = start_time

with torch.profiler.profile() if args.profile else nullcontext() as prof:
for batch in pbar:
Expand All @@ -265,13 +257,18 @@ def evaluate_model(model, args):
if args.cosine_lr_scheduler:
lr = lr_schedule.get_lr(step)
for param_group in optim.param_groups:
param_group["lr"] = lr

if step % 100 == 0:
logger.log(
dict(loss=loss.item(), lr=optim.param_groups[0]["lr"]),
step=step,
)
if isinstance(param_group["lr"], torch.Tensor):
param_group["lr"].fill_(lr)
else:
param_group["lr"] = lr

if step % log_interval == 0:
log_dict = dict(loss=loss.item(), lr=optim.param_groups[0]["lr"])
if step > 0:
t1 = datetime.datetime.now()
log_dict["imgs_per_second"] = args.batch_size * log_interval / (t1 - t0).total_seconds()
t0 = t1
logger.log(log_dict, step=step)

if args.optim_cpu_offload == "deepspeed":
model.step()
Expand All @@ -295,4 +292,6 @@ def evaluate_model(model, args):
print(f"Epoch {epoch_idx + 1}/{args.n_epochs}: val_acc={val_acc.item() * 100:.2f}")
logger.log(dict(val_acc=val_acc), step=step)

print(f"Max memory used: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB")
peak_mem = torch.cuda.max_memory_allocated() / 1e9
print(f"Max memory used: {peak_mem:.02f} GB")
logger.log(dict(max_memory_allocated=peak_mem))
7 changes: 5 additions & 2 deletions torchao/prototype/low_bit_optim/adam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ def __init__(self, params, lr, betas, eps, weight_decay, amsgrad, *, block_size)
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, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)
defaults = dict(lr=torch.tensor(lr), betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)
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is this the right place to set it? i would advocate for making this very obvious with users what is going on, and i would modify the AdamW constructor below.

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unless you’re adamant that this optimizer will only support Tensor lrs. In pytorch/pytorch, we support python float lrs in eager because it is faster to compute python math than launch kernels, though that may be less relevant here.

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The alternative I’d suggest for max visibility from user is changing line 165 below to be torch.tensor(1e-3)

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I think to keep it simple, we just enforce lr to be a scalar tensor here. If lr is not changed during training, whether it is a tensor or not would not matter. But if it is changed during training, we need lr to be a tensor anyway since torch.compile will recompile when python float lr changes value.

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Yea, but I still think it’d be most visible/clear to users if the constructors of Adam and AdamW clearly set the default as Tensors and if this base would just error if lr was not a Tensor.

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@gau-nernst gau-nernst Aug 23, 2024 •
edited
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Just to clarify, do you mean that forcing users to pass in LR as Tensor instead of Python float? i.e. error is raised if LR is not a Tensor.
And is it your view that converting Python float to Tensor in the constructor might seem unexpected to the users?

Personally when using optimizers, I never pass in Tensor LR before, so it feels strange to me 😅 (doesn't mean I'm correct, just a feeling from my limited experience). I think that converting LR from float to Tensor inside the constructor is an implementation detail that the users shouldn't need to care about.
Also, most (if not all?) other optimizers will work if I pass in a Python float LR? So feel kinda strange (again 🤣) if users are forced to pass in Tensor LR to this particular optimizer.

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Yes, I think it'd be good to have users pass in Tensor lrs, so they're most aware of what is going on. I think it is not great to switch it up under the hood and have the user be confused if there's ever an error regarding the Tensorness of the lr.

super().__init__(params, defaults)
self.block_size = block_size

Expand Down Expand Up @@ -81,7 +81,10 @@ def _prepare_param_groups(self):
# practically, only lr is changed during training.
# NOTE: if lr is changed at every step, moving lr to CUDA can slow down training 3-4%.
if not isinstance(group["lr"], Tensor):
group["lr"] = torch.tensor(group["lr"], device=p.device)
raise ValueError(
"lr was changed to a non-Tensor object. If you want to update lr, please use "
"optim.param_groups[0]['lr'].fill_(new_lr)"
)

p_grad_state = (
p,
Expand Down
7 changes: 5 additions & 2 deletions torchao/prototype/low_bit_optim/adamw.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ def __init__(self, params, lr, betas, eps, weight_decay, amsgrad, *, block_size)
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, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)
defaults = dict(lr=torch.tensor(lr), betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)
super().__init__(params, defaults)
self.block_size = block_size

Expand Down Expand Up @@ -81,7 +81,10 @@ def _prepare_param_groups(self):
# practically, only lr is changed during training.
# NOTE: if lr is changed at every step, moving lr to CUDA can slow down training 3-4%.
if not isinstance(group["lr"], Tensor):
group["lr"] = torch.tensor(group["lr"], device=p.device)
raise ValueError(
"lr was changed to a non-Tensor object. If you want to update lr, please use "
"optim.param_groups[0]['lr'].fill_(new_lr)"
)

p_grad_state = (
p,
Expand Down
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