Fix LR schedule handling for low-bit optimizers

benchmarks/benchmark_low_bit_adam.py

@@ -18,18 +18,18 @@
 # To enable cosine learning rate scheduler, set --cosine_lr_scheduler
 
 import argparse
-import datetime
 import json
 import math
+import time
 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
@@ -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)
@@ -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")
 
@@ -239,14 +229,14 @@ 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
+ t0 = time.perf_counter()
 
  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()
-
  with torch.profiler.profile() if args.profile else nullcontext() as prof:
  for batch in pbar:
  if args.full_bf16:
@@ -265,13 +255,18 @@ 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 = time.perf_counter()
+ log_dict["imgs_per_second"] = args.batch_size * log_interval / (t1 - t0)
+ t0 = t1
+ logger.log(log_dict, step=step)
 
  if args.optim_cpu_offload == "deepspeed":
  model.step()
@@ -289,10 +284,10 @@ def evaluate_model(model, args):
  prof.export_chrome_trace("trace.json")
 
  else:
- print(f"Time taken for epoch {epoch_idx + 1}: {(datetime.datetime.now() - start_time)}")
-
  val_acc = 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))

torchao/prototype/low_bit_optim/README.md

@@ -26,23 +26,23 @@ To use 4-bit Adam, replace the above with `Adam4bit`. Similarly for `AdamFp8`. Y
 NOTE:
 - The low-bit optimizers require PyTorch >= 2.3. FP8 optimizers require CUDA compute capability >= 8.9.
 - For 4-bit optimizers, we don't implement rank-1 normalization for quantizing 2nd moment as originally done in the paper.
-- **Known issue**: When learning rate is updated every step (e.g. using cosine learning rate scheduler), training speed is slower. This is because we have to convert learning rate to a CUDA tensor (which incurs expensive memory transfer cost), since torch.compile() will treat a Python float as a constant and trigger recompile whenever the value is changed.
+- The first training step is expected to be slow since the optimizer needs to be compiled.
 
 ## Benchmarks
 
 Benchmark script for fine-tuning a [timm](https://github.com/huggingface/pytorch-image-models) model on [resisc45](https://huggingface.co/datasets/timm/resisc45) dataset is available at [benchmarks/benchmark_low_bit_adam.py](../../../benchmarks/benchmark_low_bit_adam.py).
 
-Results for fine-tuning ViT-H (630M params) with BF16 AMP for 2 epochs, batch size 8, on 4070Ti SUPER, with fixed random seed:
-
-Adam impl | max memory (GB) | time taken for 2nd epoch | accuracy
----------------|-----------------|--------------------------|----------
-PyTorch  | 12.94 |  8m 18s | 91.14
-bnb 8-bit | 8.31 |  6m 50s | 90.67
-ao 8-bit | 8.31 |  6m 44s | 90.63
-ao FP8 E4M3 | 8.32 |  6m 35s | 90.98
-lpmm 4-bit | 7.72 |  5m 59s | 89.97
-ao 4-bit | 7.72 |  7m 13s | 90.05
-lpmm 4-bit (*) | 7.73 | 11m 10s | 89.71
+Results for fine-tuning ViT-H (630M params) with BF16 AMP for 1 epoch, batch size 8, cosine LR scheduler, 4070Ti SUPER, fixed random seed:
+
+Adam impl  | max memory (GB) | imgs/s | accuracy
+----------------|-----------------|--------|----------
+PyTorch (fused) | 12.23 | 41.8 | 94.38
+bnb 8-bit  | 8.32 | 43.6 | 94.18
+ao 8-bit  | 8.33 | 42.6 | 94.25
+ao FP8 E4M3  | 9.27 | 44.1 | 94.40
+lpmm 4-bit  | 7.72 | 46.0 | 94.29
+ao 4-bit  | 7.72 | 40.0 | 94.03
+lpmm 4-bit (*)  | 7.74 | 26.6 | 94.25
 
 (*) means rank-1 normalization is used for 2nd optimizer state. Refer to [paper](https://arxiv.org/abs/2309.01507) for more details.
 

torchao/prototype/low_bit_optim/__init__.py

@@ -1,3 +1,2 @@
-from .adam import Adam8bit, Adam4bit, AdamFp8
-from .adamw import _AdamW, AdamW8bit, AdamW4bit, AdamWFp8
+from .adam import Adam4bit, Adam8bit, AdamFp8, AdamW4bit, AdamW8bit, AdamWFp8, _AdamW
 from .cpu_offload import CPUOffloadOptimizer