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Optimizer benchmark on a ~500M parameter fp32 transformer.
Compares speed (ms/step) and peak VRAM across:
- AdamW (torch, unfused — traditional Python loop)
- AdamW8bit (bitsandbytes)
- Adafactor
- Automagic v1
- Automagic v2 (only optimizer using fused-backward)
- Prodigy
"""
import contextlib
import gc
import io
import os
import sys
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
# Allow running this file directly: `python test_optimizers.py` without setting PYTHONPATH.
# Toolkit imports happen inside main() so they pick this up.
_REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
if _REPO_ROOT not in sys.path:
sys.path.insert(0, _REPO_ROOT)
# ---- model ---------------------------------------------------------------
class TransformerBlock(nn.Module):
def __init__(self, d_model: int, n_heads: int, d_ff: int):
super().__init__()
self.n_heads = n_heads
self.d_head = d_model // n_heads
self.ln1 = nn.LayerNorm(d_model)
self.q = nn.Linear(d_model, d_model, bias=False)
self.k = nn.Linear(d_model, d_model, bias=False)
self.v = nn.Linear(d_model, d_model, bias=False)
self.o = nn.Linear(d_model, d_model, bias=False)
self.ln2 = nn.LayerNorm(d_model)
self.ffn_up = nn.Linear(d_model, d_ff, bias=False)
self.ffn_down = nn.Linear(d_ff, d_model, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, S, D = x.shape
h = self.ln1(x)
q = self.q(h).view(B, S, self.n_heads, self.d_head).transpose(1, 2)
k = self.k(h).view(B, S, self.n_heads, self.d_head).transpose(1, 2)
v = self.v(h).view(B, S, self.n_heads, self.d_head).transpose(1, 2)
a = F.scaled_dot_product_attention(q, k, v, is_causal=True)
a = a.transpose(1, 2).contiguous().view(B, S, D)
x = x + self.o(a)
h = self.ln2(x)
x = x + self.ffn_down(F.gelu(self.ffn_up(h)))
return x
class Transformer(nn.Module):
def __init__(self, d_model=1024, n_heads=16, n_layers=40, d_ff=4096):
super().__init__()
self.blocks = nn.ModuleList([
TransformerBlock(d_model, n_heads, d_ff) for _ in range(n_layers)
])
self.norm = nn.LayerNorm(d_model)
def forward(self, x: torch.Tensor) -> torch.Tensor:
for b in self.blocks:
x = b(x)
return self.norm(x)
# ---- benchmark -----------------------------------------------------------
DEVICE = "cuda"
DTYPE = torch.float32
D_MODEL = 1024
N_HEADS = 16
N_LAYERS = 40
D_FF = 4096
BATCH = 1
SEQ = 128
WARMUP = 3
ITERS = 10
def build_model():
torch.manual_seed(0)
return Transformer(D_MODEL, N_HEADS, N_LAYERS, D_FF).to(DEVICE, dtype=DTYPE)
def benchmark(results: list, label: str, opt_factory):
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
model = build_model()
# Some optimizers print on construction; mute that so the final table is clean.
with contextlib.redirect_stdout(io.StringIO()):
opt = opt_factory(model.parameters())
x = torch.randn(BATCH, SEQ, D_MODEL, device=DEVICE, dtype=DTYPE)
print(f" running {label}...", flush=True)
try:
for _ in range(WARMUP):
opt.zero_grad(set_to_none=True)
model(x).sum().backward()
opt.step()
torch.cuda.synchronize()
t0 = time.perf_counter()
for _ in range(ITERS):
opt.zero_grad(set_to_none=True)
model(x).sum().backward()
opt.step()
torch.cuda.synchronize()
dt = (time.perf_counter() - t0) / ITERS * 1000
peak = torch.cuda.max_memory_allocated() / 1024**3
results.append({"label": label, "ms": dt, "peak": peak, "ok": True})
except torch.cuda.OutOfMemoryError:
results.append({"label": label, "ms": float("inf"), "peak": float("inf"), "ok": False})
finally:
del opt, model
gc.collect()
torch.cuda.empty_cache()
def print_table(results: list):
results = sorted(results, key=lambda r: r["peak"])
headers = ["#", "Optimizer", "Peak VRAM", "Time/step"]
rows = []
for i, r in enumerate(results, 1):
if not r["ok"]:
rows.append([str(i), r["label"], "OOM", "-"])
continue
rows.append([str(i), r["label"], f"{r['peak']:.2f} GB", f"{r['ms']:.1f} ms"])
widths = [max(len(str(row[c])) for row in [headers] + rows) for c in range(len(headers))]
def fmt(row, sep=" │ "):
return sep.join(s.ljust(widths[c]) if c == 1 else s.rjust(widths[c]) for c, s in enumerate(row))
line_top = "─" * (sum(widths) + 3 * (len(widths) - 1))
print()
print(line_top)
print(fmt(headers))
print(line_top)
for row in rows:
print(fmt(row))
print(line_top)
def main():
n_params = sum(p.numel() for p in build_model().parameters())
dtype_name = str(DTYPE).replace("torch.", "")
print(f"Model: {N_LAYERS} blocks × d_model={D_MODEL} × d_ff={D_FF}")
print(f" {n_params/1e6:.1f}M params ({dtype_name})")
print(f"Step: batch={BATCH}, seq={SEQ}")
print(f"Timing: {WARMUP} warmup + {ITERS} timed iters")
print()
from toolkit.optimizers.automagic import Automagic
from toolkit.optimizers.automagic2 import Automagic2
from toolkit.optimizers.adafactor import Adafactor
from prodigyopt import Prodigy
import bitsandbytes as bnb
results: list = []
benchmark(results, "AdamW",
lambda p: torch.optim.AdamW(p, lr=1e-4, eps=1e-6, foreach=False, fused=False))
benchmark(results, "AdamW8bit",
lambda p: bnb.optim.AdamW8bit(p, lr=1e-4, eps=1e-6))
benchmark(results, "Adafactor",
lambda p: Adafactor(p, lr=1e-4, scale_parameter=False, relative_step=False, warmup_init=False))
benchmark(results, "Automagic v1",
lambda p: Automagic(p, lr=1e-4))
benchmark(results, "Automagic v2",
lambda p: Automagic2(p, lr=1e-4))
benchmark(results, "Prodigy",
lambda p: Prodigy(p, lr=1.0, eps=1e-6))
print_table(results)
if __name__ == "__main__":
main()
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